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IEEE Internet of Things Journal

Call for Papers

Please prepare your manuscript according to the Guidelines for Authors.

Current and past issues are accessible in IEEE Xplore.

Special Issues

Tiny Machine Learning in Internet of Unmanned Aerial Vehicles Prognostics and Health Management using the Internet of Things Energy Internet: A Cyber-Physical-Social Perspective Data Management and Security in Resource-constrained Intelligent IoT Systems Current Research Trend and Open Challenge for Industrial Internet-of-Things Next Generation Multiple Access for Internet-of-Things Integrated Sensing and Communications (ISAC) for 6G IoE Edge Learning in B5G IoT Systems Integrated Sensing, Computing and Communication for Internet of Robotic Things Efficient, Effective, and Explicable Artificial Intelligence Inspired IoT over Non-Terrestrial Networks Augmented Intelligence of Things for Vehicle Road Cooperation Systems Low-Carbon Sustainable Computing Enabled Artificial Intelligence of Things

Review & Tutorial Papers

Purpose and scope.

The IEEE IoT Journal (IoT-J) , launched in 2014 (“ Genesis of the IoT-J “), publishes papers on the latest advances, as well as review articles, on the various aspects of IoT. Topics include IoT system architecture, IoT enabling technologies, IoT communication and networking protocols, IoT services and applications, and the social implications of IoT. Examples are IoT demands, impacts, and implications on sensors technologies, big data management, and future internet design for various IoT use cases, such as smart cities, smart environments, smart homes, etc. The fields of interest include:

IoT architectures such as things-centric, data-centric, service-centric architecture, CPS and SCADA platforms, future Internet design for IoT, cloud-based IoT, and system security and manageability.
IoT enabling technologies such as sensors, radio frequency identification, low power and energy harvesting, sensor networks, machine-type communication, resource-constrained networks, real-time systems, IoT data analytics, in situ processing, and embedded software.
IoT services, applications, standards, and test-beds such as streaming data management and mining platforms, service middleware, open service platform, semantic service management, security and privacy-preserving protocols, design examples of smart services and applications, and IoT application support.

Editor-in-Chief

Nei Kato, Tohoku University, Japan (Email: [email protected] )

Internet of Things - Open Access Research

Articles, call for papers, journals and more on iot.

Take a look at our open access journals covering the Internet of Things, browse selected freely available research and submit your IoT manuscript to our SpringerOpen journals.

Selected IoT Article Collections

Research and Challenges of Wireless Networks in Internet of Things

Published in EURASIP Journal on Wireless Communications and Networking

Recent Advances in Internet of Things Security and Privacy

Published in EURASIP Journal on Wireless Communications and Networking.

Internet of Things Article Highlights

Featured Open Access Journals covering IoT

EURASIP Journal on Advances in Signal Processing - SpringerOpen

Human-centric Computing and Information Sciences - SpringerOpen

EURASIP Journal on Wireless Communications and Networking - SpringerOpen

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IEEE Xplore Digital Library
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Publications

IEEE publications on IoT include:

IEEE Internet of Things Journal (IoT-J)

Launched in 2014, the IEEE IoT-J publishes papers on the latest advances, as well as review articles, on the various aspects of IoT from open call and special issues. Topics include IoT system architecture, IoT enabling technologies, IoT communication and networking protocols, IoT services and applications, and the social implications of IoT. The current issue is available in IEEE Xplore .

The IEEE IoT-J solicits original work that must not be currently under consideration for publication in other venues. For more information, view the Call for Papers .

IEEE Communications Magazine - This award-winning magazine brings you the latest international coverage of current issues and advances in key areas of wireless, optical and wired communications. Written in tutorial applications-driven style by the industry's leading experts, IEEE Communications Magazine delivers practical, current information on hot topics, implementations, and best industry practices.

IEEE Transactions on Communications - The IEEE Transactions on Communications (TCOM) publishes high-quality papers reporting theoretical and experimental advances in the general area of communications. TCOM has a broad scope spanning several areas such as wireless communications, wired communications, and optical communications.

IEEE Transactions on Wireless Communications - The IEEE Transactions on Wireless Communications is a major archival journal that is committed to the timely publication of very high-quality, peer-reviewed, original papers that advance the theory and applications of wireless communication systems and networks.

IEEE Communications Letters - IEEE Communications Letters provides researchers with an ideal venue for sharing their newest results in a timely manner. Every month this journal publishes 20-25 short (up to 4 pages) high-quality contributions on the theory and practice of communications.

IEEE Wireless Communications Letters - Publishes timely, novel and high-quality recent results on Wireless Communications in letter format. IEEE Wireless Communications Letters have a 4-page limit. The journal's goal is rapid dissemination of original, cutting-edge ideas and timely, significant contributions in the theory and applications of wireless communications.

IEEE/ACM Transactions on Networking - The IEEE/ACM Transactions on Networking 's high-level objective is to publish high-quality, original research results derived from theoretical or experimental exploration of the area of communication/computer networking, covering all sorts of information transport networks over all sorts of physical layer technologies, both wireline (all kinds of guided media: e.g., copper, optical) and wireless (e.g., radio-frequency, acoustic (e.g., underwater), infra-red), or hybrids of these.

IEEE Transactions on Network and Service Management - IEEE Transactions on Network and Service Management (IEEE TNSM) is a journal for timely publication of archival research on the management of networks, systems, services and applications, as well as on issues in communications software, service engineering, policies and business processes for network and service management.

IEEE Pervasive Computing - IEEE Pervasive Computing explores the many facets of pervasive and ubiquitous computing with research articles, case studies, product reviews, conference reports, departments covering wearable and mobile technologies, and more.

IEEE Sensors Journal - The IEEE Sensors Journal is a peer-reviewed, semi-monthly online journal devoted to sensors and sensing phenomena.

IEEE Calls for Papers

IEEE Internet of Things Journal IEEE Communications Magazine IEEE Communications Standard Magazine IEEE Internet of Things Magazine IEEE Network IEEE Wireless Communications

IEEE Talks IoT

Check out our ongoing series of Q&A articles with the IEEE experts! Read more

Selected Articles from IEEE Xplore

The IEEE Xplore digital library is a powerful resource for scientific and technical content on a vast breadth of topics including the Internet of Things (IoT). Each month IEEE IoT will select articles from this influential repository of information published by the IEEE and its publishing partners, and make them available to the IEEE IoT Technical Community members on a complimentary basis. Read more

Search IEEE Xplore for more articles on IoT

IEEE World Forum on Internet of Things (WF-IoT) Conference Proceedings

2018 IEEE WF-IoT, 5-8 February 2018, Singapore

2016 IEEE WF-IoT, 12-14 December 2016, Virginia, USA

2015 IEEE WF-IoT, 14-16 December 2015, Milan, Italy

2014 IEEE WF-IoT, 6-8 March 2014, Seoul, Korea

IEEE IoT Brain Trust Blog (ECN Magzine)

The IEEE IoT Brain Trust series is a collection of blogs exploring IoT in the industry.

Meeting Cloud Challenges May Pave Way for IoT - 28 July 2016

The Increasingly Concerning Carbon Footprint of Information and Telecommunication Technologies - 29 April 2016

Standardizing 3D Body Processing Technology - 8 March 2016

IoT’s Special Gift to Big Data - 22 January 2016

IoT and the Cloud - 22 December 2015

IEEE 802.11’s Role in Enabling the Internet of Things - 1 December 2015

Defining the Internet of Things: A Work in Progress - 3 November 2015

How the Smart Grid Will Impact IoT - 19 June 2015

What Does IoT + Big Data Mean to You? - 27 April 2015

How IoT Will Affect Telecom (Part II) - 31 March 2015

In June 2016, IEEE-USA, along with the IEEE Internet Initiative, had the opportunity to comment on the National Telecommunication and Information Administration's role in promoting and regulating the IoT.

News Articles

Connected Tech at CES IEEE Transmitter - December 2016

With smart home devices now owned by 15% of households, IoT products for the home are catching on. At the Consumer Electronics Show (CES), you can expect to see the latest in IoT smart home gadgets, smart tech partnerships, and advancements in voice-activated technologies. Additional tech on display includes facial recognition, thermal imaging, and connected luggage.

iot applications Recently Published Documents

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Service Provisioning for Multi-source IoT Applications in Mobile Edge Computing

We are embracing an era of Internet of Things (IoT). The latency brought by unstable wireless networks caused by limited resources of IoT devices seriously impacts the quality of services of users, particularly the service delay they experienced. Mobile Edge Computing (MEC) technology provides promising solutions to delay-sensitive IoT applications, where cloudlets (edge servers) are co-located with wireless access points in the proximity of IoT devices. The service response latency for IoT applications can be significantly shortened due to that their data processing can be performed in a local MEC network. Meanwhile, most IoT applications usually impose Service Function Chain (SFC) enforcement on their data transmission, where each data packet from its source gateway of an IoT device to the destination (a cloudlet) of the IoT application must pass through each Virtual Network Function (VNF) in the SFC in an MEC network. However, little attention has been paid on such a service provisioning of multi-source IoT applications in an MEC network with SFC enforcement. In this article, we study service provisioning in an MEC network for multi-source IoT applications with SFC requirements and aiming at minimizing the cost of such service provisioning, where each IoT application has multiple data streams from different sources to be uploaded to a location (cloudlet) in the MEC network for aggregation, processing, and storage purposes. To this end, we first formulate two novel optimization problems: the cost minimization problem of service provisioning for a single multi-source IoT application, and the service provisioning problem for a set of multi-source IoT applications, respectively, and show that both problems are NP-hard. Second, we propose a service provisioning framework in the MEC network for multi-source IoT applications that consists of uploading stream data from multiple sources of the IoT application to the MEC network, data stream aggregation and routing through the VNF instance placement and sharing, and workload balancing among cloudlets. Third, we devise an efficient algorithm for the cost minimization problem built upon the proposed service provisioning framework, and further extend the solution for the service provisioning problem of a set of multi-source IoT applications. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.

Design and Analysis of a RFID Reader Microstrip Array antenna for IoT Applications in Smart Cities

This paper presents the design of 2*1 and 4*1 RFID reader microstrip array antenna at 2.4GHz for the Internet of things (IoT) networks which are Zigbee, Bluetooth and WIFI. The proposed antenna is composed of identical circular shapes radiating patches printed in FR4 substrate. The dielectric constant εr and substrate thickness h are 4.4 and 1.6mm, respectively. The 2*1 and 4*1 array antennas present a gain improvement of 27.3% and 61.9%, respectively. The single,2*1 and 4*1 array antennas were performed with CADFEKO.

A Survey on Privacy Preservation in Fog-Enabled Internet of Things

Despite the rapid growth and advancement in the Internet of Things (IoT ), there are critical challenges that need to be addressed before the full adoption of the IoT. Data privacy is one of the hurdles towards the adoption of IoT as there might be potential misuse of users’ data and their identity in IoT applications. Several researchers have proposed different approaches to reduce privacy risks. However, most of the existing solutions still suffer from various drawbacks, such as huge bandwidth utilization and network latency, heavyweight cryptosystems, and policies that are applied on sensor devices and in the cloud. To address these issues, fog computing has been introduced for IoT network edges providing low latency, computation, and storage services. In this survey, we comprehensively review and classify privacy requirements for an in-depth understanding of privacy implications in IoT applications. Based on the classification, we highlight ongoing research efforts and limitations of the existing privacy-preservation techniques and map the existing IoT schemes with Fog-enabled IoT schemes to elaborate on the benefits and improvements that Fog-enabled IoT can bring to preserve data privacy in IoT applications. Lastly, we enumerate key research challenges and point out future research directions.

Design and Deployment of Expressive and Correct Web of Things Applications

Consumer Internet of Things (IoT) applications are largely built through end-user programming in the form of event-action rules. Although end-user tools help simplify the building of IoT applications to a large extent, there are still challenges in developing expressive applications in a simple yet correct fashion. In this context, we propose a formal development framework based on the Web of Things specification. An application is defined using a composition language that allows users to compose the basic event-action rules to express complex scenarios. It is transformed into a formal specification that serves as the input for formal analysis, where the application is checked for functional and quantitative properties at design time using model checking techniques. Once the application is validated, it can be deployed and the rules are executed following the composition language semantics. We have implemented these proposals in a tool built on top of the Mozilla WebThings platform. The steps from design to deployment were validated on real-world applications.

Design and Analysis of a RFID Reader Microstrip Array Antenna for IoT Applications in Smart Cities

Blockchain technology - based solutions for iot security.

Blockchain innovation has picked up expanding consideration from investigating and industry over the later a long time. It permits actualizing in its environment the smart-contracts innovation which is utilized to robotize and execute deals between clients. Blockchain is proposed nowadays as the unused specialized foundation for a few sorts of IT applications. Blockchain would aid avoid the duplication of information because it right now does with Bitcoin and other cryptocurrencies. Since of the numerous hundreds of thousands of servers putting away the Bitcoin record, it’s impossible to assault and alter. An aggressor would need to change the record of 51 percent of all the servers, at the precise same time. The budgetary fetched of such an assault would distantly exceed the potential picks up. The same cannot be said for our private data that lives on single servers possessed by Google and Amazon. In this paper, we outline major Blockchain technology that based as solutions for IOT security. We survey and categorize prevalent security issues with respect to IoT data privacy, in expansion to conventions utilized for organizing, communication, and administration. We diagram security necessities for IoT together with the existing scenarios for using blockchain in IoT applications.

Energy-Aware Security Adaptation for Low-Power IoT Applications

The constant evolution in communication infrastructures will enable new Internet of Things (IoT) applications, particularly in areas that, up to today, have been mostly enabled by closed or proprietary technologies. Such applications will be enabled by a myriad of wireless communication technologies designed for all types of IoT devices, among which are the Long-Range Wide-Area Network (LoRaWAN) or other Low-power and Wide-Area Networks (LPWAN) communication technologies. This applies to many critical environments, such as industrial control and healthcare, where wireless communications are yet to be broadly adopted. Two fundamental requirements to effectively support upcoming critical IoT applications are those of energy management and security. We may note that those are, in fact, contradictory goals. On the one hand, many IoT devices depend on the usage of batteries while, on the other hand, adequate security mechanisms need to be in place to protect devices and communications from threats against their stability and security. With thismotivation in mind, we propose a solution to address the management, in tandem, of security and energy in LoRaWAN IoT communication environments. We propose and evaluate an architecture in the context of which adaptation logic is used to manage security and energy dynamically, with the goal of guaranteeing appropriate security, while promoting the lifetime of constrained sensing devices. The proposed solution was implemented and experimentally evaluated and was observed to successfully manage security and energy. Security and energy are managed in line with the requirements of the application at hand, the characteristics of the constrained sensing devices employed and the detection, as well as the threat, of particular types of attacks.

Motivating Users to Manage Privacy Concerns in Cyber-Physical Settings—A Design Science Approach Considering Self-Determination Theory

Connectivity is key to the latest technologies propagating into everyday life. Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) applications enable users, machines, and technologically enriched objects (‘Things’) to sense, communicate, and interact with their environment. Albeit making human beings’ lives more comfortable, these systems collect huge quantities of data that may affect human privacy and their digital sovereignty. Engaging in control over individuals by digital means, the data and the artefacts that process privacy-relevant data can be addressed by Self-Determination Theory (SDT) and its established instruments. In this paper, we discuss how the theory and its methodological knowledge can be considered for user-centric privacy management. We set the stage for studying motivational factors to improve user engagement in identifying privacy needs and preserving privacy when utilizing or aiming to adapt CPS or IoT applications according to their privacy needs. SDT considers user autonomy, self-perceived competence, and social relatedness relevant for human engagement. Embodying these factors into a Design Science-based CPS development framework could help to motivate users to articulate privacy needs and adopt cyber-physical technologies for personal task accomplishment.

Preventing MQTT Vulnerabilities Using IoT-Enabled Intrusion Detection System

The advancement in the domain of IoT accelerated the development of new communication technologies such as the Message Queuing Telemetry Transport (MQTT) protocol. Although MQTT servers/brokers are considered the main component of all MQTT-based IoT applications, their openness makes them vulnerable to potential cyber-attacks such as DoS, DDoS, or buffer overflow. As a result of this, an efficient intrusion detection system for MQTT-based applications is still a missing piece of the IoT security context. Unfortunately, existing IDSs do not provide IoT communication protocol support such as MQTT or CoAP to validate crafted or malformed packets for protecting the protocol implementation vulnerabilities of IoT devices. In this paper, we have designed and developed an MQTT parsing engine that can be integrated with network-based IDS as an initial layer for extensive checking against IoT protocol vulnerabilities and improper usage through a rigorous validation of packet fields during the packet-parsing stage. In addition, we evaluate the performance of the proposed solution across different reported vulnerabilities. The experimental results demonstrate the effectiveness of the proposed solution for detecting and preventing the exploitation of vulnerabilities on IoT protocols.

Proposed RPL routing protocol in the IoT applications

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IoT-enabled smart cities: a hybrid systematic analysis of key research areas, challenges, and recommendations for future direction

Open access
Published: 12 March 2024
Volume 1 , article number 2 , ( 2024 )

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Hossein Omrany 1 ,
Karam M. Al-Obaidi 2 ,
Mohataz Hossain 2 ,
Nayef A. M. Alduais 3 ,
Husam S. Al-Duais 4 &
Amirhosein Ghaffarianhoseini 5

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Cities are expected to face daunting challenges due to the increasing population in the near future, putting immense strain on urban resources and infrastructures. In recent years, numerous studies have been developed to investigate different aspects of implementing IoT in the context of smart cities. This has led the current body of literature to become fairly fragmented. Correspondingly, this study adopts a hybrid literature review technique consisting of bibliometric analysis, text-mining analysis, and content analysis to systematically analyse the literature connected to IoT-enabled smart cities (IESCs). As a result, 843 publications were selected for detailed examination between 2010 to 2022. The findings identified four research areas in IESCs that received the highest attention and constituted the conceptual structure of the field. These include (i) data analysis, (ii) network and communication management and technologies, (iii) security and privacy management, and (iv) data collection. Further, the current body of knowledge related to these areas was critically analysed. The review singled out seven major challenges associated with the implementation of IESCs that should be addressed by future studies, including energy consumption and environmental issues, data analysis, issues of privacy and security, interoperability, ethical issues, scalability and adaptability as well as the incorporation of IoT systems into future development plans of cities. Finally, the study revealed some recommendations for those interconnected challenges in implementing IESCs and effective integrations within policies to support net-zero futures.

A Review of Dominant Issues, Multi-dimensions, and Future Research Directions for Smart Cities

Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review

Simon Elias Bibri, Alahi Alexandre, … John Krogstie

Indicators for Smart Cities: Bibliometric and Systemic Search

Avoid common mistakes on your manuscript.

1 Introduction

Cities are a critical constituent of modern civilisation due to their environmental and socio-economic impacts on citizens’ lives [ 1 , 2 , 3 ]. Based on a recent report issued by the United Nations (UN), 55% of the world’s population currently lives in cities. However, the projections made by the UN shed light on the possibility of 6.5 billion people living in urban areas by 2050, equivalent to 68% of the world population [ 4 ]. This is triggered by urbanisation, a gradual shift in the residence paradigm of the human population from rural to urban areas, in tandem with the overall increase in the global population. As such, cities are expected to face daunting challenges since their resources and infrastructures are predicted to undergo an ever-increasing strain in the impending future [ 1 ]. In response, the concept of smart cities has emerged strongly over the recent decades owing to its potential for tackling these challenges through the deployment of Information and Communication Technology (ICT). Many cities around the globe have invested in becoming “smart”, aiming to improve city operations and the quality of services provided for citizens and the environment [ 2 , 5 ] (Table 1 ). In a comprehensive definition presented by Kondepudi et al. [ 6 ], smart cities are characterised as cities that utilise ICT and other advanced technologies to increase the quality of life for citizens, promote competitiveness, and improve the efficacy of urban services while assuring the perseverance of resources for present and future generations. In this regard, technologies such as the Internet of Things (IoT) play a crucial role in enabling cities to transition toward the smart city paradigm. IoT can be defined as a global infrastructure offering advanced services by interconnecting various physical and virtual “things” using interoperable ICTs [ 5 ]. The employment of IoT in the built environment enables devices to communicate with each other using different methods, such as ubiquitous and pervasive computing, sensor networks, and embedded devices [ 7 , 8 , 9 ]. The concept of integration was initially introduced as smart city testbeds that offered a platform for researchers to investigate new methods before implementing them as robust solutions. Two large projects SmartSantander and OrganiCity were introduced as smart city testbeds using IoT experimentations at an urban scale in Europe [ 10 , 11 , 12 ].

In recent years, increasing attention has been given to the deployment of IoT technologies to support smart cities to meet specific goals within Sustainable Development Goals (SDGs) such as Good Health and Wellbeing (SDG3), Industry Innovation and Infrastructure (SDG9), Sustainable Cities and Communities (SDG11) and Responsible Consumption and Production (SDG12). For instance, several applications of IoT in smart cities that are in line with SDGs including smart buildings, smart energy management, smart water management, health monitoring, environmental monitoring, intelligent traffic management, smart parking solutions, connected public transportation, smart waste management, public safety and surveillance. While emerging IoT technologies significantly contribute to smart cities aligning with SDG9, they also have an impact on the global economy. In this context, statistical information from several organisations such as ‘IoT Analytics’ on the global IoT enterprise spending dashboard indicated that the IoT enterprise market size steadily increased at a compound annual growth rate (CAGR) of 14% in 2019 to 22% in 2023 [ 18 ]. Allied Market Research [ 19 ] stated that smart cities and applications based on IoT are expected to reach $5.4 Trillion in 2030. The market was valued at $648.36 billion in 2020 and is projected to reach $6,061.00 billion by 2030 [ 19 ]. Statista indicated that approximately 50 billion IoT devices will be used around the world by 2030 [ 20 ], which has influenced the development of smart cities to increase from 118 cities in 2021 to 141 cities in 2023, as reflected in a report by IMD World Competitive Centre (WCC) [ 21 ].

Since 2010, various studies discussed the concept of the Internet of Things for smart cities. Initial database searching was conducted using the Web of Science (WoS) with the keywords “Internet of Things”, “IoT”, “and/for”, and “Smart Cities”. The search pointed out that the number of publications between 2010 and 2013 was limited. Subsequently, the number of publications in this field has increased, recording more than 100 publications in 2014 while it reached more than 1000 in 2022 in different fields. By targeting “highly cited papers”, the search returned less than 200 papers from the Web of Science Core Collection in different fields. Interestingly, the findings revealed three papers that received the highest number of citations above 1000, including studies by Zanella et al. [ 22 ], Botta et al. [ 23 ] and Lin et al. [ 24 ].

As mentioned earlier, a plethora of research has been developed investigating various aspects of IoT-enabled smart cities (IESCs). In response to the increasing number of publications in the field of IESCs, many review articles have been published aiming to solidify the flourishing knowledge in the field. The focus of these papers has mainly been limited to particular aspects of IoT in smart cities. Amongst them are studies that provided an overview of IESCs’ concept [ 3 , 7 , 9 , 25 , 26 ], studies that investigated the key IoT technologies and infrastructures for smart cities [ 8 , 27 , 28 ], and those that reviewed key features and applications of the IoT technologies to support the development of smart cities [ 2 , 29 ].

Nonetheless, the rapid advancements in the field are outstripping the possibility of addressing various aspects of IESCs in a single literature review article, and this most likely can be the main reason for the absence of a comprehensive literature review in this field. In addition, performing a holistic literature review of IESCs can be challenging due to the multi-faceted nature of this research area in which the current body of literature often spans across multiple disciplines [ 5 ]. This may further point out the diverse, yet fairly fragmented intellectual base of IESCs.

Therefore, this study adopts a hybrid literature review technique consisting of bibliometric analysis, text-mining analysis, and content analysis to systematically analyse the literature connected to IESCs. To the best of the authors’ knowledge, this is the first study of its kind that investigates the IESCs literature using such a comprehensive review approach. The objectives of this study can be summarised as (i) to identify the key research topics in the field of IESCs, (ii) to critically analyse the most popular realms of IESCs research identified via bibliometric analysis, and (iii) to provide recommendations for future development of IESCs. The outcomes of this research offer a status-quo understanding of IESCs literature to the interested communities, providing them with a view of the most popular research streams as well as emerging research themes in the field. This can be particularly useful for the scientific community as the findings of this study shall furnish them with an understanding of research areas that require further investigations.

2 Methodology

The overall methodological approach of this research consists of three major stages, as illustrated in Fig. 1 . The following sections provide further details on each of these stages.

The overall research approach of this research

2.1 Database development

The choice of a database for performing scientific reviews is utterly important due to its impact on the quality of results [ 30 , 31 ]. To date, several databases have been developed to assist scholars with conducting advanced searches through various bibliometric sources such as Medline, Google Scholar, ScienceDirect, Scopus, and Web of Science (WoS). The difference between these databases resides in their coverage when it comes to research disciplines [ 31 ]. Among all, the WoS is one of the most widely utilised databases for the purpose of review analysis owing to its distinguished features in enabling researchers to gain access to more than 171 million scholarly records available via 34,000 journals, allowing users to carry out advanced searches, and offering access to over 1.9 billion cited references across various disciplines [ 32 , 33 ]. As such, this paper has selected WoS as the primary database for the retrieval of publication materials owing to its comprehensive coverage and scientific soundness.

The first step involved constructing a comprehensive search syntax consisting of terms related to the concept of IoT-enabled smart cities. To this end, a search string was formulated using keywords such as “Internet of Things” OR “IoT”. These keywords were thence combined with “Smart Cit*” OR “Cit*” OR “Urban” OR “Built Environment” via Booleans (“AND”) and deployed as the search query for retrieval of relevant data in the WoS database. It is also noteworthy to mention that the scope of the current paper is limited to the investigation of IoT applications in the context of urban environments.

The constructed search string was applied in the Web of Science Core Collection (including Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Emerging Sources Citation Index (ESCI), Conference Proceedings Citation Index-Social Science & Humanities (CPCI-SSH), Conference Proceedings Citation Index-Science (CPCI-S), and Arts & Humanities Citation Index (A&HCI)) database indexed since 1900 using the “titles, abstracts, and keywords” of scholarly materials. The search returned 4223 documents on the 2nd of October 2022 including 2018 articles, 1811 proceeding papers, 203 review articles, 81 early access, 66 book chapters, 36 editorial materials, 2 books, 2 datasets, and 4 miscellaneous.

Further, this paper considered several inclusion criteria to filter out materials irrelevant to the defined objectives. First, the “ Document Types ” filter was used to retain only documents classified as “ articles ”, “ review articles ”, “ books ” and “ book chapters ” since these materials are considered “certified knowledge” because of their reputability and comprehensiveness [ 34 ]. Second, documents written in non-English languages were also excluded. Third, resources that were not related to the IESCs (e.g., law, medical science, agriculture, nursing, parasitology, and fisheries) were filtered out using the filtering functions of the WoS.

This ensured that only documents directly relevant to the concept of IESCs were retained for further analysis. Thereupon, a peer-review check was conducted to ensure that the selected articles underwent a rigorous peer-review process. This was done by cross-referencing the publication sources of the shortlisted articles with recognised databases and directories of peer-reviewed journals, such as Ulrich's Periodicals Directory or the Directory of Open Access Journals (DOAJ). This thorough verification process ensured that only articles from sources with established peer-review processes were included in the analysis. In addition, the titles, abstracts, and conclusions of shortlisted articles were scanned to ensure their alignment with the scope and objectives of this paper. In this stage, studies must have investigated the IoT implementation in the context of smart cities to be included in the final analysis. Hence, materials solely focused on IoT implementation or exploring IoT in unrelated contexts (e.g., manufacturing) were excluded. It is also noteworthy to mention that studies with incomplete information, both in terms of methodology and reporting of findings, were excluded.

As a result, the application of these filters led to a downsizing of the initial search results to 843 documents. These materials were then exported using the ‘ Tab-delimited ’ file format to be processed and analysed via VOSviewer.

2.2 Identification of key research themes

The keywords co-occurrence analysis is commonly utilised for mapping out the theoretical and empirical knowledge in research disciplines [ 30 , 31 ]. The application of this method enables researchers to demonstrate the cumulative knowledge of the target literature, unveiling the conceptual and thematic structure of the research and identifying key areas within a given research domain [ 31 ]. In this approach, the calculation is done based on the frequency of co-occurring keywords in publications and their corresponding strength of associations. The current paper constructed the network of keywords co-occurrence via VOSviewer software using all keywords of selected studies, including “author’s keywords” and “keywords plus”, e.g., those indexed by publishing journals.

VOSviewer is one of the bibliometric tools being widely employed across many research disciplines to assess the literature, such as smart cities [ 35 ], innovation in the construction industry [ 36 ], or construction waste management [ 37 ]. This software offers a user-friendly interface, allowing researchers to develop, visualise, and explore the bibliometric nexus of various entities related to a given research area [ 31 ]. One of the attributes of this software relates to the function of data cleaning via using a thesaurus file [ 38 ], providing the possibility of polishing the dataset to enhance the accuracy of analyses by merging different variations that may exist for one term. Hence, a thesaurus file has been developed to ensure the precision of analyses (e.g., IoT, internet of things, and internet of thing refer to one term, thus they have been merged and represented as IoT). Further information regarding data analysis and science mapping can be found in the software manual [ 38 ].

Further, this study applied text mining analysis via VOSviewer software. This technique is aimed at extracting information from a massive corpus of documents in texts [ 39 , 40 ]. The use of text mining enables researchers to adequately capture semantic structures and prevalent patterns of phrases that characterise a large amount of data in text format [ 39 , 40 ]. In this paper, text mining analysis was employed using the term co-occurrence algorithm to analyse the concatenation of titles and abstracts of 843 publications. The adoption of this technique, in combination with keywords co-occurrence analysis facilitated identifying research areas with the highest interest that shape the conceptual structure of IESCs.

2.3 Content analysis

Upon identifying the major thematic research themes, the selected materials were imported into an Excel Spreadsheet in order to be classified based on the results of previous steps. To this end, the titles, abstracts, and conclusions of all 843 studies were thoroughly read to allocate each study to its corresponding research theme category. Thereafter, studies in each category were critically analysed. The results of these analyses are provided in Sect. 3 .

3 Results and discussions

3.1 an overview of results.

Figure 2 a illustrates the overall publication trend per year, focusing on IoT implementation in smart cities. Starting from 2017, a significant surge in publications can be observed with 75 articles published in that year alone which is equivalent to nearly 60% of all materials published in the previous 6 years combined. This notable increase can be attributed to multiple factors, such as increasing investment in innovative technologies, rising awareness of IoT's role in addressing urban challenges, and advancements in IoT technology and infrastructure [ 35 , 41 ]. The impact of policies aimed at stimulating innovative ICT-based solutions for enhancing city governance can also be highlighted as one of the key contributors to the growing popularity of this topic, as many countries have begun supporting smart city projects by adjusting their policies over recent years [ 42 , 43 ]. The support provided by policies can potentially drive further research and innovation in the field. Furthermore, the publication trend has shown a consistent upward trajectory in recent years, with 53% of all materials (i.e., 445) published between 2020 and 2022. Considering this steady rise in publications across these periods, alongside the escalating interest in ICT-based technologies, the interest in IESCs is expected to continue growing in the foreseeable future.

a Publication trend of IoT implementation in smart cities. Note that the limited number of documents identified for 2023 is attributed to the search that was conducted in late 2022. Hence, we expect an increase in publications for 2023, indicating an upward trend. b Top 10 journals with the highest publications

The findings also revealed that a total of 127 journals collectively published 843 articles spanning the years 2010 to 2022. Figure 2 b illustrates the top ten prominent journals that have significantly contributed to the advancement of the field, accounting for nearly 51% of the published materials. Notably, IEEE Access emerged as the leading publisher, with 14% of the selected materials included in this review, followed by Sensors–MDPI (13%), IEEE Internet of Things Journal (8%), and Smart Cities–MDPI (4%). A common characteristic shared among these top journals is their multidisciplinary nature, covering a diverse array of topics related to "smart cities" and "IoT." Yet, there is a particular focus in the scope of these journals that resonates with the main research themes (i.e., data analysis, network and communication management and technologies, security and privacy management and data collection) identified in this research (See the next Section).

3.2 Identification of key research areas using bibliometric analysis

Figure 3 shows the results of this analysis performed for a minimum threshold of 25 keywords. In this figure, the frequency of co-occurring keywords in the target literature is represented by the sizes of the nodes while the strength of associations between keywords is represented by the thickness of the connecting links. The location of the nodes can also be an important point of reference, indicating that the keywords with proximity would most likely have strong relationships with each other.

Illustration of keywords’ co-occurrence analysis

Thirty keywords with the highest values of co-occurrence, along with their respective link strength are shown in Table 2 , indicating that these keywords have received high attention in the literature and are strongly associated with other keywords. In this regard, the high values of “IoT” and “smart cities” keywords were expected because they were included in the search string used for the retrieval of primary data. Nevertheless, these terms were kept in the analysis since their removal would have led to omitting other keywords linked to them. As shown in Fig. 3 , the results of the keywords co-occurrence analysis identified five major clusters. The findings suggest that keywords such as “smart buildings”, “artificial intelligence”, “big data analysis”, and “challenges” from cluster 1; “IoT”, “urban areas”, and “energy efficiency” from cluster 2; “cloud computing”, “edge computing”, “energy consumption”, “communication technologies”, and “5G networks” from cluster 3; “blockchain technology”, “trust management”, and “authentication protocols” from cluster 4, and “machine learning”, “deep learning”, “time-series analysis”, and “intrusion detection” from cluster 5 are located at the proximity of the clusters’ boundaries, implying that these domains of research are cross-cutting with solid associations with different clusters.

Table 3 presents the results of the text-mining analysis, introducing four key research themes within the domain of IESCs that have attacked the highest interest in terms of publications. These areas include (i) data analysis approaches, (ii) network and communication management and technologies, (iii) security and privacy management, and (iv) data collection approaches. The outcomes of the text-mining analysis are, to a large extent, consistent with the results of the keywords co-occurrence analysis shown in Fig. 3 and Table 2 .

The next step for this paper is to critically discuss these four areas. Such an analysis can provide the target audiences with a state-of-the-art understanding of the recent developments in IESCs and create a proper basis for future research to contemplate further innovations and advancements in the field.

3.3 Critical analysis of research themes

3.3.1 data analysis approaches in iescs.

Data in smart cities is generated and analysed based on multiple relationships between various technological systems and their physical environments. IoT data plays a vital role in the success of any smart city [ 72 , 73 ]. Accessibility to real data offers the capacity to instantly assess the performance of any entity within a smart city by continuously collecting and analysing data from numerous sectors (e.g., health care or transportation), and actively attending to any anomalies manifested in manufacturing products [ 20 ]. Choi [ 47 ] stated that the quality of services in a smart city relies on the type of generated data gathered from different sources, levels and scales. Therefore, data collected from IoT devices should be effectively processed and transformed into actionable insights to regulate the massive flow of information in any smart city [ 74 ]. IoT data can be generated from various sources, including (i) equipment data to demonstrate the status of the IoT devices which facilitate activities of predictive maintenance, (ii) submeter data to measure utility usage (e.g., information about water, and electricity), and (iii) environmental data to evaluate and sense temperature, humidity, air quality and movements. IoT data is often generated in discrete values representing facts or numbers that convey information including useful, irrelevant or redundant information, hence these data need to be processed and analysed in order to be meaningful [ 75 ].

Data analytics is an integral part of deploying IoT technologies in smart cities, enabling the assessment of datasets to retrieve meaningful outcomes [ 76 ]. These outcomes are thence presented using statistics, patterns and forms that help to establish effective decision-making processes [ 45 , 77 ]. Moreover, data analytics provides solutions to overcome the issues attributed to unstructured data, including controlling variant types and formats of generated data [ 78 ]. Figure 4 presents the connection between IoT data to demonstrate the flow of collecting data from sensors installed in IoT devices and the process of transferring data through an IoT gateway for it to be analysed [ 79 ] and how different data analytics processes can be employed for processing data in the context of IESCs. Data of IoT in smart cities undergoes different complexities that are undertaken based on specific values as listed below:

Descriptive/time series analytics demonstrates time-based data and massive in-motion data sets to identify urgent situations, instant actions as well as associated trends and patterns [ 50 , 80 ].

Diagnostic analytics employs data mining and statistical analysis to detect latent relationships and patterns in data that are applied to uncover the causes of specific problems [ 49 ].

Predictive analytics aims to predict future events by employing various statistical and machine learning algorithms to develop models that can be utilised for predictions about future events such as weather forecasting [ 81 ].

Prescriptive analytics uses both descriptive and predictive analyses to recognise suitable actions based on a specific situation which is common with commercial IoT applications to reach better conclusions [ 76 ].

Data process in IoT and different types of data analytics based on complexity and value in IESCs

To analyse IoT data, it is vital to recognise the nature of data prior to processing. In principle, IoT data is categorised into structured and unstructured. Structured data follows a specific model to define how the data is organised or represented. Data collected via IoT sensors often comes in structured values, especially if these sensors are used for environmental assessments such as air temperature, humidity, or air quality. This type of data is simply formatted, queried, stored and processed. Unstructured data cannot fit into predefined data models such as text, speech, images, and videos, which requires conversion into a logical schema for decoding data. According to International Data Corporation, approximately 80% of business data is unstructured [ 5 ]. Therefore, data analytics techniques are gaining considerable interest in IoT, especially in smart cities due to their capacity for processing unstructured data [ 46 , 82 ].

Data generated via IoT devices can also be classified as Real Time and Non-Real Time or in motion versus at rest [ 83 , 84 ]. Most data in IoT are in motion as they move across different networks until they reach their ultimate target. However, inactive data is considered data at rest that can be stored in different digital forms such as mobile devices, spreadsheets, or databases. Mohammadi et al. [ 85 ] highlighted IoT data with five features: high volume, high velocity, high variability, value and veracity. High-volume data is distinguished by its enormous quantity, which is constantly produced via many IoT devices. High velocity refers to data type generated at high speed by many IoT devices. High variability data, which is inconsistent due to the dynamic nature of IoT environments, encompasses a variety of data formats such as unstructured, semi-structured, quasi-structured and structured data. Value data exemplifies the significance of collected data from IoT sensors after being analysed. Finally, veracity data promotes consistency, quality, and reliability in generated data.

Yang and Shami [ 45 ] and Yin et al. [ 51 ] demonstrated tasks in IoT data analytics that consist of classification, clustering, regression and anomaly detection. Furthermore, Yang and Shami [ 86 ] indicated that information from IoT is processed according to the type of data. Alternatively, algorithms in IoT data analytics are categorised into two types of learning: batch and online. L’heureux et al. [ 87 ] indicated that batch learning represents a method to analyse static IoT data in batches via Traditional Machine learning algorithms. Online learning is a technique that employs various methods to train models in IoT environments by continuously using incoming online IoT data streams [ 88 ].

Data analytics relies on advanced methods to perform analysis. Among all, machine learning (ML) has gained momentum for providing new knowledge and improving data quality via learning and processing repetitive data to attain efficiency [ 89 ]. ML employs two forms of techniques, namely supervised and unsupervised learning [ 90 ]. The machine learns rules and models of datasets drawn from clustering or frequency of particular data using pattern recognition and reinforcement techniques [ 90 , 91 ].

Another analytical technique often used for data processing in IoT-enabled platforms is deep learning. Li et al. [ 92 ] described deep learning as an ideal method for obtaining precise information from raw IoT data that existed in complex environments. Atitallah et al. [ 76 ] categorised deep learning into different modes of learning such as unsupervised, semi-supervised, supervised and reinforcement. Deng [ 93 ] classified deep learning into discriminative, generative and hybrid classes. According to Atitallah et al. [ 94 ], the most common deep learning models are Convolutional Neural Network (CNN), Deep Belief Network (DBN), Deep Reinforcement Learning (DRL), Generative Adversarial Networks (GAN), Recurrent Neural Network (RNN) and Stacked Auto Encoder (SAE). Figure 5 summarises different deep learning models, along with their applications. IoT data analytics utilises different advanced computational platforms to improve performance and accuracy. Three types of computational analysis cover computing using Cloud, Fog and Edge [ 94 ]. In fact, both Fog and Edge represent an extended development of cloud computing that offers the power for data analytics to be performed near the source of generated data.

Taxonomy of deep learning models and their main applications

3.3.1.1 Applications of IoT data analysis in IESCs

The process of retrieving information from data generated via IoT devices requires employing data mining tools. Mining data is a process of identifying patterns and correlations or discovering anomalies within large datasets to forecast outcomes. Various types of algorithms could be used for data assessment such as clustering algorithms. The selection of algorithms for clustering data is based on considering different variables such as size, data size, data type and the number of clusters. Daissaoui et al. [ 95 ] listed several algorithms to generate and manage data in smart buildings. The study covered four general types identified in previous studies including probabilistic graphical models, system identification methods, vector support machines and data mining and clustering. Several studies used probabilistic graphical models, e.g. Stoppel and Leite [ 96 ] incorporated probabilistic methods into simulation by analysing building energy models for describing occupant presence in buildings. Chen et al. [ 97 ] proposed methods using stochastic inhomogeneous Markov chains to examine occupancy in single-zone and multi-zones within a building. For vector support machines, Akbar et al. [ 98 ] investigated the occupancy state in a smart office by proposing a non-intrusive approach via Support Vector Machines to detect an occupancy state by using electricity consumption data. For data mining and clustering, D’Oca and Hong [ 99 ] presented a framework that utilised a three-step data mining through a decision tree model. The proposed model was used for forecasting occupant presence and occupancy patterns in office spaces.

Hong [ 100 ] suggested an approach to operate data analytics using Fog and Edge computing and central servers to enhance decision-making from IoT devices. Yang [ 101 ] and Rahman et al. [ 102 ] proposed a model to ingest IoT data into the nodes of Fog computing. The model covers the limitations relating to the fundamental aspects of data analytics associated with data, humans, systems and optimisation. Portelli and Anagnostopoulos [ 103 ] proposed a learning approach that enhances the prediction and precision of IoT data by using Adaptive Vector Quantization and Linear Regression to maximise communication efficiency. Lujic et al. [ 104 ] used a set of algorithms to examine IoT data that is affected by failures in sensors, systems and networks concerning smart buildings to recover incomplete datasets, reducing forecasting error and decreasing running time.

Several studies presented frameworks that combined data assessment from real-time and historical records to achieve prediction by using Decision Tree Regression, Multiple Linear Regression, Support Vector Regression and Random Forest Regression [ 105 ]. For instance, Xiaoyi et al. [ 48 ] presented a model for smart cities by analysing the management of renewable energy systems using a Multi-Objective Distributed Dispatching algorithm. The proposed method managed to decrease energy consumption while delivering high utility services in a smart city. Gomes et al. [ 50 ] also presented a framework and modules to facilitate data analytics in real-time and data stream enhancement. The analysis layer was developed to include a set of modules to extract relevant information as shown in Fig. 6 . These modules include (1) pre-processing to eliminate invalid values and reject values out of a specified range; (2) aggregation to aggregate a data set by using various functions (e.g., min, max, sum, or count); (3) statistics of a dataset in the form of median, average, standard deviation, variance, kurtosis and skewness; (4) pattern to detect behaviour patterns such as trend changing, and stability; (5) clustering to group datasets based on distance or similarities using density-based clustering, hierarchical clustering, k-means, or subspace clustering, and (6) prediction to forecast values in several steps using various approaches such as Autoregressive Integrated Moving Average, Artificial Neural Network (ANN), Kalman filter, and Forecasting Method to Model Time Series Data. Table 4 summarises the main applications of data analysis research in IESCs.

A model of data analysis in IoT processing layer

The summary showed that data processing and management in IoT devices are considered critical. The survey demonstrated various algorithmic techniques to facilitate data. It was found that studies are still exploring numerous methods based on the level of processing. The assessment revealed that many studies have been developed to create actionable platforms to enhance the data stream and data management, control data anomalies and improve data analytics. The assessment indicated that processing real data from IoT is challenging due to limitations in IoT architecture, data speed, size, accuracy, response and security. It was noted that many studies are still reviewing and testing the integration of different algorithmic techniques for new applications to obtain effective solutions. In addition, developing new IoT frameworks by integrating different algorithmic techniques has added a new level of complexity in designing smart cities. On the other hand, these findings indicate that IoT and big data analytics are still under development and require further research and investigation, especially in designing smart cities.

3.3.2 Network and communication management and technologies in IESCs

IoT network consists of various components such as sensors, software, gadgets and appliances that communicate and exchange information data with each other. Management of IoT networks allows for various functionalities such as authentication, configuration, provisioning, routing, monitoring and security to maintain a network performance in terms of low energy consumption and low latency [ 107 ]. Aboubakar et al. [ 108 ] stated that standard network management in IoT consists of specific logical elements that include agents, network devices, managed devices, and network managers which are supported by management databases and messaging protocols. Each element performs specific tasks to keep the network running. The ‘‘agent” represents the software which operates on managed devices or groups of IoT devices. The agent performs data aggregation into a combined stream to central IoT applications and is typically managed by IoT Gateway. The “network devices” include Firewalls for a security feature, Servers to manage the devices within that network, Client Applications to allow users access to complete tasks, Routers to connect to networks, Switches to allow the devices to communicate with each other and Access Points to connect the endpoint device with the network. The “managed devices” allow organisations to better monitor and control their connected IoT devices to register and deploy connected devices, device logging, organising devices into relevant groups, indexing and searching device fleets, remotely manage and update devices, custom scripting, security tunnelling for diagnosing and resolving issues and customisable dashboards for centralised device control. The management of devices plays an important role in increasing the speed of registration of IoT devices, improving device organisation, easier remote-device management and simplifying device location. The “network manager” is a device that manages a group of managed nodes. It facilitates network topology, synchronisation of devices, and management of traffic and congestion in IoT systems. The ‘‘management database” is located in the managed device and includes data about the managed device parameters. The ‘‘messaging protocols” function as a data exchange that connects information between the managed devices and the network manager. Generally, these networks need to be efficient to support specific functional operations (Fig. 7 ), such as Network Configuration Management, Security Management, Topology Management, QoS Management, Network Maintenance and Fault Management. These functionalities are typically provided as a network service in an IoT environment to ensure sufficient network performance.

Overview of entities, operations and solutions in IESCs network management

Aboubakar et al. [ 108 ] presented management solutions for IoT networks from various perspectives. These IoT network management solutions included software-defined networking (SDN)-based, machine learning-based, cloud-based, and semantic-based frameworks. The design of a new network must incorporate efficient management processes for managing a significant number of linked devices, immense amounts of data, and services with varying Quality of Service (QoS) requirements. Monitoring the network’s infrastructure makes it possible to detect any events or changes that might impact the network's resource security or usage. In this regard, several protocols have been developed to help with network management. These protocols control and monitor different network components such as gateways, devices, and terminal servers. Since the implementation of IoT low-power networks in both public and private spaces is growing at unprecedented rates, network management has emerged as a critical component of IoT low-power networks for maximising their performance and ensuring their continued availability.

3.3.2.1 Communication technologies in IESCs

In computer networks, there are several common types of network technologies such as Local Area Network (LAN), Wireless Local Area Network (WLAN), Virtual Private Network (VPN) and Wide Area Network (WAN). LAN connects devices in the same proximity, e.g., connecting devices in a small office or a building. WLAN functions in the same way as a LAN, but it uses wireless connections. VPN is a secure network which is used to communicate with encrypted data. WAN offers the possibility to connect devices across a large distance. In smart cities, heterogeneous objects are connected by IoT communication technologies to deliver intelligent services. In IoT, several wireless network types can facilitate IoT sensor deployment and IoT applications in industries such as Radio-frequency identification (RFID)/Bluetooth Low Energy (BLE)/Near Field Communication (NFC), Wi-Fi/(LoRa and Wi-Fi)LoFI, MESH Protocols, NarrowBand-Internet of Things (NB-IoT), Ultra-Wide Bandwidth (UWB), Low-Power Wide-Area Networks (LPWAN) (LoRa, Sigfox), ZigBee and Cellular (3G/4G/5G/6G) [ 109 ]. The next paragraph highlights some of these technologies.

Bluetooth is a short-wavelength radio-based communication standard for low-power data transfer between electronic devices over short distances [ 110 ]. Bluetooth 4.1, lately issued by the Bluetooth special interest group, offers Bluetooth Low Energy besides offering high-speed and IP connectivity to promote IoT applications [ 111 ]. Machine to Machine (M2M) is the next generation of the Internet revolution connecting many devices via the Internet. The M2M was initially implemented using RFID as the first technology (RFID tag and reader), and now it is aimed at automating the communications between machines and devices via provided networks without human intervention. Ultra-wideband communication (UWB) is a communication technology that was developed to strengthen communications in areas with a low-range coverage while consuming a low amount of energy and providing a high bandwidth. Recently, the number of applications using this technology to connect sensors has increased [ 112 ]. Wi-Fi is a wireless networking technology that allows devices within a 100-m radius to exchange data using radio waves [ 57 ]. In certain ad hoc configurations, Wi-Fi enables smart systems to communicate and exchange data without requiring a router. For low-power wireless networks with the goals of reliable and scalable communications, the IEEE802.15.4 standard details both the medium access control and the physical layer [ 113 ]. LTE-A (LTE Advanced) represents an enhanced variant of Long-Term Evolution (LTE) that offers benefits such as increased bandwidth (up to 100 MHz), spatial multiplexing on both the downlink and the uplink, wider coverage, greater throughput, and lower latency [ 114 ].

There are a variety of technologies developed to improve the effectiveness of network management. One of these technologies is Radio Access Network as a Service (RANaaS) which has been developed to facilitate adaptable management of network resources [ 115 ]. IPv6-based networks have management protocols in place, such as Long-Range Wide Area Network (LoWPAN), Network Management Protocol (LNMP) and Simple Network Management Protocol (SNMP) [ 116 ]. In addition, self-organising wireless networks can benefit from Time Synchronised Mesh Protocol (TSMP), a communication protocol that empowers the sensors/devices to be synchronised with one another. Furthermore, Software-Defined Networking (SDN) is a key component in the development of 5G systems, which intends to reduce complexity in network management and design while also allowing for the network to be managed and reconfigured in a way that is automated, flexible and dynamic [ 117 , 118 , 119 ]. Furthermore, the paradigms provide features for managing heterogeneous devices in a wide range of deployments and use cases [ 120 ]. Despite the potential of these paradigms for introducing effective methods in managing networks, several issues remain [ 121 ].

Several studies such as Cedillo-Elias et al. [ 56 ] proposed cloud platforms utilising SDN and OpenStack to safeguard citizens' data contained by the government and make extra efficient usage of existing IT infrastructures for smart city facilities. In another study, Purnama et al. [ 54 ] analysed the viability of deploying IoT connectivity for AMI (Advanced Meter Infrastructure) services in Surabaya, Indonesia. To experimentally evaluate and compare multi-hop and single-hop LoRa topologies in terms of energy efficiency and range extension. Aslam et al. [ 122 ] presented a case study that measured Packet Reception Ratio (PRR) for different source-to-destination distances, transmission powers and spreading factors (SFs). The findings demonstrated that the configuration of a LoRa network with multiple hops can save a significant amount of energy and improve coverage. Nashiruddin and Nugraha [ 53 ] investigated LoRaWAN's network planning for Smart Metering Infrastructure (SMI) by counting the number of gateways required to support the communication of SMI devices. Fraile et al. [ 55 ] also compared IEEE 802.15.4 and LoRa for indoor deployments in IoT-enabled school buildings. Using information gathered from 8 networks and 49 devices spread across 6 educational facilities, the study compared the efficiency and cost of various IoT solutions. The outcomes demonstrated that LoRa can achieve lower costs and higher data rates than IEEE 802.15.4 while maintaining similar or better link quality. Table 5 presents the recent protocols to enhance the management of IoT communication.

To sum up, this section discusses various network and communication management and technologies used in IESCs to connect heterogeneous objects and provide intelligent services while consuming low power. Intriguingly, surveying the literature on IoT network management shows no detailed or comprehensive overview available of existing resource-constrained network solutions. Future research could focus on developing more energy-efficient and scalable communication technologies to handle the increasing number of smart devices in smart cities. Further research should aim to investigate several solutions to improve data security, reliability, energy efficiency, network scalability, interoperability, and data privacy. These solutions include encryption, authentication, access control, low-power communication protocols, energy-efficient hardware, cloud-based architectures, edge computing technologies, blockchain, digital certificates, context-aware, adaptive IoT communication systems, and hierarchical clustering. Numerous studies have investigated the use of IoT connectivity for various applications, such as smart city services and advanced meter infrastructure, and have compared the efficiency and cost of different IoT solutions, including LoRa and IEEE 802.15.4. Therefore, it is recommended that IoT network management should incorporate efficient management processes for handling a large number of devices, vast amounts of data, and diverse services with varying QoS requirements. Furthermore, network managers should continuously evaluate and adopt new technologies and protocols to enhance network performance and security. Developing an effective solution for managing IoT networks can be challenging due to the inherent constraints of IoT networks [ 123 ], such as the diversity of IoT devices, the fluidity of network topologies, the scarcity of available resources, and the unpredictability of radio links. More research is needed to design effective solutions to manage IoT with low-power networks that can handle heterogeneity while ensuring security and privacy and allowing for scalable resource utilisation.

3.3.3 Security and privacy management in IESCs

Smart cities are equipped with advanced technological infrastructures to actively monitor and control physical objects and furnish citizens with real-time information about transport, smart parking, traffic, or public safety [ 124 ]. Nevertheless, there are various issues related to security and privacy at different levels of smart cities’ architecture. This is largely due to the nature of devices deployed in these cities which are often resource-constrained, thus making cities vulnerable to security attacks [ 64 ]. An example of such attacks is the major electricity breakdown that occurred in Ukraine due to malicious attacks on smart grids [ 124 ]. Therefore, this section discusses six major areas of security and privacy issues in IESCs.

Intrusion Detection System (IDS) . As the number of things connected to systems increases, the centralised or cloud-based IDS will suffer from excessive latency and network overhead. Subsequently, it makes it difficult to respond to attacks and detect rogue users. For example, a fog-oriented IDS was developed with the capacity to use an Online Sequential Extreme Learning Machine, which is decentralised in computing infrastructure and has no fixed location between the data source and the cloud [ 125 ].

Automobiles and Transportation . Attempts have been previously undertaken to develop a “Smart Accident Precognition System (SAPS)” aiming to minimise the risks of accidents and protect the users’ safety on the road. To further improve the system, SAPS was coupled with Google Assistant to make use of various embedded devices for monitoring several aspects of vehicles and passengers such as speed, distance, and safety measures. The real-time data are stored within the cloud and accessed by both the vehicle and the Google Assistant, allowing smarter decision-making and acting based on the previous data recorded [ 67 ]. However, the implementation of such a scheme may pose threats to unauthorised access to users’ personal details as well as gaining control over vehicles and transportation systems.

e-Healthcare System . This is an improvement to the traditional healthcare systems by connecting to IoT systems and the Internet. However, e-Healthcare systems are subject to the same issues as any IoT-enabled systems such as compromising privacy and personal data due to hacking by malicious users from across the Internet [ 126 ]. For example, in a smart city environment, the healthcare system is often a collaboration between the public and private sectors. Although the public sector can be the central decision-maker, distributing treatment and medicine to the private sector may be more effective and efficient. However, this means the personal information will be overseen by different parties and thus have a larger chance of being hacked or exposed [ 127 ].

Communication methods of IoT . Since the communication of IESCs is reliant on online networking systems, it is susceptible to different types of cyberattacks [ 124 ]. In communication, connectivity is a critical component in delivering an IoT solution. Many protocols can be employed within the same IoT solution to maintain the stability of IoT communications to be suited for varied contexts with different barriers and limits. Some of these difficulties relate to the physical elements, i.e., the distance between devices, the specific IoT task performed such as the need for real-time applications requiring higher and more stable connectivity capabilities, and the device’s computing resources such as weaker or power-saving devices may need communication protocols that require less power. Every of these communication protocols has their strengths and weaknesses and some of them are more prone to attacks [ 127 ].

Code and program level of IESCs . Data aggregation has different levels, such as the need to achieve trust and quality in shared information models to enable reuse, secure data interchange and transfer, and protection mechanisms for vulnerable devices [ 59 ]. As each of the IoT solutions is deployed with different objectives and means, on the coding level, they should be customised and carefully integrated to maximise their performance and data protection. As an example, most websites and Internet-connected apps incorporate at least one type of Web API to assist with a specific function in the grand scheme of things, such as Samsung SmartThings, which provides classes via an API to process HTTPS calls within the IoT solution asynchronously [ 127 ].

Ethics and morality of humans . Caution is required with those working with the system and those holding the collected data, as there are research papers which showed that immoralities and irresponsible acts of corporations and authorities are important causes of compromised security and privacy. A study conducted in a smart city in India showed that although not all the usable subjects think that it is significant, in general, the trust and intention of the authorities and holders of the gathered data are affecting their trust in IoT as a whole [ 128 ].

3.3.3.1 Managing and combating the issues

To combat the issues highlighted above, active attempts must be made to improve security and privacy by safeguarding communications of devices and networks. To this end, specific measures can be implemented.

To combat the ineffectiveness of a centralised IDS, hybrid semi-distributed and distributed intrusion detection systems can be promoted. In these systems, the associated databases demonstrate effective feature extractions and selections, combined with parallel machine-learning models and fog-edge coordinated analytics that can mitigate the risks of centralised IDS [ 62 ].

To combat possible security and privacy threats associated with transportation and automobile systems, including both unauthorized use of users’ information and attacks triggered by malware, spam, black holes, wormholes, and outages, it is necessary to improve Vehicular Sensor Networks in IoT-enabled transportation and automobile system in terms of robustness, reliability and security [ 65 ].

To improve security and privacy issues, an active defensive line is needed consisting of ML and blockchain technologies in which the former enables predicting and detecting vulnerabilities while the latter secures the networks by creating tamper-resistant records of shared transactions [ 61 ].

Securing different data layers is also a promising way to prevent security and privacy issues via using payload-based symmetric encryption for the data security layer, utilising computation of secured data for the data computational layer, and only extracting visions from the last data layer, i.e., the decision-making layer [ 60 ].

The physical layer is often ignored to be protected. The passive observer’s data is usually unreachable to the network's authentic source and destination nodes, deploying countermeasures such as an efficient “Sequential Convex Estimation Optimization” algorithm that can be very useful against them [ 129 ].

Table 6 presents a comprehensive summary of recent research in the field of security and privacy in smart environments. One of the notable observations is the diverse range of applications covered in the papers, including smart cities, e-healthcare systems, industrial IoT, smart homes, and more. This highlights the importance of context-specific solutions that can address the unique security and privacy challenges in different environments. The proposed solutions in the papers utilise various technologies, such as machine learning, blockchain, elliptic curve cryptography, and homomorphic encryption. While these technologies offer high levels of security and privacy, they also require significant computational resources, which can be a challenge in resource-constrained smart environments.

The limitations and trade-offs of the proposed solutions in the papers need to be carefully considered as well. For instance, Arunkumar et al. [ 130 ] presented a lightweight security key generating system that can detect and prevent security threats in smart cities using machine learning and elliptic curve cryptography. However, the hardware used to deploy the system is relatively immobile and consumes a lot of energy. The blockchain-based authentication method offers improved communication metrics and privacy-preserving features but comes at the cost of weaker identity management and slower automation speed [ 131 ]. Similarly, the routing model presented by Haseeb et al. [ 132 ] can efficiently establish direct trust between nodes but does not handle malicious attacks and flooding of messages well. The seamless authentication IoT framework for e-Healthcare systems presented by Deebak et al. [ 133 ] is more efficient and has a better packet delivery ratio and network lifetime, but consumes more resources compared to related works. Additionally, some papers address specific challenges in smart environments, such as predictive computation [ 134 ], security in fog computing [ 135 ], security in smart grid networks and access control in edge computing [ 136 ]. These papers highlight the importance of addressing specific security and privacy challenges in emerging technologies and infrastructures in smart environments.

In conclusion, Table 6 presents a summary of recent research in the field of security and privacy in smart environments. The diversity of applications, technologies, and challenges discussed in the papers highlights the need for context-specific solutions that can balance the trade-offs between security, privacy, efficiency, and scalability in resource-constrained smart environments.

3.3.4 Data collection approach in IESCs

The approach for data collection in the context of smart cities may vary depending on the smart data applications from macro to micro scales and the sectors from which the data is collected [ 138 , 139 , 140 ]. Different studies showed research in areas such as Energy Conservation, Urban Environment, Health & Wellbeing, Biodiversity, Surveillance/Security & Safety, Transportation & Mobility, Infrastructure & Communication, Tourism and Waste Management. Below, the review identifies a number of domains in which IoT can be integrated with advanced technologies for the purpose of data collection in smart cities.

Electrical Energy. Several studies collected and forecasted data from the microgrid using smart meters such as the Heuristic Intelligent Neural Decision Support System [ 141 , 142 , 143 ]. In a study, Abu-Rayash and Dincer [ 144 ] developed a new integrated solar energy system capable of meeting the energy demands of a small city of 5000 homes. The proposed system can also collect real-time solar energy and thermal energy data using Photovoltaic panels and thermal energy storage tanks, respectively.

Urban Environmental Pollution. Yu et al. [ 145 ] showed the effective collection of air pollution data (Particulate matter—PM2.5) from 242 cities in China based on an online IoT monitoring system. In another research, P.M2.5 sensors were deployed on street levels and via drones at Xidian University and Peking University using 4G (fourth-generation) internet network base platforms and the stations complied with narrowband IoT communications [ 146 ]. Furthermore, electrochemical (SNS-MQ135) and MQ9 gas sensors were employed via Bluetooth, ZigBee and Z-Wave networks to measure air quality at the polluted city Bucharest, Romania in order to detect carbon dioxide (CO2) level, ammonium (NH4), ethanol (C2H6O), toluene (C7H8), carbon monoxide (CO) and methane (CH4) [ 147 ]. Segura-Garcia et al. [ 148 ] also validated an IoT prototype for monitoring real-time Psycho-Acoustic Soundscape utilising 5G (fifth-generation) LTE-M1 sound monitoring devices. Further, Dembski et al. [ 149 ] developed an urban digital twin and computational simulations such as space syntax, SUMO—Simulation of Urban Mobility and simulated wind flow by installing a test sensor and mobile App – Reallabor Tracker for citizen’s feedback.

Biodiversity. In a study, Chen and Han [ 150 ] used a range of turbidity, oxidation–reduction, or pH potential (ORP), conductivity and dissolved oxygen (DO) sensors to measure water quality in Bristol, UK. In this project, a multi-parameter water quality sonde (Aqua Troll 600) was used to assess the water quality while an IP Network-based Camera was utilised to collate video images of the water surface. Studies also used low-cost data collection methods through renewable wireless sensor networks for measuring environmental parameters such as temperature, pressure, humidity, smoke, and noise sensors, smart IoT-enabled bins, pyroelectric infrared, UV/Lux sensors and rain sensors [ 151 ]. For instance, Gallacher et al. [ 152 ] deployed Echo Boxes that consisted of low‐cost sensor networks combined with artificial intelligence techniques to monitor bats’ activities in a large urban park. Podder et al. [ 153 ] also proposed an IoT-based Smart AgroTech system in the context of urban farming with the capacity to monitor humidity, temperature, and soil moisture, and decide when the irrigation system should operate.

Infrastructure, Information, Security and Safety. For example, real-time e-learning data collected via phones and gadgets can be applied in virtual classrooms [ 154 ]. Kinawy et al. [ 155 ] developed an online portal where the use of citizen profiles and knowledge items, such as tagging and comments on project websites were utilised. Similarly, business, parking, and tour information, i.e., users’ real-time scores and interaction, were shared with the people utilising e-government platforms and Mobile Apps at Petaling Jaya City Council and Putrajaya Corporation in Malaysia [ 156 ]. Recently, drones were also used for monitoring disasters, search and rescue tasks, surveillance and taking photographs with aerial views [ 157 ]. Shah et al. [ 106 ] developed a comprehensive disaster management model by which data can be collected from various sources such as Twitter datasets, weather sensors, surveillance sensors (e.g., CCTV cameras), pedestrian count, location, time, screen sensors for tracking vehicular traffic, and pollution and smoke sensors [ 106 ].

Transportation. Recently, Sato et al. [ 66 ] proposed a prototype that included a crowd road surface sensing system on a sensing vehicle on a winter road and a sensor server system using an Axis Mechanical sensor, GPS, temperature, humidity, quasi-electric sensor and infrared laser. This IoT-based server is connected to a communication server. Chakroun et al. [ 158 ] proposed a system to reduce delays during emergency traffic by focusing on the density of vehicles vs delays in alert dissemination. The project incorporated the Location-based Alert Messages Dissemination Scheme and the sensors that are provided in the vehicle cluster system look at the speed and flow of traffic using cameras. Ajay et al. [ 159 ] proposed smart management systems using IoT sensors such as CCTV, fuzzy logic, pedestrian sensors, and ultrasonic sensors. Li et al. [ 160 ] analysed data from 8,900 personal cars for three months in the city of Changsha, China from an IoT-based vehicle monitoring system. Toutouh and Alba [ 161 ] developed a data collection method using broadcasted beacon frequency (Hz) to neighbouring vehicles to maintain traffic safety, congestion control and efficiency.

Waste Management. Solid waste management with IoT was deployed using bin-level monitoring at home and public spaces using ultrasonic sensors and LoRaWAN networks [ 162 ]. Similarly, IoT-based sensors attached to bins included automatic open/close smart bin lids, filling level sensors, smart bin waste segregation, garbage collector alerts and ultra-sonic human detectors which helped arrange waste management systems and minimised delays in collecting bins when they are full [ 163 ]. Cerchecci et al. [ 164 ] proposed a prototype with an ultrasound distance sensor and a microcontroller for determining the level of bins and the count of changes in the bins.

Nonetheless, the process of data collection via IoT in the context of smart cities is being challenged by a number of factors (Table 7 ).

Based on the systematic review of surveyed articles, Table 7 illustrates data collection methods with a special focus on data collected from IoT devices, relevant human factors and other related systems. From the literature reviews in Table 7 , it was observed that there is an extensive range of data collection methods applied in IESCs. Despite the collaborations between IoT sensors and other types of data sources, there is still a lack of consistency when the human factor is involved due to the unpredictability of data and variables. The bulkiness of IoT devices may be reduced in the future with the advancement of technology. However, privacy, security and reliability of human data are the major apprehensions in the successful implementation of smart cities. Finally, Fig. 8 illustrates the main data collection approaches applicable in different sectors of smart cities.

IoT-based data collection approaches in various application sectors of smart cities

4 Challenges and recommendations

This review showed that IoT-based technologies have a critical role in realising smart cities. In the following sections, the key challenges and associated solutions are expanded by focusing on interconnectivity and integrating those challenges for more sustainable smart cities. From the critical analysis of the research themes, i.e. data analysis approaches, network and communication management and technologies, security and privacy management and data collection approach in IESCs, seven key challenges have been identified and these are elaborated below:

Energy consumption and environmental issues: IoT offers the possibility of collecting, analysing, and delivering massive amounts of data via advanced communication technologies. The big data received from IoT devices requires storage capacity, cloud computing, and wide bandwidth for data transmission [ 174 ]. However, the entire process of analysing and transmitting big data can be very energy-consuming. This is in addition to the amount of energy that sensing devices consume to continually remain operational. Therefore, there is a concern about the energy efficiency of IoT implementations in smart cities to meet specific SDGs such as Sustainable Cities and Communities (SDG11) and Responsible Consumption and Production (SDG12). This is compounded by issues associated with e-waste generation due to the booming trend in employing IoT in cities.

To address this challenge, the idea of green IoT has recently gained momentum. Green IoT is described as adopting energy-efficient measures to reduce energy consumption and GHG emissions caused by IoT systems in the built environment [ 175 , 176 ]. In this regard, studies suggested the use of green ICT technologies for green IoT, including the use of green RFID, green wireless sensor network, green cloud computing technology, green M2M, green data centre technology, green communication and networking, and green internet [ 174 , 175 , 176 ]. The use of drones to help with data transmission is another promising technology for improving energy efficiency in IoT systems. In principle, devices operating within IoT schemes consume high transmission power to transmit data over long distances. Drones can assist with this process by moving close to IoT devices, gathering data, analysing and processing the collected data, and sending it to those devices which are out of the coverage area [ 176 ].

Data analysis: It is evident that IoT analytics provides many benefits, however, these analytics share difficulties during the implementation, specifically in the form of technical challenges. Tibco [ 177 ] highlighted two types of challenges including features related to ascertaining time series with data structures and balancing speed and storage. This issue can affect diagnostic and predictive efforts. Alternatively, balancing speed with storage and scaling the process up, especially in the case of time-sensitive data is considered a challenge when historical data is necessary to make comparisons. Studies also underlined issues related to detecting anomalies with IoT data. These anomalies are considered serious complications in the upstream chain and the data ingestion process. As such, there is a need to manage a large amount of data by delivering timely and accurate feedback. Bellini et al. [ 178 ] suggested that a promising solution for anomaly detection is to examine IoT data through structure, movement, producer, stack faults, noise, outlier, conditional, typical trends, period, rate, and scalability. On the other hand, there are challenges associated with remote data processing that create issues in centralised computing systems due to high response time and connection loss [ 50 ]. For instance, Atitallah et al. [ 94 ] showed that false data injection can mislead the analytics processes. Such issues can subsequently lead to incorrect outcomes, guidance, and forecasts [ 179 ].

Privacy and security: The issues related to privacy and security are among the most daunting challenges for implementing IoT in smart cities. These issues are manifested in different layers of IoT architecture such as device level or communication level. At the device level of privacy, there is an issue of “inadequate authorisation and authentication”, “insecure software”, “firmware”, “web interface” and poor “transport layer encryption” [ 180 ]. To address this, security considerations should be improvised at different layers of IoT architecture to preclude security threats and attacks [ 181 ]. A number of protocols have been developed and implemented on different communication layers to ensure increasing security and privacy in IoT-based systems such as Secure Socket Layer (SSL) and Datagram Transport Layer Security (DTLS) [ 180 ]. However, the IoT communication layer is still open to threats imposed by malicious actions mainly due to employing wireless technologies within IoT systems. Therefore, there is an urgency to deploy methods for the detection of malicious activities and activating self-healing measures when threats are identified. Another issue associated with implementing IoT in the context of privacy is that users would feel secure once utilising services provided by IoT systems. Hence, recommendations point out the necessity of maintaining authorisation and authentication via secure networks which enable establishing safe communications between trusted parties [ 180 ].

Interoperability: Interoperability is widely regarded as a challenge in implementing IoT in smart cities. This term refers to the possibility for IoT devices and systems to readily communicate and exchange information with each other without using any particular middleware applications. The root of this issue stems from the heterogeneous nature of IoT systems in which various types of devices and technologies are often deployed for data collection purposes. The issue of interoperability may arise at four levels, including technical, semantic, syntactic, and organizational, as stated by van der Veer and Wiles [ 182 ]. Koo and Kim [ 183 ] presented five interoperability types, i.e., network, semantic, middleware, syntactic, and security, for which common security should be ensured for each other. Studies proposed solutions to facilitate interoperability in IoT systems such as “adapters/gateways-based solutions”, “virtual networks/ overlay-based solutions”, or “networking technologies” [ 184 ]. A study also proposed a hybrid solution, such as ‘Double Obfuscation Approach’, which is comprehensive and reliable in implementing IESCs [ 185 ]. Despite these efforts, interoperability still remains a challenge for the implementation of IoT in smart cities.

Ethical issues: Ethics in IoT applications in smart cities include issues related to social behaviour standards, encapsulating a wide range of challenges such as intellectual property rights, data accessibility, data sharing, and the use of data or information [ 186 ]. In a study, Allhoff and Henschke [ 187 ] discussed five fundamental issues associated with IoT applications in the context of smart cities, including informed consent, privacy, information security, physical safety, and trust. The study emphasised that these issues intersect in many ways, hence their impacts should be observed in connection with each other. While the ethical requirements vary between countries, Chang [ 188 ] proposed an ethical framework that can be used in six smart cities and explained how the framework can be used even in those countries with lower ethical requirements.

Scalability, adaptability, and reliability: IoT systems provide a large number of services and applications by connecting numerous devices. However, it becomes challenging to design a system that can constantly adapt to the changing needs of users. Scalability refers to the characteristics of a system for accepting the addition of new services, devices, and equipment to its configuration without suffering any interruption or degradation in performance [ 180 , 189 ]. The scalability characteristic can be vital in helping a system to be competitive, efficient, and capable of delivering sufficient quality of service. In this regard, one of the main challenges for the future development of IoT systems is to become scalable so that such systems can support the integration of a large number of devices with each other having different memory, processing, storage power, and bandwidth [ 180 ]. While Artificial Intelligence of Things (AIoT) Initiatives are being adopted to implement smart cities, they are usually ineffective due to a lack of preparedness, resources and capabilities [ 190 ]. This study proposed three emerging themes, i.e. proof-of-value, treating and managing data as a key asset and comprehensive commitments, that should be taken into consideration in cities to reduce the challenges of scalability issues.

IoT and future development of cities: There is a necessity to develop a vision at the government and policy level for incorporating IoT infrastructures when planning for the future development of cities. IoT is fundamental to the realisation of fully functioning smart cities, thus it is important that future urban development plans would encompass smart features (e.g., smart grids, connected homes, telematics, etc.) as a measure to facilitate IoT implementations. Javed et al. [ 73 ] emphasised examining smart cities as an integrated network of interconnected systems rather than isolated entities. Since IoT technologies are emerging and many concepts such as climate resilience, net-zero city, climate-intelligent cities, and digital circular economy are continuously influencing the future development of cities at policy levels, the lack of IoT integration within these concepts could have a social and economic impact, including unintended consequences following the adoption of efficiency-improving measures [ 191 , 192 ].

The literature review of the above seven challenges also indicated the interconnectivity between them which have direct and/or indirect influence on each other. The recommendations that emerged from the literature reviews are also potential solutions for more than one challenge and have a rebound effect. Figure 9 illustrates the interconnected nature of the challenges and solutions in implementing IESC. Figure 9 reveals that recommended solutions may help to mitigate more than one interconnected challenge, however, most challenges also depend on how IESCs would fit and integrate with future technologies.

Interconnectivity between challenges and potential solutions in implementing IESCs

By summarising the seven key challenges attained using hybrid systematic analysis, several aspects are identified in line with SDGs in terms of Industry Innovation and Infrastructure (SDG9), Sustainable Cities and Communities (SDG11) and Responsible Consumption and Production (SDG12) that can be listed into specific points. (1) The complexity of IESCs reveals limitations with performance across management, implementation and operation at different levels and scales, offering new opportunities to explore this field in-depth. (2) The projected deployment of 50 billion IoT devices around the world by 2030 [ 20 ] and the continuous growth of smart cities [ 21 ] would impose a devastating environmental impact in terms of e-waste. Hence, efficient implementations regarding circular economy are needed within the boundary of IESCs developments. (3) Data transmission and data storage from IoT devices require considerable electrical power that intensifies energy loads in smart cities. Several solutions were proposed to overcome this issue, but further research is needed to limit this demand to achieve net zero by 2050. (4) Handling and analysing IoT data in smart cities encounter challenges in terms of speed, connection, process, anomalies and forecast that pose a need to improve the reliability of computing in selected IESCs infrastructure to ensure delivering smooth and efficient actions. (5) Data privacy and security in wireless environments are still developing. Protections from any external threats or breaches require further research and exploration to maintain secure authorization and authentication in IESCs. (6) Unclarity or immaturity of standards and guides towards intellectual property rights, data accessibility, data sharing, and the use of data or information are the key challenges in terms of ethical use. Further investigations are needed to enhance transparency in developing frameworks. (7) Exchanging data in different devices and environments without using proper intermediate layers could lead to issues in terms of interoperability as data moves through different levels. Thus, further studies should examine these aspects in detail. (8) Integrating IoT solutions in smart cities features issues in terms of scalability and adaptability to meet users’ needs that are always changing based on the context. Thus, advanced studies to explore the reliability of modifications are required. (9) Several IESCs concepts have been proposed to achieve functionality, however, environmental aspects in terms of future climate resilience were found to be neglected. As a result, accelerating research in this direction is a future demand. (10) Exploring and developing IESCs requires comprehensible vision and policies as stated in several guides to achieve optimum implementation, such as Smart Readiness Indicator and IoT Readiness Level Index, however, further explorations should be extended in providing proper regulations for different industries and experts in this field, especially in the built environment [ 5 ].

5 Conclusions

This study has adopted a hybrid literature review technique to identify and critically analyse hot research topics in the field of IESCs. To this end, 843 documents were retrieved from the WoS database and analysed with reflection on the defined objectives. The results of keywords’ co-occurrence analysis in combination with text-mining analysis identified four main areas of IESCs research, including (i) data analysis, (ii) network and communication management and technologies, (iii) security and privacy management, and (iv) data collection.

From bibliometric analysis and text-mining analysis, the publication trend has shown a consistent upward trajectory in recent years, with 53% of all materials (i.e., 445) published between 2020 and 2022. Considering this steady rise in publications across these periods, alongside the escalating interest in ICT-based technologies, the interest in IESCs is expected to continue growing in the foreseeable future. The findings also revealed that a total of 127 journals collectively published 843 articles spanning the years 2010 to 2022. The top ten prominent journals that have significantly contributed to the advancement of the field, accounting for nearly 51% of the published materials.

The study examined actionable platforms to enhance the data stream, data management, control data anomaly and improve data analytics. The content analysis of these research areas showed that most data collected via IoT devices is unstructured. Thus, data analytics techniques are required for deployment to process unstructured data for IESCs. Data in IESCs undergoes different complexities that are undertaken based on specific values. The assessment revealed several limitations with IoT data speed, size, accuracy, response and security. It was found that studies are still investigating numerous methods based on the level of processing. The review found that many studies are still exploring and experimenting with different algorithmic techniques for new applications to obtain effective solutions. Finally, achieving integration between IoT and big data analytics shows a promising future but requires further research and investigation, especially in designing smart cities.

The study found that IoT network management shows no available detailed or comprehensive overview of existing resource-constrained network solutions. The study found that IoT network management needs to incorporate efficient management processes for handling a large number of devices, vast amounts of data, and diverse services with varying requirements. In addition, there are some limitations in terms of developing an effective solution for managing IoT networks that can be challenging due to the inherent constraints of IoT networks. Finally, further studies are required to provide efficient solutions to manage IESCs with low-power networks to handle heterogeneity while ensuring security and privacy and allowing for scalable resource utilisation.

The assessment of security and privacy management in IESCs revealed issues at different levels of smart cities’ architecture due to the nature of devices being deployed in cities, which are often resource-constrained, thus making cities vulnerable to security attacks. The study identified new technologies that offer high levels of security and privacy, however, they require significant computational resources, which can be a challenge in IESCs with resource-constrained. The study found that areas of predictive computation, security in fog computing, security in smart grid networks and access control in edge computing have specific challenges in addressing particular security and privacy in emerging technologies and infrastructures. Finally, the survey found the need for context-specific solutions to balance the trade-offs between security, privacy, efficiency, and scalability in resource-constrained smart environments in IESCs.

Data collection in IESCs demonstrated challenges that may vary depending on the smart data applications, data scale and the sector subject to data collection. The study identified 9 areas for data collection, including Energy Conservation, Urban Environment, Health & Wellbeing, Biodiversity, Surveillance/Security & Safety, Transportation & Mobility, Infrastructure & Communication, Tourism and Waste Management. Also, it was observed that there is an extensive range of data collection methods applied in IESCs. Despite the collaborations between IoT sensors and other types of data sources, there is still a lack of consistency when the human factor is involved due to the unpredictability of data and variables.

The review singled out seven main challenges associated with the implementation of IoT in smart cities for future research. These include energy consumption and environmental issues, data analysis, privacy and security, interoperability, ethical issues, scalability, adaptability and reliability and incorporation of IoT systems into future development plans of cities. The review also revealed some recommendations for those interconnected challenges in implementing IESCs, where most of those issues rely on future/emerging technology and effective integrations within policies to support environmental agendas such as circular economy, climate resilience and net-zero futures.

Data availability

No data was used for the research described in the article.

Ismagilova E, Hughes L, Dwivedi YK, Raman KR. Smart cities: advances in research—an information systems perspective. Int J Inf Manage. 2019;1(47):88–100.

Article Google Scholar

Alavi AH, Jiao P, Buttlar WG, Lajnef N. Internet of things-enabled smart cities: state-of-the-art and future trends. Measurement. 2018;1(129):589–606.

Article ADS Google Scholar

Bellini P, Nesi P, Pantaleo G. IoT-enabled smart cities: a review of concepts, frameworks and key technologies. Appl Sci. 2022;12(3):1607.

Article CAS Google Scholar

The United Nations. A review of world population. Department of Economic and Social Affairs. 2022. https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html . Accessed 24 Sept 2022.

Al-Obaidi KM, Hossain M, Alduais NA, Al-Duais HS, Omrany H, Ghaffarianhoseini A. A review of using IoT for energy efficient buildings and cities: a built environment perspective. Energies. 2022;15(16):5991.

Kondepudi SN, Ramanarayanan V, Jain A, Singh GN, Nitin Agarwal NK, Kumar R, Gemma P. Smart Sustainable Cities: an Analysis of Definitions; the ITU-T Focus Group for Smart Sustainable Cities. International Telecommunication Union (ITU): Geneva, Switzerland. 2014.

Bauer M, Sanchez L, Song J. IoT-enabled smart cities: evolution and outlook. Sensors. 2021;21(13):4511.

Article ADS PubMed PubMed Central Google Scholar

Jia M, Komeily A, Wang Y, Srinivasan RS. Adopting Internet of Things for the development of smart buildings: a review of enabling technologies and applications. Autom Constr. 2019;1(101):111–26.

Talari S, Shafie-Khah M, Siano P, Loia V, Tommasetti A, Catalão JP. A review of smart cities based on the internet of things concept. Energies. 2017;10(4):421.

Amaxilatis D, Boldt D, Choque J, Diez L, Gandrille E, Kartakis S, Mylonas G, Vestergaard LS. Advancing experimentation-as-a-service through urban IoT experiments. IEEE Internet Things J. 2018;6(2):2563–72.

Sanchez L, Muñoz L, Galache JA, Sotres P, Santana JR, Gutierrez V, Ramdhany R, Gluhak A, Krco S, Theodoridis E, Pfisterer D. SmartSantander: IoT experimentation over a smart city testbed. Comput Netw. 2014;14(61):217–38.

Sotres P, Santana JR, Sánchez L, Lanza J, Muñoz L. Practical lessons from the deployment and management of a smart city internet-of-things infrastructure: the smartsantander testbed case. IEEE Access. 2017;5(5):14309–22.

City of Darwin. Switching on Darwin. 2022. https://www.darwin.nt.gov.au/transforming-darwin/innovation/switching-on-darwin . Accessed 27 Sept 2022.

SGIM. How a Smart City Tackles Rainfall-Chicago SGIM. 2022. https://www.iotnewsportal.com/cities/how-a-smart-city-tackles-rainfall-chicago-sgim . Accessed 19 Feb 2024.

Putrajaya. Putrajaya as a smart city. 2022. https://smart.putrajaya.my/blueprint/ . Accessed 27 Sept 2022.

Dimmer. DIMMER FP7 project. 2017. http://dimmer.polito.it . Accessed 27 Sept 2022.

INTUBE. Intelligent Use of Buildings' Energy Information. 2008. https://cordis.europa.eu/project/id/224286 . Accessed 27 Sept 2022.

IoT-Analytics. Global IoT market size to grow 19% in 2023—IoT shows resilience despite economic downturn. https://iot-analytics.com/iot-market-size/ . Accessed 26 Feb 2024

Allied Market Research. Smart Cities Market Size, Share, Competitive Landscape and Trend Analysis Report by Component (Hardware, Software, and Service) and Functional Area (Smart Infrastructure, Smart Governance and Smart Education, Smart Energy, Smart Mobility, Smart Healthcare, Smart Buildings, and Others): Global Opportunity Analysis and Industry Forecast, 2021–2030. https://www.alliedmarketresearch.com/smart-cities-market . Accessed 26 Feb 2024

Statista, Number of connected devices worldwide 2030, Statista, 2020. https://www.statista.com/statistics/802690/worldwide-connected-devices-by-accesstechnology/ . Accessed 26 Feb 2024

IMD World Competitive Centre. IMD Smart City Index Report. https://www.imd.org/wp-content/uploads/2023/04/smartcityindex-2023-v7.pdf . Accessed 26 Feb 2024

Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M. Internet of things for smart cities. IEEE Internet Things J. 2014;1(1):22–32.

Botta A, De Donato W, Persico V, Pescapé A. Integration of cloud computing and internet of things: a survey. Futur Gener Comput Syst. 2016;1(56):684–700.

Lin J, Yu W, Zhang N, Yang X, Zhang H, Zhao W. A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 2017;4(5):1125–42.

Arasteh H, Hosseinnezhad V, Loia V, Tommasetti A, Troisi O, Shafie-khah M, Siano P. Iot-based smart cities: A survey. In2016 IEEE 16th international conference on environment and electrical engineering (EEEIC) 2016 (pp. 1–6). IEEE.

Nitulescu M, Jawad YK. Smart city and internet of things technologies. In2021 25th International Conference on System Theory, Control and Computing (ICSTCC) 2021 (pp. 606–611). IEEE.

Janani RP, Renuka K, Aruna A. IoT in smart cities: a contemporary survey. Global Transit Proceed. 2021;2(2):187–93.

Shahrour I, Xie X. Role of Internet of Things (IoT) and crowdsourcing in smart city projects. Smart Cities. 2021;4(4):1276–92.

Belli L, Cilfone A, Davoli L, Ferrari G, Adorni P, Di Nocera F, Dall’Olio A, Pellegrini C, Mordacci M, Bertolotti E. IoT-enabled smart sustainable cities: challenges and approaches. Smart Cities. 2020;3(3):1039–71.

Omrany H, Chang R, Soebarto V, Zhang Y, Ghaffarianhoseini A, Zuo J. A bibliometric review of net zero energy building research 1995–2022. Energy Build. 2022;1(262): 111996.

Sharifi A. Urban sustainability assessment: an overview and bibliometric analysis. Ecol Ind. 2021;1(121): 107102.

Clarivate analysis. Web of Science. 2022. https://clarivate.com/webofsciencegroup/solutions/web-of-science/ . Accessed 19 Feb 2024.

Niñerola A, Sánchez-Rebull MV, Hernández-Lara AB. Six sigma literature: a bibliometric analysis. Total Qual Manag Bus Excell. 2021;32(9–10):959–80.

Olawumi TO, Chan DW. A scientometric review of global research on sustainability and sustainable development. J Clean Prod. 2018;10(183):231–50.

Guo YM, Huang ZL, Guo J, Li H, Guo XR, Nkeli MJ. Bibliometric analysis on smart cities research. Sustainability. 2019;11(13):3606.

Oladinrin OT, Arif M, Rana MQ, Gyoh L. Interrelations between construction ethics and innovation: a bibliometric analysis using VOSviewer. Constr Innov. 2023;23(3):505–23.

Wu H, Zuo J, Zillante G, Wang J, Yuan H. Construction and demolition waste research: a bibliometric analysis. Archit Sci Rev. 2019;62(4):354–65.

Van Eck NJ, Waltman L. VOSviewer Manual. 2020. https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.15.pdf . Accessed 19 Feb 2024.

Ranjbari M, Saidani M, Esfandabadi ZS, Peng W, Lam SS, Aghbashlo M, Quatraro F, Tabatabaei M. Two decades of research on waste management in the circular economy: insights from bibliometric, text mining, and content analyses. J Clean Prod. 2021;10(314): 128009.

Jung H, Lee BG. Research trends in text mining: semantic network and main path analysis of selected journals. Expert Syst Appl. 2020;30(162): 113851.

González-Zamar MD, Abad-Segura E, Vázquez-Cano E, López-Meneses E. IoT technology applications-based smart cities: research analysis. Electronics. 2020;9(8):1246.

Caragliu A, Del Bo CF. Smart innovative cities: the impact of smart city policies on urban innovation. Technol Forecast Soc Chang. 2019;1(142):373–83.

Razmjoo A, Østergaard PA, Denai M, Nezhad MM, Mirjalili S. Effective policies to overcome barriers in the development of smart cities. Energy Res Soc Sci. 2021;1(79): 102175.

Yan L, Shi Y, Wei M, Wu Y. Multi-feature fusing local directional ternary pattern for facial expressions signal recognition based on video communication system. Alex Eng J. 2023;1(63):307–20.

Yang L, Shami A. IoT data analytics in dynamic environments: from an automated machine learning perspective. Eng Appl Artif Intell. 2022;1(116): 105366.

Kutty AA, Wakjira TG, Kucukvar M, Abdella GM, Onat NC. Urban resilience and livability performance of European smart cities: a novel machine learning approach. J Clean Prod. 2022;10(378): 134203.

Choi J. Enablers and inhibitors of smart city service adoption: a dual-factor approach based on the technology acceptance model. Telematics Inform. 2022;1(75): 101911.

Xiaoyi Z, Dongling W, Yuming Z, Manokaran KB, Antony AB. IoT driven framework based efficient green energy management in smart cities using multi-objective distributed dispatching algorithm. Environ Impact Assess Rev. 2021;1(88): 106567.

Sun M, Wu F, Ng CT, Cheng TC. Effects of imperfect IoT-enabled diagnostics on maintenance services: a system design perspective. Comput Ind Eng. 2021;1(153): 107096.

Gomes MM, da Rosa RR, da Costa CA, Griebler D. Simplifying IoT data stream enrichment and analytics in the edge. Comput Electr Eng. 2021;1(92): 107110.

Yin C, Zhang S, Wang J, Xiong NN. Anomaly detection based on convolutional recurrent autoencoder for IoT time series. IEEE Transact Syst Man Cyber Syst. 2020;52(1):112–22.

Jhingta P, Vasudeva A, Sood M. Applicability of communication technologies in internet of things: a review. InInternational Conference on Innovative Computing and Communications: Proceedings of ICICC 2022, Volume 3 2022 (pp. 249–264). Singapore: Springer Nature Singapore.

Nashiruddin MI, Nugraha MA. Long range wide area network deployment for smart metering infrastructure in urban area: case study of Bandung City. In2021 4th International Conference on Information and Communications Technology (ICOIACT) 2021 (pp. 221–226). IEEE.

Purnama AA, Nashiruddin MI, Murti MA. Feasibility Study of The IoT-Connectivity Deployment for AMI Service: A Case Study in Surabaya City. In2020 IEEE International Conference on Communication, Networks and Satellite (Comnetsat) 2020 Dec 17 (pp. 61–66). IEEE.

Fraile LP, Tsampas S, Mylonas G, Amaxilatis D. A comparative study of LoRa and IEEE 802.15. 4-based IoT deployments inside school buildings. IEEE Access. 2020;8:160957–81.

Cedillo-Elias EJ, Larios VM, Orizaga-Trejo JA, Lomas-Moreno CE, Ramirez JR, Maciel R. A Cloud Platform for Smart Government Services, using SDN networks: the case of study at Jalisco State in Mexico. In2019 IEEE International Smart Cities Conference (ISC2) 2019 (pp. 372–377). IEEE.

Yaqoob I, Hashem IA, Mehmood Y, Gani A, Mokhtar S, Guizani S. Enabling communication technologies for smart cities. IEEE Commun Mag. 2017;55(1):112–20.

Raza S, Misra P, He Z, Voigt T. Building the Internet of Things with bluetooth smart. Ad Hoc Netw. 2017;15(57):19–31.

Bohli JM, Langendörfer P, Skarmeta AF. Security and privacy challenge in data aggregation for the iot in smart cities. InInternet of Things 2022 Sep 1 (pp. 225–244). River Publishers.

Zhang H, Babar M, Tariq MU, Jan MA, Menon VG, Li X. SafeCity: toward safe and secured data management design for IoT-enabled smart city planning. IEEE Access. 2020;6(8):145256–67.

Waheed N, He X, Ikram M, Usman M, Hashmi SS, Usman M. Security and privacy in IoT using machine learning and blockchain: Threats and countermeasures. ACM Comput Surveys. 2020;53(6):1–37.

Rahman MA, Asyhari AT, Leong LS, Satrya GB, Tao MH, Zolkipli MF. Scalable machine learning-based intrusion detection system for IoT-enabled smart cities. Sustain Cities Soc. 2020;1(61): 102324.

Hernandez-Ramos JL, Martinez JA, Savarino V, Angelini M, Napolitano V, Skarmeta AF, Baldini G. Security and privacy in internet of things-enabled smart cities: challenges and future directions. IEEE Secur Priv. 2020;19(1):12–23.

Atlam HF, Wills GB. IoT security, privacy, safety and ethics. Digital twin technologies and smart cities. 2020:123–49.

Al-Turjman F, Lemayian JP. Intelligence, security, and vehicular sensor networks in internet of things (IoT)-enabled smart-cities: an overview. Comput Electr Eng. 2020;1(87): 106776.

Sato G, Sakuraba A, Uchida N, Shibata Y. A new road state information platform based on crowed sensing on challenged network environments. Internet of Things. 2022;1(18): 100214.

Menon VG, Jacob S, Joseph S, Sehdev P, Khosravi MR, Al-Turjman F. An IoT-enabled intelligent automobile system for smart cities. Internet of Things. 2022;1(18): 100213.

Kusumastuti RD, Nurmala N, Rouli J, Herdiansyah H. Analyzing the factors that influence the seeking and sharing of information on the smart city digital platform: empirical evidence from Indonesia. Technol Soc. 2022;1(68): 101876.

Aljoufie M, Tiwari A. Citizen sensors for smart city planning and traffic management: crowdsourcing geospatial data through smartphones in Jeddah. Saudi Arabia GeoJournal. 2022;87(4):3149–68.

Wang S, Liu X, Liu S, Muhammad K, Heidari AA, Del Ser J, de Albuquerque VH. Human short long-term cognitive memory mechanism for visual monitoring in IoT-assisted smart cities. IEEE Internet Things J. 2021;9(10):7128–39.

Tian J, Gao L. Using data monitoring algorithms to physiological indicators in motion based on Internet of Things in smart city. Sustain Cities Soc. 2021;1(67): 102727.

Dhungana D, Engelbrecht G, Parreira JX, Schuster A, Tobler R, Valerio D. Data-driven ecosystems in smart cities: A living example from Seestadt Aspern. In2016 IEEE 3rd World Forum on Internet of Things (WF-IoT) 2016 (pp. 82–87). IEEE.

Javed AR, Shahzad F, UrRehman S, Zikria YB, Razzak I, Jalil Z, Xu G. Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects. Cities. 2022;129:103794.

Yassine A, Singh S, Hossain MS, Muhammad G. IoT big data analytics for smart homes with fog and cloud computing. Futur Gener Comput Syst. 2019;1(91):563–73.

Azad P, Navimipour NJ, Rahmani AM, Sharifi A. The role of structured and unstructured data managing mechanisms in the Internet of things. Clust Comput. 2020;23:1185–98.

Atitallah SB, Driss M, Ghzela HB. Microservices for data analytics in IoT applications: current solutions, open challenges, and future research directions. Procedia Comput Sci. 2022;1(207):3938–47.

Al-Obaidi KM, Al-Duais HS, Alduais NA, Alashwal A, Ismail MA. Exploring the environmental performance of liquid glass coating using Sol-Gel technology and responsive Venetian blinds in the tropics. J Build Eng. 2022;15(62): 105329.

Marjani M, Nasaruddin F, Gani A, Karim A, Hashem IA, Siddiqa A, Yaqoob I. Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access. 2017;5:5247–61.

TechTarget. Internet of things (IoT). 2018. https://www.techtarget.com/ . Accessed 16 Feb 2024.

Minteer A. Analytics for the internet of things (iot). Packt Publishing Ltd; 2017.

Akbar A, Khan A, Carrez F, Moessner K. Predictive analytics for complex IoT data streams. IEEE Internet Things J. 2017;4(5):1571–82.

Chen M, Herrera F, Hwang K. Cognitive computing: architecture, technologies and intelligent applications. IEEE Access. 2018;15(6):19774–83.

Sidorov V, Ng WK. Transparent data encryption for data-in-use and data-at-rest in a cloud-based database-as-a-service solution. In2015 IEEE world congress on services 2015 (pp. 221–228). IEEE.

Ali U, Calis C. Centralized smart governance framework based on iot smart city using ttg-classified technique. In2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT) 2019 (pp. 157–160). IEEE.

Mohammadi M, Al-Fuqaha A, Sorour S, Guizani M. Deep learning for IoT big data and streaming analytics: a survey. IEEE Communicat Surveys Tutor. 2018;20(4):2923–60.

Yang L, Shami A. A lightweight concept drift detection and adaptation framework for IoT data streams. IEEE Int Things Magazine. 2021;4(2):96–101.

L’heureux A, Grolinger K, Elyamany HF, Capretz MA. Machine learning with big data: challenges and approaches. IEEE Access. 2017;5:7776–97.

Maciel BI, Hidalgo JI, de Barros RS. An ultimately simple concept drift detector for data streams. In2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2021 (pp. 625–630). IEEE.

Natarajan P, Frenzel JC, Smaltz DH. Demystifying big data and machine learning for healthcare. CRC Press; 2017.

Saheb T, Izadi L. Paradigm of IoT big data analytics in the healthcare industry: a review of scientific literature and mapping of research trends. Telematics Inform. 2019;1(41):70–85.

Leeflang PS, Wieringa JE, Bijmolt TH, Pauwels KH, editors. Advanced methods for modeling markets. New York City: Springer; 2017.

Google Scholar

Li H, Ota K, Dong M. Learning IoT in edge: deep learning for the internet of things with edge computing. IEEE Network. 2018;32(1):96–101.

Deng L. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transact Signal Informat Process. 2014;3: e2.

Atitallah SB, Driss M, Boulila W, Ghézala HB. Leveraging Deep Learning and IoT big data analytics to support the smart cities development: review and future directions. Comput Sci Rev. 2020;1(38): 100303.

Daissaoui A, Boulmakoul A, Karim L, Lbath A. IoT and big data analytics for smart buildings: a survey. Procedia Comput Sci. 2020;1(170):161–8.

Stoppel CM, Leite F. Integrating probabilistic methods for describing occupant presence with building energy simulation models. Energy Build. 2014;1(68):99–107.

Chen Z, Xu J, Soh YC. Modeling regular occupancy in commercial buildings using stochastic models. Energy Build. 2015;15(103):216–23.

Akbar A, Nati M, Carrez F, Moessner K. Contextual occupancy detection for smart office by pattern recognition of electricity consumption data. In2015 IEEE international conference on communications (ICC) 2015 (pp. 561–566). IEEE.

D’Oca S, Hong T. Occupancy schedules learning process through a data mining framework. Energy Build. 2015;1(88):395–408.

Hong HJ, Tsai PH, Cheng AC, Uddin MY, Venkatasubramanian N, Hsu CH. Supporting internet-of-things analytics in a fog computing platform. In2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) 2017 (pp. 138–145). IEEE.

Yang S. IoT stream processing and analytics in the fog. IEEE Commun Mag. 2017;55(8):21–7.

Rahman MA, Hossain MS, Hassanain E, Muhammad G. Semantic multimedia fog computing and IoT environment: sustainability perspective. IEEE Commun Mag. 2018;56(5):80–7.

Portelli K, Anagnostopoulos C. Leveraging edge computing through collaborative machine learning. In2017 5th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW) 2017 Aug 21 (pp. 164–169). IEEE.

Lujic I, De Maio V, Brandic I. Adaptive recovery of incomplete datasets for edge analytics. In2018 IEEE 2nd international conference on fog and edge computing (ICFEC) 2018 (pp. 1–10). IEEE.

Ali MI, Patel P, Breslin JG. Middleware for real-time event detection and predictive analytics in smart manufacturing. In 2019 15th international conference on distributed computing in sensor systems (DCOSS) 2019 (pp. 370–376). IEEE.

Shah SA, Seker DZ, Rathore MM, Hameed S, Yahia SB, Draheim D. Towards disaster resilient smart cities: can internet of things and big data analytics be the game changers? IEEE Access. 2019;11(7):91885–903.

Srinidhi NN, Kumar SD, Venugopal KR. Network optimizations in the internet of things: a review. Eng Sci Techn Int J. 2019;22(1):1–21.

Aboubakar M, Kellil M, Roux P. A review of IoT network management: current status and perspectives. J King Saud Univer Comput Informat Sci. 2022;34(7):4163–76.

Sikimić M, Amović M, Vujović V, Suknović B, Manjak D. An overview of wireless technologies for IoT network. In2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH) 2020 (pp. 1–6). IEEE.

Lombardi M, Pascale F, Santaniello D. Internet of things: a general overview between architectures, protocols and applications. Information. 2021;12(2):87.

Lonzetta AM, Cope P, Campbell J, Mohd BJ, Hayajneh T. Security vulnerabilities in Bluetooth technology as used in IoT. J Sens Actuator Netw. 2018;7(3):28.

VenkataLakshmi Y, Singh P. UWB localization procedures with range control methods—a review. Adv Signal Process Commun Eng Select Proceed ICASPACE. 2022;2021(2):295–316.

Group, I. W. Layer MP. Part 15.4: low-rate wireless personal area networks (LR-WPANs). IEEE Std, 2011, 802, 4–2011.

Abied SR, Shams AB, Kawser MT. Comparison of the LTE performance parameters in different environments under close loop spatial multiplexing (CLSM) mode in downlink LTE-A. J Comput Commun. 2017;5(09):117.

Rost P, Bernardos CJ, De Domenico A, Di Girolamo M, Lalam M, Maeder A, Sabella D, Wübben D. Cloud technologies for flexible 5G radio access networks. IEEE Commun Mag. 2014;52(5):68–76.

Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M. Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Communicat Surveys Tutor. 2015;17(4):2347–76.

Cho HH, Lai CF, Shih TK, Chao HC. Integration of SDR and SDN for 5G. Ieee Access. 2014;11(2):1196–204.

Santos MA, Nunes BA, Obraczka K, Turletti T, De Oliveira BT, Margi CB. Decentralizing SDN's control plane. In39th Annual IEEE conference on local computer networks 2014 (pp. 402–405). IEEE.

Palattella MR, Dohler M, Grieco A, Rizzo G, Torsner J, Engel T, Ladid L. Internet of things in the 5G era: enablers, architecture, and business models. IEEE J Sel Areas Commun. 2016;34(3):510–27.

Bizanis N, Kuipers FA. SDN and virtualization solutions for the internet of things: a survey. IEEE Access. 2016;9(4):5591–606.

Sood K, Yu S, Xiang Y. Software-defined wireless networking opportunities and challenges for internet-of-things: a review. IEEE Internet Things J. 2015;3(4):453–63.

Aslam MS, Khan A, Atif A, Hassan SA, Mahmood A, Qureshi HK, Gidlund M. Exploring multi-hop LoRa for green smart cities. IEEE Network. 2019;34(2):225–31.

Ndiaye M, Hancke GP, Abu-Mahfouz AM. Software defined networking for improved wireless sensor network management: a survey. Sensors. 2017;17(5):1031.

Al-Turjman F, Zahmatkesh H, Shahroze R. An overview of security and privacy in smart cities’ IoT communications. Transact Emerg Telecommun Technol. 2022;33(3): e3677.

Prabavathy S, Sundarakantham K, Shalinie SM. Design of cognitive fog computing for intrusion detection in internet of things. J Commun Netw. 2018;20(3):291–8.

Butpheng C, Yeh KH, Xiong H. Security and privacy in IoT-cloud-based e-health systems—a comprehensive review. Symmetry. 2020;12(7):1191.

Babun L, Denney K, Celik ZB, McDaniel P, Uluagac AS. A survey on IoT platforms: communication, security, and privacy perspectives. Comput Netw. 2021;19(192): 108040.

Chatterjee S. The safety of IoT-enabled system in smart cities of India: do ethics matter? Int J Ethics Syst. 2020;36(4):601–18.

Anajemba JH, Tang Y, Iwendi C, Ohwoekevwo A, Srivastava G, Jo O. Realizing efficient security and privacy in IoT networks. Sensors. 2020;20(9):2609.

Arunkumar JR, Velmurugan S, Chinnaiah B, Charulatha G, Prabhu MR, Chakkaravarthy AP. Logistic Regression with Elliptical Curve Cryptography to Establish Secure IoT. Comput Syst Sci Eng. 2023;46(1).

Deebak BD, Memon FH, Dev K, Khowaja SA, Wang W, Qureshi NM. TAB-SAPP: a trust-aware blockchain-based seamless authentication for massive IoT-enabled industrial applications. IEEE Trans Industr Inf. 2022;19(1):243–50.

Haseeb K, Saba T, Rehman A, Ahmed Z, Song HH, Wang HH. Trust management with fault-tolerant supervised routing for smart cities using internet of things. IEEE Internet Things J. 2022;9(22):22608–17.

Deebak BD, Memon FH, Cheng X, Dev K, Hu J, Khowaja SA, Qureshi NM, Choi KH. Seamless privacy-preservation and authentication framework for IoT-enabled smart eHealth systems. Sustain Cities Soc. 2022;1(80): 103661.

Zhao K, Wang XA, Yang B, Tian Y, Zhang J. A privacy preserving homomorphic computing toolkit for predictive computation. Inf Process Manage. 2022;59(2): 102880.

Guo Y, Zhang Z, Guo Y. SecFHome: secure remote authentication in fog-enabled smart home environment. Comput Netw. 2022;22(207): 108818.

Chaudhry SA, Alhakami H, Baz A, Al-Turjman F. Securing demand response management: a certificate-based access control in smart grid edge computing infrastructure. IEEE Access. 2020;20(8):101235–43.

Haseeb K, Ud Din I, Almogren A, Islam N. An energy efficient and secure IoT-based WSN framework: an application to smart agriculture. Sensors. 2020;20(7):2081.

Bilal M, Usmani RS, Tayyab M, Mahmoud AA, Abdalla RM, Marjani M, Pillai TR, Targio Hashem IA. Smart cities data: framework, applications, and challenges. Handbook Smart Cities. 2020:1–29.

Gray M, Kovacova M. Internet of Things sensors and digital urban governance in data-driven smart sustainable cities. Geo Hist Inte Relat. 2021;13(2):107–20.

Li W, Batty M, Goodchild MF. Real-time GIS for smart cities. Int J Geogr Inf Sci. 2020;34(2):311–24.

Ahuja K, Khosla A. Data analytics criteria of IoT enabled smart energy meters (SEMs) in smart cities. Int J Energy Sect Manage. 2019;13(2):402–23.

Cipollina A, Di Silvestre ML, Giacalone F, Micale GM, Sanseverino ER, Sangiorgio R, Tran QT, Vaccaro V, Zizzo G. A methodology for assessing the impact of salinity gradient power generation in urban contexts. Sustain Cities Soc. 2018;1(38):158–73.

Jiang D, Zhu W, Muthu B, Seetharam TG. Importance of implementing smart renewable energy system using heuristic neural decision support system. Sustainable Energy Technol Assess. 2021;1(45): 101185.

Abu-Rayash A, Dincer I. Development and analysis of an integrated solar energy system for smart cities. Sust Energy Technol Assess. 2021;1(46): 101170.

Yu A, Zhang P, Rudkin S. Simultaneous action or protection after production? Decision making based on a chance-constrained approach by measuring environmental performance considering PM2. 5. Soc Eco Plann Sci. 2022;80: 101147.

Hu Z, Bai Z, Yang Y, Zheng Z, Bian K, Song L. UAV aided aerial-ground IoT for air quality sensing in smart city: architecture, technologies, and implementation. IEEE Network. 2019;33(2):14–22.

Toma C, Alexandru A, Popa M, Zamfiroiu A. IoT solution for smart cities’ pollution monitoring and the security challenges. Sensors. 2019;19(15):3401.

Segura-Garcia J, Calero JM, Pastor-Aparicio A, Marco-Alaez R, Felici-Castell S, Wang Q. 5G IoT system for real-time psycho-acoustic soundscape monitoring in smart cities with dynamic computational offloading to the edge. IEEE Internet Things J. 2021;8(15):12467–75.

Dembski F, Wössner U, Letzgus M, Ruddat M, Yamu C. Urban digital twins for smart cities and citizens: the case study of Herrenberg, Germany. Sustainability. 2020;12(6):2307.

Chen Y, Han D. Water quality monitoring in smart city: a pilot project. Autom Constr. 2018;1(89):307–16.

Jha S, Nkenyereye L, Joshi GP, Yang E. Mitigating and monitoring smart city using internet of things. Comput Mater Contin. 2020;65(2):1059–79.

Gallacher S, Wilson D, Fairbrass A, Turmukhambetov D, Firman M, Kreitmayer S, Mac Aodha O, Brostow G, Jones K. Shazam for bats: Internet of Things for continuous real-time biodiversity monitoring. IET Smart Cities. 2021;3(3):171–83.

Podder AK, Al Bukhari A, Islam S, Mia S, Mohammed MA, Kumar NM, Cengiz K, Abdulkareem KH. IoT based smart agrotech system for verification of Urban farming parameters. Microprocess Microsyst. 2021;1(82): 104025.

Setiawan R, Devadass MM, Rajan R, Sharma DK, Singh NP, Amarendra K, Ganga RK, Manoharan RR, Subramaniyaswamy V, Sengan S. IoT based virtual E-learning system for sustainable development of smart cities. J Grid Comput. 2022;20(3):24.

Kinawy SN, El-Diraby TE, Konomi H. Customizing information delivery to project stakeholders in the smart city. Sustain Cities Soc. 2018;1(38):286–300.

Lim SB, Yigitcanlar T. Participatory governance of Smart cities: Insights from e-participation of Putrajaya and Petaling Jaya. Malaysia Smart Cities. 2022;5(1):71–89.

Yazdinejad A, Parizi RM, Dehghantanha A, Karimipour H, Srivastava G, Aledhari M. Enabling drones in the internet of things with decentralized blockchain-based security. IEEE Internet Things J. 2020;8(8):6406–15.

Chakroun R, Abdellatif S, Villemur T. LAMD: location-based alert message dissemination scheme for emerging infrastructure-based vehicular networks. Int Things. 2022;1(19): 100510.

Ajay P, Nagaraj B, Pillai BM, Suthakorn J, Bradha M. Intelligent ecofriendly transport management system based on iot in urban areas. Environ Dev Sustain. 2022;4:1–8.

Li H, Liu Y, Qin Z, Rong H, Liu Q. A large-scale urban vehicular network framework for IoT in smart cities. IEEE Access. 2019;28(7):74437–49.

Toutouh J, Alba E. A swarm algorithm for collaborative traffic in vehicular networks. Vehicular Commun. 2018;1(12):127–37.

Vishnu S, Ramson SJ, Senith S, Anagnostopoulos T, Abu-Mahfouz AM, Fan X, Srinivasan S, Kirubaraj AA. IoT-Enabled solid waste management in smart cities. Smart Cities. 2021;4(3):1004–17.

Ashwin M, Alqahtani AS, Mubarakali A. Iot based intelligent route selection of wastage segregation for smart cities using solar energy. Sustain Energy Technol Assess. 2021;1(46): 101281.

Cerchecci M, Luti F, Mecocci A, Parrino S, Peruzzi G, Pozzebon A. A low power IoT sensor node architecture for waste management within smart cities context. Sensors. 2018;18(4):1282.

Huang CJ, Kuo PH. A deep CNN-LSTM model for particulate matter (PM2. 5) forecasting in smart cities. Sensors. 2018;18(7):2220.

Islam MM, Rahaman A, Islam MR. Development of smart healthcare monitoring system in IoT environment. SN computer science. 2020;1:1–1.

Mutanu L, Gupta K, Gohil J. Leveraging IoT solutions for enhanced health information exchange. Technol Soc. 2022;1(68): 101882.

Trencher G, Karvonen A. Stretching “smart”: Advancing health and well-being through the smart city agenda. InSmart and Sustainable Cities? 2020 (pp. 54–71). Routledge.

Abd El-Latif AA, Abd-El-Atty B, Mehmood I, Muhammad K, Venegas-Andraca SE, Peng J. Quantum-inspired blockchain-based cybersecurity: securing smart edge utilities in IoT-based smart cities. Inf Process Manage. 2021;58(4): 102549.

Park MS, Lee H. Smart city crime prevention services: the incheon free economic zone case. Sustainability. 2020;12(14):5658.

Aloqaily M, Otoum S, Al Ridhawi I, Jararweh Y. An intrusion detection system for connected vehicles in smart cities. Ad Hoc Netw. 2019;1(90): 101842.

Wang W, Kumar N, Chen J, Gong Z, Kong X, Wei W, Gao H. Realizing the potential of the internet of things for smart tourism with 5G and AI. IEEE Network. 2020;34(6):295–301.

Srikantha N, Moinuddin K, Lokesh KS, Narayana A. Waste management in IoT-enabled smart cities: a survey. Int J Eng Comput Sci. 2017;6(6):2319–7242.

Almalki FA, Alsamhi SH, Sahal R, Hassan J, Hawbani A, Rajput NS, Saif A, Morgan J, Breslin J. Green IoT for eco-friendly and sustainable smart cities: future directions and opportunities. Mobile Netw Appl. 2023;28(1):178–202.

Arshad R, Zahoor S, Shah MA, Wahid A, Yu H. Green IoT: an investigation on energy saving practices for 2020 and beyond. Ieee Access. 2017;31(5):15667–81.

Alsamhi SH, Ma O, Ansari MS, Meng Q. Greening internet of things for greener and smarter cities: a survey and future prospects. Telecommun Syst. 2019;72:609–32.

TIBCO. What is IoT Analytics?. 2022. tibco.com/reference-center/what-is-iot-analytics#:~:text=Internet%20of%20Things%20(IoT)%20analytics,with%20Industrial%20IoT%20(IIoT). Accessed 20 Dec 2022.

Bellini P, Cenni D, Nesi P, Soderi M. Anomaly detection on IoT data for smart city. In2020 IEEE International Conference on Smart Computing (SMARTCOMP) 2020 (pp. 416–421). IEEE.

Bostami B, Ahmed M, Choudhury S. False data injection attacks in internet of things. Performability in internet of things. 2019:47–58.

Kumar S, Tiwari P, Zymbler M. Internet of Things is a revolutionary approach for future technology enhancement: a review. J Big Data. 2019;6(1):1–21.

Yan Z, Zhang P, Vasilakos AV. A survey on trust management for internet of things. J Netw Comput Appl. 2014;1(42):120–34.

Van Der Veer H, Wiles A. Achieving technical interoperability. European telecommunications standards institute. 2008.

Koo J, Kim YG. Interoperability requirements for a smart city. InProceedings of the 36th Annual ACM Symposium on Applied Computing 2021 (pp. 690–698).

Noura M, Atiquzzaman M, Gaedke M. Interoperability in internet of things: taxonomies and open challenges. Mobile Netw Appl. 2019;15(24):796–809.

Albouq SS, Abi Sen AA, Almashf N, Yamin M, Alshanqiti A, Bahbouh NM. A survey of interoperability challenges and solutions for dealing with them in IoT environment. IEEE Access. 2022;25(10):36416–28.

AboBakr A, Azer MA. IoT ethics challenges and legal issues. In2017 12th International Conference on Computer Engineering and Systems (ICCES) 2017 (pp. 233–237). IEEE.

Allhoff F, Henschke A. The internet of things: foundational ethical issues. Internet of Things. 2018;1(1):55–66.

Chang V. An ethical framework for big data and smart cities. Technol Forecast Soc Chang. 2021;1(165): 120559.

Gupta A, Christie R, Manjula R. Scalability in internet of things: features, techniques and research challenges. Int J Comput Intell Res. 2017;13(7):1617–27.

Kuguoglu BK, van der Voort H, Janssen M. The giant leap for smart cities: scaling up smart city artificial intelligence of things (AIOT) initiatives. Sustainability. 2021;13(21):12295.

Li X, Bao J, Sun J, Wang J. Development of circular economy in smart cities based on FPGA and wireless sensors. Microprocess Microsyst. 2021;1(80): 103600.

Pee LG, Pan SL. Climate-intelligent cities and resilient urbanisation: challenges and opportunities for information research. Int J Inf Manage. 2022;1(63): 102446.

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Omrany, H., Al-Obaidi, K.M., Hossain, M. et al. IoT-enabled smart cities: a hybrid systematic analysis of key research areas, challenges, and recommendations for future direction. Discov Cities 1 , 2 (2024). https://doi.org/10.1007/s44327-024-00002-w

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Top 20 Internet of Things (IoT) Journals – 2024

IoT Journals List 2024

Top 20 iot journals 2024, top iot journals, 1. ieee – internet of things journal, 2. elsevier – internet of things, 3. inderscience – international journal of internet of things and cyber-assurance.

4. IEEE – Wireless Communications
5. IEEE – Transactions on Wireless Communications

6. Springer – Wireless Networks (SpringerNature)

7. wiley – information systems journal, 8. igi global – international journal of hyperconnectivity and the internet of things (ijhiot), 9. igi global – protocols and applications for the industrial internet of things.

10. MDPI – Sensors — Open Access Journal

The Internet of Things(IoT) is an emerging technology, which got huge attention among new business model creators and researchers. As part of ilovephd ‘s research, future technology, and innovative idea recommendation, this article finds out the top 10 SCI-indexed Internet of Things Journals with a high impact factor to pursue ongoing groundbreaking research.

IEEE Internet of Things Journal
IEEE Transactions on Industrial Informatics
IEEE Transactions on Cognitive Communications and Networking
IEEE Transactions on Network and Service Management
IEEE Transactions on Mobile Computing
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IEEE Internet of Things (IoT) Journal publishes articles on the latest advances and review articles on the various aspects of IoT. Topics include IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Examples are IoT demands, impacts, and implications on sensor technologies, big data management, and future Internet design for various IoT use cases, such as smart cities, smart environments, smart homes, etc.

The fields of interest include IoT architecture such as things-centric, data-centric, and service-oriented IoT architectures, IoT enabling technologies and systems integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments, IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.

Internet of Things; Engineering Cyber-Physical Human Systems is a comprehensive journal encouraging cross-collaboration between researchers, engineers, and practitioners in the field of IoT and cyber-physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.

The Internet of Things must address the reliable and timely delivery of information, regardless of cyber threats, using secure automatic processes over distributed and heterogeneous computing systems.

IJITCA provides an embedded security, information assurance, and cyber-security research perspective, illustrating how cyber-assurance must integrate with IoT devices and networks to understand how these individual components interact in ways to resist and avoid intentional attempts to compromise normal operations. IJITCA addresses the protection of IoT networks from mobile devices to complex processing systems.

4. IEEE – Wireless Communications

IEEE Wireless Communications is designed for audiences working in wireless communications and networking communities. It covers technical, policy, and standard issues relating to wireless communications in all media (and combinations of media), and at all protocol layers.

All wireless/mobile communications, networking, computing, and services will be covered. Each issue of this interdisciplinary magazine provides tutorial articles of high quality and depth concerning the revolutionary technological advances in wireless/mobile communications, networking, and computing.

5. IEEE – Transactions on Wireless Communications

The IEEE Transactions on Wireless Communications is a major archival journal that is committed to the timely publication of very high-quality, peer-reviewed, original papers that advance the theory and applications of wireless communication systems and networks.

The wireless communication revolution is bringing fundamental changes to data networking, and telecommunication, making integrated networks a reality.

By freeing the user from the cord, personal communications networks, wireless LANs, mobile radio networks, and cellular systems, harbor the promise of fully distributed mobile computing and communications, anytime, anywhere.

The Information Systems Journal (ISJ) is an international journal promoting the study of, and interest in, information systems. Articles are welcome on research, practice, experience, current issues, and debates.

The ISJ encourages submissions that reflect the wide and interdisciplinary nature of the subject and articles that integrate technological disciplines with social, contextual, and management issues, based on research using appropriate research methods.

The International Journal of Hyperconnectivity and the Internet of Things (IJHIoT) promotes innovative, interesting, and rigorously developed conceptual and empirical contributions and encourages theory-based multi- or inter-disciplinary research.

This journal covers topics relating to IoT and the current age of hyperconnectivity including security concerns, applications of IoT, development, and management of the IoT, wearable computing, IoT for home automation, smart cities, and other environments.

The Internet of Things (IoT) has become a major influence on developing new technologies and innovations. When utilized properly, these applications can enhance business functions and make them easier to perform. Protocols and Applications for the Industrial Internet of Things discuss and address the difficulties, challenges, and applications of IoT in industrial processes and production and work life.

Featuring coverage on a broad range of topics such as industrial process control, machine learning, and data mining, this book is geared toward academicians, computer engineers, students, researchers, and professionals seeking current and relevant research on applications of the IoT.

10. MDPI – Sensors — Open Access Journal

“Sensors” is the leading international peer-reviewed open-access journal on the science and technology of sensors and biosensors. Sensors are published monthly online by MDPI.

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Published: 25 March 2024

An IoT-based low-cost architecture for smart libraries using SDN

Hui Xu 1 , 2 , 3 ,
Wei-dong Liu 2 ,
Lu Li 2 &
Qi Zhou 4

Scientific Reports volume 14 , Article number: 7022 ( 2024 ) Cite this article

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Computational science
Computer science
Information technology

In the evolving landscape of smart libraries, this research pioneers an IoT-based low-cost architecture utilizing Software-Defined Networking (SDN). The increasing demand for more efficient and economical solutions in library management, particularly in the realm of RFID-based processes such as authentication, property circulation, and book loans, underscores the significance of this study. Leveraging the collaborative potential of IoT and SDN technologies, our proposed system introduces a fresh perspective to tackle these challenges and advance intelligent library management. In response to the evolving landscape of smart libraries, our research presents an Internet of Things (IoT)-based low-cost architecture utilizing SDN. The exploration of this architectural paradigm arises from a recognized gap in the existing literature, pointing towards the necessity for more efficient and cost-effective solutions in managing library processes. Our proposed algorithm integrates IoT and SDN technologies to intelligently oversee various library activities, specifically targeting RFID-based processes such as authentication, property circulation management, and book loan management. The system's architecture, encompasses components like the data center, SDN controllers, RFID tags, tag readers, and other network sensors. By leveraging the synergy between RFID and SDN, our innovative approach reduces the need for constant operator supervision in libraries. The scalability and software-oriented nature of the architecture cater to extensive library environments. Our study includes a two-phase investigation, combining practical implementation in a small-scale library with a simulation environment using MATLAB 2021. This research not only fills a crucial gap in current knowledge but also lays the foundation for future advancements in the integration of IoT and SDN technologies for intelligent library management.

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Yan Song, Wenjing Luo, … Xiangbo Qi

A data plane security model of segmented routing based on SDP trust enhancement architecture

Liang Wang, Hailong Ma, … Tao Hu

Introduction

The importance of an IoT-based low-cost architecture lies in its potential to democratize and extend the benefits of the IoT to a broader range of applications and users 1 , 2 . By focusing on cost-effectiveness, such architectures enable the deployment of IoT solutions in diverse settings, including resource-constrained environments and developing regions. This affordability facilitates widespread adoption in sectors such as agriculture, healthcare, smart cities, and industrial automation, where cost-efficient connectivity and data exchange are paramount 3 . The low-cost IoT architecture can empower businesses, communities, and individuals to harness the transformative capabilities of IoT technology, fostering innovation, efficiency, and improved quality of life across various domains 4 .

Smart libraries play a crucial role in modernizing and enhancing traditional library services through the integration of advanced technologies. The importance of smart libraries lies in their ability to leverage innovations such as IoT, artificial intelligence, and data analytics to create more efficient and user-centric library experiences 5 . These technologies facilitate tasks like automated cataloging, personalized recommendations, and real-time tracking of library resources 6 . Smart libraries enhance accessibility to information, offering users a seamless and interactive environment for research and learning. Additionally, they contribute to the preservation of valuable resources by employing digital archiving and conservation methods 7 . The application of smart libraries spans across educational institutions, research facilities, and public spaces, promoting the evolution of libraries into dynamic hubs that adapt to contemporary information needs and technological advancements 8 .

SDN holds paramount importance in revolutionizing traditional network architectures by providing a more flexible, scalable, and programmable approach to network management. By decoupling the control plane from the data plane, SDN allows for centralized control, enabling administrators to dynamically allocate network resources and implement changes efficiently 9 . The significance of SDN lies in its ability to streamline network provisioning, enhance scalability, and simplify network management, leading to improved operational efficiency 10 . SDN finds applications across various sectors, including data centers, telecommunications, and enterprise networks. In data centers, SDN facilitates the orchestration of resources and ensures optimal traffic flow, contributing to better overall performance. In telecommunications, SDN enables the creation of agile and programmable networks, paving the way for innovations like 5G 11 . Enterprises benefit from SDN by achieving greater control over their network infrastructure, supporting dynamic business requirements, and enhancing security through centralized policy management. Overall, SDN plays a pivotal role in reshaping network architectures to meet the demands of modern, dynamic, and data-intensive applications 12 .

The IoT-based low-cost architecture for smart libraries utilizing SDN is of significant importance in transforming traditional libraries into intelligent, connected spaces 13 . This innovative approach leverages the IoT to enhance library services, improve resource management, and provide an enriched experience for users. By integrating sensors, RFID technology, and other IoT devices, the system enables real-time monitoring of book availability, user preferences, and environmental conditions within the library 14 . SDN comes into play by offering a centralized and programmable network infrastructure, allowing efficient communication and control of diverse IoT devices 4 . This architecture not only optimizes resource allocation and energy efficiency but also enhances security through centralized monitoring. Applications of this IoT-based low-cost architecture for smart libraries are diverse, including smart inventory management, personalized user services, and data-driven decision-making for library administrators 15 . Overall, this integrated approach transforms traditional libraries into dynamic, responsive, and technology-driven hubs that cater to the evolving needs of library users in the digital age 16 .

An IoT-based low-cost architecture is a technological framework designed to integrate the IoT into a system or environment with a primary focus on cost-effectiveness 17 . This architecture utilizes IoT devices, which are interconnected physical devices equipped with sensors, actuators, and communication modules, to efficiently collect and exchange data 18 . Emphasizing affordability, it enables the deployment of IoT applications in various scenarios with budget constraints, optimizing resource utilization and ensuring efficient connectivity among IoT devices. The goal is to bring the benefits of IoT, such as real-time data monitoring and enhanced automation, to environments where cost efficiency is paramount. Smart libraries, on the other hand, are modernized library systems incorporating advanced technologies like IoT, artificial intelligence, and data analytics to streamline operations and services 19 . These libraries use automation for tasks such as cataloging, check-in/check-out, and inventory management, enhancing efficiency and accuracy. Featuring digitized collections, interactive learning spaces, and personalized services, smart libraries create an intelligent and user-centric environment, adapting to the evolving needs of patrons in the digital age 20 .

SDN, or Software-Defined Networking, is a revolutionary networking paradigm that revolutionizes traditional network architectures by separating the control plane from the data plane 21 . In SDN, network intelligence and decision-making are centralized through a software-based controller, offering programmability, flexibility, and dynamic management of network resources. This decoupling enables efficient network configuration, monitoring, and optimization through software applications, fostering automation and agility in response to changing network conditions 22 . The goal is to simplify network management, enhance scalability, and expedite the deployment of new services. When applied to an IoT-based low-cost architecture for smart libraries, SDN becomes instrumental in creating intelligent and affordable library systems 23 . By integrating IoT devices like sensors and smart devices into the library environment, data collection and diverse applications are enabled. The emphasis on being low-cost ensures accessibility for libraries facing resource constraints, with SDN providing a programmable and centralized control plane for dynamic adaptation to changing requirements. This integrated approach enhances library services, facilitates real-time monitoring, and supports innovative applications, ultimately establishing a smart and interconnected library ecosystem 24 .

Our research paper significantly contributes to the field of smart libraries by introducing a groundbreaking IoT-based low-cost architecture. Unlike prior studies that have primarily focused on IoT-based low-cost architecture, our work explores a novel approach by integrating IoT and SDN technologies to optimize library activities. The key contribution lies in adapting the synergy of IoT and SDN, originally designed for smart libraries. Our proposed algorithm, leveraging RFID-based processes and SDN-based network configuration, provides a unique and efficient strategy for enhancing library intelligence. The integration of RFID technology for authentication, property circulation management, and library book loan management, along with SDN's streamlined communication infrastructure, forms a pioneering architecture. This novel framework not only reduces the need for constant operator supervision in libraries but also presents a scalable and software-oriented solution. The hierarchical tree topology, formed through our clustering algorithm, further enhances communication efficiency, offering a valuable and innovative perspective for future developments in IoT-based library systems.

Innovative IoT-Based Architecture: Introduces a groundbreaking low-cost architecture integrating IoT and SDN technologies.

Comprehensive RFID-Based Processes: Proposes an algorithm for authentication, property circulation management, and library book loan management using RFID.

Efficient SDN-Based Network Configuration: Presents a novel approach to SDN-based network configuration and routing.

Reduced Operator Dependency: Emphasizes the reduction of constant operator supervision in overseeing library processes.

Cost-Effective Strategy: Highlights the cost-effectiveness of the proposed architecture.

Hierarchical Tree Topology: Introduces a hierarchical tree topology through a clustering algorithm.

Scalability and Adaptability: Offers a scalable and adaptable framework for resource management.

Future Research Directions: Identifies prospective tasks for future investigations in the integration of IoT and SDN technologies.

Related works

Considering related works in the field of an IoT-based low-cost architecture for smart libraries using SDN is crucial for several reasons. Firstly, it allows researchers and practitioners to build upon existing knowledge and identify gaps in current solutions, ensuring that the proposed architecture addresses specific challenges in the context of smart libraries. Secondly, a review of related works facilitates the incorporation of successful strategies and lessons learned from prior implementations, enhancing the likelihood of success and efficiency in the development of the proposed architecture. Lastly, a comprehensive understanding of the existing literature helps in creating a more innovative and contextually relevant solution, positioning the IoT-based architecture within the broader landscape of smart libraries and SDN applications. In this regard, Also, Sankar, Ramasubbareddy 25 introduced a routing protocol, CT-RPL, designed specifically for the IoT applications. The protocol is based on a cluster tree structure, aiming to optimize energy efficiency and maximize the overall network lifetime. By organizing nodes into clusters and establishing a hierarchical tree topology, CT-RPL efficiently manages communication and routing within the IoT network. The key focus of the protocol is on prolonging the lifespan of IoT devices by minimizing energy consumption, thus addressing a critical challenge in IoT deployments. The paper provided insights into the design, implementation, and performance evaluation of CT-RPL as a promising solution for enhancing the longevity of IoT networks.

Luo 6 presented an approach to enhancing the security of IoT environments. The proposed system leveraged SDN to create a distributed intrusion detection system tailored for IoT devices. By employing optimized forests, the system aimed to efficiently detect and mitigate potential security threats across a decentralized network. The utilization of SDN provided a centralized control plane, allowing for dynamic and programmable management of network resources, which is particularly advantageous for securing diverse and interconnected IoT ecosystems. The paper contributed to the field by addressing the unique security challenges posed by IoT devices through a distributed SDN-based approach with optimized forests for effective intrusion detection.

In addition, Gupta, Juneja 26 proposed a method to address the challenges of network resource management in the context of 5G-enabled IoT applications for smart healthcare. The authors introduced an intelligent technique that aims to optimize the allocation and utilization of network resources to enhance the performance and efficiency of healthcare applications. The study involved the integration of artificial intelligence or machine learning methods to analyze and dynamically manage network resources, ensuring the seamless operation of 5G-IoT applications in the healthcare domain. The proposed approach contributed valuable insights into improving the reliability and responsiveness of smart healthcare systems, fostering advancements in the integration of 5G and IoT technologies for healthcare applications.

As well, Bhuiyan, Billah 27 focused on the development of a practical and applicable model for healthcare monitoring in both rural and urban settings using the IoT. The proposed system leveraged IoT technologies to create a ubiquitous healthcare monitoring infrastructure, ensuring that individuals in diverse geographical areas can access healthcare services seamlessly. The model considered the specific challenges and requirements of both rural and urban environments, aiming to provide an inclusive and effective healthcare monitoring solution. This paper contributed to the advancement of IoT-based healthcare systems by offering a feasible and adaptable model tailored to the unique characteristics of different regions.

Besides, de Melo, Miani 28 addressed the critical issue of securing home networks through the introduction of the FamilyGuard security architecture. The proposed model focused on anomaly detection within home environments, aiming to safeguard the interconnected devices and systems commonly found in modern households. The authors presented a comprehensive approach to network security, utilizing anomaly detection techniques to identify potential threats or irregularities. By addressing the unique challenges posed by home networks, FamilyGuard offered a tailored and effective security solution to protect the privacy and integrity of users' connected devices within a domestic setting.

Additionally, Elhoseny, Siraj 29 concentrated on addressing the challenges of energy efficiency and security in IoT applications. The authors proposed a mobile agent-based protocol designed to enhance the sustainability of IoT systems. The protocol aimed to optimize energy consumption in IoT devices while ensuring robust security measures. By employing mobile agents that can autonomously move between devices to perform specific tasks, the protocol seeks to minimize energy usage, prolong device lifespan, and enhance overall system efficiency. Additionally, the authors emphasized the importance of securing IoT applications against potential threats, contributing to the development of sustainable and secure IoT solutions. Table 1 indicated specification of investigated related works.

As well, Chiliquinga, Manzano 22 explored an approach for monitoring IoT networks by leveraging SDN and efficient traffic signatures. The proposed method aimed to address the challenges associated with the growing complexity and diversity of IoT devices and their communication patterns. By incorporating SDN, the network monitoring process becomes more flexible and dynamic, allowing for adaptive responses to emerging threats or changes in network behavior. Additionally, the use of cost-effective traffic signatures helped in efficiently identifying and analyzing IoT-related traffic, optimizing resource utilization. The paper emphasized a progressive monitoring strategy that evolves with the evolving landscape of IoT networks, enhancing overall network security and performance.

Also, Njah, Pham 30 proposed an innovative approach to flow management in a smart digital campus using SDN. The proposed scheme focused on efficiently managing network flows by considering both service requirements and available resources. By leveraging the programmability and central control offered by SDN, the system aimed to dynamically adapt to the varying demands of services within a digital campus. This approach enhanced the overall performance and resource utilization of the network infrastructure, ensuring that services in the smart campus environment are delivered optimally. The paper contributed to the advancement of SDN-based solutions tailored for complex digital campus scenarios, emphasizing the importance of balancing service requirements and resource management for effective flow control.

Furthermore, Gordon, Batula 31 presented an innovative approach to enhancing the security of smart homes. The authors proposed a solution that integrates SDN with low-cost traffic classification techniques. This combination allowed for dynamic and programmable control over the home network, enabling adaptive responses to security threats. The use of low-cost traffic classification enhanced the efficiency of identifying and managing different types of network traffic associated with smart home devices. By leveraging SDN's capabilities and cost-effective traffic classification, the paper contributed to the development of practical and accessible security measures tailored for the unique challenges presented by smart home environments, ultimately aiming to provide homeowners with a more resilient and responsive defense against potential cybersecurity threats.

Moreover, Ganesan, Hwang 32 presented an approach to network traffic classification in a SDN-enabled Fiber-Wireless-Internet of Things (FiWi-IoT) smart environment. The study employed supervised machine learning (ML) models to classify network traffic efficiently. By integrating SDN with FiWi-IoT infrastructure, the proposed method allowed for centralized and programmable control, enhancing adaptability to dynamic network conditions. The application of supervised ML models provided an intelligent mechanism for accurately categorizing diverse traffic types within the smart environment. This approach contributed to improved network management, ensuring that the FiWi-IoT network is optimized for various traffic patterns and enhancing the overall efficiency and responsiveness of the smart environment.

And, Wang and Wang 33 explored a strategic approach to mitigating Distributed Denial-of-Service (DDoS) attacks within SDN environments. The study focused on developing an efficient and cost-effective defense mechanism against DDoS threats, leveraging the programmability and centralized control capabilities inherent in SDN. By employing intelligent traffic monitoring and analysis, the proposed method identified and mitigated malicious traffic patterns, thereby enhancing the network's resilience to DDoS attacks. The emphasis on efficiency and low-cost solutions is crucial for making the defense mechanisms accessible and practical, addressing the economic considerations associated with implementing robust security measures in SDN-based networks.

Additionally, Abid, Afaqui 34 explored the transformative journey of the IoT towards becoming smarter and more adaptive through the integration of SDN principles. The study delved into the evolution of IoT technologies, emphasizing the need for a software-defined approach to address the increasing complexity, heterogeneity, and dynamic nature of IoT environments. By adopting SDN, the proposed evolution sought to enhance the intelligence and flexibility of IoT networks, allowing for efficient resource management, dynamic adaptation to changing conditions, and improved overall performance. The paper discussed key concepts and challenges associated with this evolution, providing insights into the future of smart and software-defined IoT systems.

Proposed algorithm

The proposed method for imbuing libraries with intelligence leverages the synergy of IoT and SDN technologies. This architecture demonstrates the capability to intelligently oversee a diverse array of library activities, thereby mitigating the requirement for constant operator supervision. The suggested framework incorporates SDN to streamline the intricacies associated with resource management in IoT, offering an efficient and cost-effective strategy. The supported activities within libraries employing this architecture include:

1. RFID-based processes encompass:

Authentication

Property circulation management

Library book loan management

2. Data exchange based on IoT SDN

Thus, the entire intelligent processes in the library can be categorized into two groups based on RFID and SDN, depending on the platform used. In the proposed architecture, authentication, property circulation management, and book lending processes utilize RFID technology and operate on the IoT communication platform. Conversely, the information exchange mechanism relies on SDN architecture to furnish an efficient communication platform in smart libraries. Figure 1 illustrates the proposed architecture, which, based on the amalgamation of Internet of Things and software-oriented network technologies, can be segmented into the following fundamental components:

Data center

SDN controllers

RFID tags and tag readers, along with other network sensors

A view of the proposed smart library model.

The architecture presented in Fig. 1 offers the potential to modernize key processes in traditional libraries. In this proposed architecture, intelligent library information management relies on a data center, serving as the hub for organizing crucial data for intelligent library management. The data center accommodates key information in the following tables:

Books information

Member information

Book loan information

The table for book information encompasses key attributes associated with each book in the library. Within this table, access to individual books is facilitated through a master key. This unique key, recorded in the book information table, is also stored in the RFID tag affixed to the corresponding book. Consequently, by scanning the RFID tag and extracting its unique code, the book's information can be retrieved via the data center. Similarly, the member information table comprises distinct characteristics of each library member, with each member differentiated by a unique membership ID. This unique code is stored in the RFID tag on the member's card, enabling the retrieval of all personal information through tag access. The book loan information table serves as a communication interface between the aforementioned tables, managing transactions related to book entries and exits. Each entry in this table describes the loan or return of a book by a member, utilizing foreign keys corresponding to the unique identifiers of the book and the member. Additionally, it includes time information regarding the book's loan and return. Two additional tables are employed to oversee book reservation processes and library property management. These tables mirror the structure of the book loan information table, storing information on book reservations or transfers of library property by members. It is evident that components such as book and member RFID tags, the data center, and RFID tag reader nodes play essential roles in processes related to member authentication, book loans, and returns.

Conducting the mentioned processes intelligently, especially in extensive libraries, necessitates the utilization of an efficient communication infrastructure. The proposed method addresses this requirement through the integration of IoT and SDN, as illustrated in Fig. 1 . The suggested architecture categorizes the array of smart library sensors using SDN technology, organizing each group of adjacent sensors into a subnet. Controller nodes, supervising each sub-network, govern the data exchange between the sensors and the data center within the smart library. This communication mechanism, coupled with RFID technology, offers a solution to concerns related to the model's cost and scalability in expansive environments. The subsequent section provides a detailed description of each process outlined in the proposed smart library model.

RFID-based processes in the proposed architecture

As mentioned, RFID-based processes encompass a range of functions associated with member authentication, property movement control, and library book loan management. In all these processes, three key components—data center, RFID tag, and tag reader—collaborate. Figure 2 illustrates the mechanism of RFID-based processes within the proposed system architecture.

The mechanism of RFID-based processes in the proposed system architecture.

As illustrated in Fig. 2 , the architecture of this system comprises four components: tag, tag reader, antenna, and computer network. In the proposed model, passive tags are utilized to minimize the overall implementation cost of the system. This choice is based on the suitable distance of 5–10 m supported by passive tags for effective identification. To detect the presence of a person, a sensor and an activating switch are employed in the identity verification station. When an individual enters the identity verification station, the switch within the sensor triggers the tag reader. The tag reader component is positioned adjacent to the sensor. Consequently, upon switch activation, the tag reader component transmits a coded signal to the tags affixed to the user's membership card, books, or property. Each tag component then emits its unique identification code as a radio signal to the reader.

Upon receiving the identification codes, this information is transmitted to the computer system linked to the tag reader. Subsequently, the computer system relays this data to the data center via the network. The data center securely stores the encrypted IDs of all members, assets, and books in the library. The received identifier undergoes a search operation within the data center through the tag reader. If the desired identifier is located in the database, a confirmation message is dispatched to the sender's computer system, signaling successful verification. Conversely, if the identifier is deemed invalid, an error message is generated. Upon successful identity verification, the data center records the transaction time and other pertinent details. Notably, the search and validation processes are carried out independently for each member and book/property component. To achieve this, the unique membership ID (Tagcard in Fig. 2 ) is sought in the member information table, while the book/property's unique ID (Tagbook/Tagproperty) is searched in the books/property information table. The corresponding flowchart depicting these processes is presented in Fig. 3 .

Flowchart of RFID-based processes in the proposed architecture.

The electronic tags employed to identify library members, books, and assets in the proposed model represent an automated data carrier utilizing RFID technology. The system's architecture adopts passive RFID tags operating in the UHF band, with a reading range spanning 6–10 m. Figure 4 illustrates the structure of these tags. Passive tags, as mentioned, lack an independent energy source for transmitting identifiers to the reader component. Instead, they harness energy from radio frequency pulses dispatched by the reader component. Upon receiving these pulses, the tag charges its internal capacitor, utilizing it as an energy supply source. Subsequently, the tag transmits its information to the reader component through internal antennas. It's noteworthy that the identifiers stored in these specialized electronic tags boast resistance to copying or alteration. The extended lifespan and cost-effectiveness of RFID technology contribute to its advantageous use in creating intelligent libraries.

UFH band passive tag structure used in the proposed architecture.

SDN-based network configuration and routing

The proposed architecture for smart libraries adopts a scalable and software-oriented network-based mechanism to establish the data exchange pattern among network components. This entails defining the necessary mechanisms for constructing topology and data routing based on that topology—an essential process in the network operations of IoT-based architectures. The topology construction involves creating a network communication infrastructure using a subset of stable communication links. Simultaneously, the routing process dictates how data is exchanged between network sensors (such as members' smartphones, tag readers, and controllers) and the data center, leveraging the established topology. This facilitates data routing for activities like book search, reservation, identity authentication, and other smart library functions between diverse network components. The proposed routing algorithm, detailed in the subsequent section, is a multi-step strategy rooted in the SDN architecture. This algorithm determines optimal routes for data transmission based on a cost function.

Construction of topology

The configuration process of the proposed intelligent library architecture commences with the construction and control of the network topology, utilizing a software-based network. In this method, network nodes initially exchange positional information and then establish the clustering structure based on SDN principles. Notably, as a substantial portion of network sensors consists of members' smartphones, all of which are mobile, considering the movement characteristics of these objects during the topology construction process becomes paramount. Therefore, the topology construction process begins by calculating the relative speed between the network nodes. To assess the relative speed of two nodes, the first step involves estimating the distance to the node. In this proposed method, it is assumed that the distance between two nodes can be estimated based on the received signal strength of each node, calculated using the provided equation.

In the given equation, 'd' denotes the distance between two nodes, and 'u' is an independent random parameter following a Gaussian distribution with a zero mean. Additionally, 'K1' and 'K2' represent the path loss parameters in the 802.11 standard. As expressed in Eq. ( 1 ), the distance between two nodes can be approximated through the received signal strength. This relationship can be estimated as described below, without taking the environmental noise factor into account:

In the next step of the proposed method, we calculate the relative speed and communication stability. Let's consider two nodes, A and B, at a distance 'd' from each other. These nodes move in a straight line with a speed 'v', and each node has a movement angle 'θ'. Therefore, the velocity vector for nodes A and B will be denoted as $\overline{{V }_{A}}=\left({v}_{A}, {\theta }_{A}\right)$ and $\overline{{V }_{B}}=({v}_{B}, {\theta }_{B})$ . We assume that the radio range of nodes is 'R', and.

d < R. The relative velocity vector of the two nodes can be calculated using the following equation:

In the above relationship, θ_BA is the relative angle of two nodes A and B. also:

Using relations ( 4 ) and ( 5 ), it can be shown that the duration of two nodes being neighbors will be equal to:

Using the above relationship, it is possible to predict whether two nodes A and B will be neighbors after the time interval tΔ or not? This will happen if T_neighbor > ∆t. In this case, the similarity of the movement pattern of two users A and B will be stored in a matrix as follows:

In the above relationship, loc_a^current is the current position of node A as (x_a,y_a) and loc_a^future is the predicted position for node A after the time interval tΔ and is calculated as follows:

In the above relationship, ${V}_{{x}_{a}}$ represents the speed of the node along the x-axis, and ${V}_{{y}_{a}}$ is its speed along the y-axis. By calculating the value of T ab for each pair of nodes in the network, a portion of the similarity matrix is formed. This matrix contains the degree of similarity in the movement patterns of each pair of nodes. All nodes transmit their matrix portions to the data center node, contributing to the construction of the topology based on the clustering structure. The central node responsible for clustering the moving nodes integrates these ${T}_{ab}$ matrix parts and categorizes nodes into clusters using two fundamental rules. In this method, nodes with similar movement patterns are grouped into a cluster. To assess the similarity of the movement pattern between two nodes, the following conditions are checked:

The term "two nodes should be in the same radio range (both nodes have direct one-step access to each other)" refers to the requirement that two nodes should be within each other's direct radio communication range, enabling a one-step direct connection.

Additionally, it should be anticipated that after a period of time Δt, the distance between the two nodes does not exceed a predefined threshold distance.

The second condition involves predicting the persistent position of the connection between two nodes, and based on these criteria, the user's movement pattern information is stored in a matrix, denoted as T. Following these rules, the steps for clustering network nodes by the central node are as follows:

Input: List of users L and connection period matrix T

Output: C network clusters

Repeat the following steps until a node is in the L list.

Pick a random node x from list L, remove it from L, and create a new cluster in C.

For each node $y\in L$ : if y is a neighbor of x and based on the matrix T, ${T}_{x,y}\ge \Delta t$ , then add y to the current cluster in clustering C and omit node y from the list L.

Go to step 1.

After this process, all network nodes will be grouped into clusters based on their movement patterns, and each cluster will be assigned to the nearest SDN controller node in the form of a subnet. The result will be a set of isolated clusters. At the end of the topology control step, these clusters need to be efficiently connected. The proposed algorithm achieves this by forming the network topology based on an optimal subset of connections for each cluster, facilitated through cluster head controller nodes. In the proposed method, neighbors belonging to other clusters are evaluated for each controller node, and the weight of the connection between two nodes i and j is determined using the provided relationship.

In the above relation, ${N}_{(i)}$ represents the neighborhood set of node i. ${B}_{K}^{\prime}$ represents the set of nodes covered by the current topology structure (nodes that are either members of the topology or are located in the neighborhood of at least one of its members). P represents the initial energy of network nodes, and ${E}_{j}$ specifies the current energy of node j. Finally, ${D}_{ij}$ indicates the end-to-end delay in communication between two nodes i and j, which is estimated during the neighbor discovery process.

The weight function used in relation ( 9 ) consists of two parts: profit and loss. This function states that connecting two clusters by adding a link between two nodes i and j to the network topology, how much benefit and how much loss will result. The first part of this relationship is $\frac{|{N}_{(i)}-{B}_{K}^{\prime}|}{|{B}_{K}^{\prime}|}$ and it shows the number of new nodes that will be covered by the topology by joining these two clusters. If the selection of a node can add more members to the topology, adding with that node will have a high benefit. On the other hand, if the selection of a node does not add any new member to the topology, then $\frac{|{N}_{(i)}-{B}_{K}^{\prime}|}{|{B}_{K}^{\prime}|}=0$ and relation ( 9 ) is negative Will have. As a result, the proposed weight calculation relationship will prevent the addition of ineffective nodes in the topology. The second part of the weight function in relation ( 9 ) represents the amount of loss resulting from the selection of node j and is described as $\frac{P{\times D}_{ij}}{{E}_{j}}$ . This part of the weight function shows how adding node j to the topology will affect the topology's lifetime and latency.

After evaluating the weight function by the nodes completing the topology (Relation 9 ), each time the link with the most positive weight is selected and the cluster corresponding to that selection will be added to the topology.

Considering this feature, the network clustering structure will become a hierarchical tree topology, an example of which is shown in Fig. 5 .

A view of the topology structure formed in the proposed method.

In Fig. 5 , each SDN controller and its neighboring nodes are considered as a sub-domain in SDN. Communication between subdomains is facilitated through gateway nodes (shown in gray in the figure). Additionally, the connections between the controllers are illustrated with dashed lines in this figure. Once each cluster is formed, the controller node will possess its member list and exchange it with other controllers. After establishing the network topology, route selection, and data exchange operations will be executed. This step of the proposed method will be further elucidated in the following section.

Data routing using a structured structure

After forming the network topology, this structure will be utilized for data routing concerning library information. Due to the tree topology, it is evident that there will be only one path between each controller node and the data center. Thus, if a node intends to send a request to receive information from the data center, the source node initially transmits its ID to its subdomain controller. The controller node then searches for the source ID in its member list, and if it corresponds, it forwards the sensor request to the central node through the unique path in the hierarchical tree. Upon receiving this message, the central node matches the requester's sensor ID with the requested information ID, and if the information is accurate, the requested media is transmitted to the controller node through the existing path.

Result and discussion

The approach under consideration underwent a thorough assessment comprising two specific stages. During the initial phase, we concentrated on the practical application in a small-scale library for a month. Through this on-site evaluation, we scrupulously examined the effectiveness of the suggested architectural method, making comparisons with the library's conventional approach during the same period. In the subsequent phase, we created a simulation environment utilizing MATLAB 2021. In this stage, we methodically evaluated the performance of the proposed algorithm, offering a detailed analysis in contrast to the algorithm outlined in the reference articles 25 and 6 .

Real-world implementation results

The proposed architecture addresses the evolving landscape of smart libraries by introducing an IoT-based low-cost system leveraging SDN. Targeting RFID-based processes, such as authentication, property circulation, and book loans, the system intelligently oversees various library activities, reducing the need for constant operator supervision and providing scalability for extensive library environments. The evaluation of this architecture over a one-month period, as indicated in Figs. 6 , 7 , 8 , 9 , demonstrates its practical implementation in a small-scale library, highlighting its potential to enhance efficiency and cost-effectiveness in managing library processes. Figures 6 , 7 , 8 , 9 pertain to the first phase, where we conducted evaluations at various instances to record library item transactions, encompassing four components: the first involves tag identification, the second encompasses tag processing, the third engages in ID searching, and the fourth involves saving the results. Average processing times were scrutinized at different intervals, revealing that, on the whole, this architecture can accomplish each transaction in under half a second, signifying a substantial enhancement in system speed. Figure 7 illustrates accuracy and the frequency of error types. The average accuracy rate is presented in Fig. 7 .a, calculated by determining the error count on different days, dividing it by the total number of transactions, and deriving its rate. The results, as depicted in Fig. 7 .a, indicate that the proposed method attains a minimum accuracy of 99% on various days, demonstrating an error probability of less than one percent. Figure 7 b further categorizes errors by type, revealing three categories: (1) tag reader error, (2) user error, and (3) network error, with user error exhibiting the highest error rate. Nearly half of the errors are attributed to user operator mistakes, largely attributed to users' unfamiliarity with the system, although this has significantly decreased over different days with the operators gaining more familiarity with the system.

The average processing time of the proposed system for recording the transaction of library items.

System performance chart in transaction registration ( a ) average accuracy per day, ( b ) frequency of error types.

Average demand queue by day.

Comparison of sensitivity and specificity criteria of the proposed method with the traditional method.

Figure 8 presents the daily average number of demand queues, determined over a span of days, with an observed average of 1.4 queues. This signifies that, even in the presence of continuous demand within the library system, the number of individuals in the demand queue remains below a specified threshold, effectively preventing congestion in the system.

In Fig. 9 , a comparative analysis is conducted between the proposed smart system, denoted as RFID, and the traditional user-based system, focusing on specificity and sensitivity criteria. This comparison assesses whether the RFID system excels in identifying users and making book judgments compared to human users. The findings suggest that the proposed method, marked as RFID, demonstrates relatively high accuracy and ideal performance compared to the traditional system.

Simulation results

In this operational phase, where the proposed method is simulated in an environment, we have focused on the construction of topology and information routing between the nodes of the smart library network and compared the results with 6 and 25 . Moving on to Figs. 10 , 11 , 12 , 13 , these figures delve into a comprehensive comparison of the proposed method with previous approaches. Figure 10 specifically addresses the packet delivery rate concerning changes in the number of network nodes. Through a simulated environment involving 100 to 300 nodes with heterogeneous characteristics, the figure reveals an increasing packet delivery rate as the number of nodes rises. This is attributed to the multi-step method employed, utilizing users themselves to exchange data, resulting in a higher probability of successful packet delivery.

The percentage of successful reception of packets against the number of visiting nodes.

Energy consumption of the whole network in relation to the number of visiting nodes.

Average end-to-end network latency versus number of visitors.

Load imposed on each node for forwarding data according to the position of that node in the environment.

Figure 11 shifts the focus to energy consumption, indicating an exponential increase in constant energy as the number of nodes rises. Interestingly, the proposed method consumes less energy up to 250 nodes, exceeding the compared method for 300 nodes. This lower energy consumption for 100 to 250 nodes indicates superior performance in terms of energy efficiency, attributed to the use of an efficient energy topology in the software network architecture considered. However, as the number of nodes reaches 300, energy consumption rises due to significant differences in transmissions between the proposed and compared methods.

Figure 12 calculates the average user delay, revealing consistently lower delays in the proposed method across various scenarios. Lastly, Fig. 13 showcases the successful load balancing achieved by the proposed method. The equal participation of nodes in sending data, regardless of their position, demonstrates the method's effectiveness in load distribution, contributing to an extended network lifespan and enhanced efficiency.

Our research paper proposes an innovative IoT-based low-cost architecture for smart libraries using SDN, presenting a comprehensive method for imbuing libraries with intelligence. The core contribution lies in the synergy of IoT and SDN technologies, showcasing the capacity to intelligently oversee diverse library activities, thus reducing the need for constant operator supervision. Our proposed framework incorporates RFID-based processes for authentication, property circulation management, and library book loan management, while the data exchange relies on IoT SDN. The architecture encompasses key components such as a data center, SDN controllers, RFID tags, tag readers, and other network sensors. Our proposed architecture offers a transformative approach to traditional libraries, enhancing key processes through intelligent information management. The data center plays a pivotal role in organizing crucial information related to books, members, and book loans, facilitating efficient library resource management. Leveraging RFID technology for member and book identification, the proposed system ensures secure and streamlined processes. The integration of SDN-based network configuration involves a hierarchical tree topology and a weight-based algorithm for efficient cluster connectivity, addressing concerns related to cost and scalability. Our innovative approach provides a foundation for modernizing extensive libraries and lays the groundwork for future advancements in the integration of IoT and SDN technologies.

Data availability

All data generated or analysed during this study are included in this published article.

Alqarni, H., Alnahari, W. & Quasim, M. T. Internet of things (IoT) security requirements: Issues related to sensors. In 2021 National Computing Colleges Conference (NCCC) 1–6 (IEEE, 2021).

Alnahari, W. & Quasim, M. T. Authentication of IoT device and IoT server using security key. In 2021 International Congress of Advanced Technology and Engineering (ICOTEN) 1–9 (IEEE, 2021).

Quasim, M. T., Khan, M. A., Algarni, F. & Alshahrani, M. M. Fundamentals of smart cities. Smart cities: A data analytics perspective. 3–16 (2021).

Bekri, W., Jmal, R. & Chaari Fourati, L. Internet of things management based on software defined networking: A survey. Int. J. Wireless Inform. Netw. 27 , 385–410 (2020).

Article Google Scholar

Kumhar, M. & Bhatia, J. Software-defined networks-enabled fog computing for IoT-based healthcare: Security, challenges and opportunities. Secur. Privacy 6 (5), e291 (2023).

Luo, K. A distributed SDN-based intrusion detection system for IoT using optimized forests. PLoS ONE 18 (8), e0290694 (2023).

Article CAS PubMed PubMed Central Google Scholar

Singh, S. K., et al. Evolving requirements and application of SDN and IoT in the context of industry 4.0, blockchain and artificial intelligence. In Software Defined Networks: Architecture and Applications 427–496 (2022).

Amiri, Z. et al. The personal health applications of machine learning techniques in the internet of behaviors. Sustainability 15 (16), 12406 (2023).

Bhola, B., Kumar, R. & Mishra, B. K. Internet of things-based low cost water meter with multi functionality. Int. J. Web Grid Serv. 18 (3), 250–265 (2022).

Gupta, B. B. & Quamara, M. An overview of Internet of Things (IoT): Architectural aspects, challenges, and protocols. Concurr. Comput.: Pract. Exp. 32 (21), e4946 (2020).

Chen, X. et al. Traffic modeling and performance evaluation of SDN-based NB-IoT access network. Concurr. Comput.: Pract. Exp. 32 (16), e5145 (2020).

Amiri, Z., et al. Adventures in data analysis: A systematic review of Deep Learning techniques for pattern recognition in cyber-physical-social systems. Multim. Tools Appl. 1–65 (2023).

Ali, S., Pandey, M. & Tyagi, N. SDFog-Mesh: A software-defined fog computing architecture over wireless mesh networks for semi-permanent smart environments. Comput. Netw. 211 , 108985 (2022).

Amiri, Z. et al. Resilient and dependability management in distributed environments: A systematic and comprehensive literature review. Cluster Comput. 26 (2), 1565–1600 (2023).

Suresh Kumar, K., et al., Modeling of VANET for future generation transportation system through Edge/Fog/Cloud computing powered by 6G. Cloud and IoT‐based vehicular ad hoc networks 105–124 (2021).

Stallings, W. Foundations of Modern Networking: SDN, NFV, QoE, IoT, and Cloud (Addison-Wesley Professional, 2015).

Tavana, M., Hajipour, V. & Oveisi, S. IoT-based enterprise resource planning: Challenges, open issues, applications, architecture, and future research directions. Internet Things 11 , 100262 (2020).

Molina Zarca, A. et al. Enhancing IoT security through network softwarization and virtual security appliances. Int. J. Netw. Manag. 28 (5), e2038 (2018).

Haseeb, K. et al. A machine learning SDN-enabled big data model for IoMT systems. Electronics 10 (18), 2228 (2021).

Fathy, C. & Ali, H. M. A secure IoT-based irrigation system for precision agriculture using the expeditious cipher. Sensors 23 (4), 2091 (2023).

Article ADS PubMed PubMed Central Google Scholar

Stolojescu-Crisan, C., Crisan, C. & Butunoi, B.-P. An IoT-based smart home automation system. Sensors 21 (11), 3784 (2021).

Chiliquinga, S., et al. An approach of low-cost software-defined network (SDN) based internet of things. In 2020 International Conference of Digital Transformation and Innovation Technology (Incodtrin) (IEEE, 2020).

Conti, M., Kaliyar, P. & Lal, C. CENSOR: Cloud-enabled secure IoT architecture over SDN paradigm. Concurr. Comput.: Pract. Exp. 31 (8), e4978 (2019).

Younus, M. U. et al. A survey on software defined networking enabled smart buildings: Architecture, challenges and use cases. J. Netw. Comput. Appl. 137 , 62–77 (2019).

Sankar, S. et al. CT-RPL: Cluster tree based routing protocol to maximize the lifetime of Internet of Things. Sensors 20 (20), 5858 (2020).

Gupta, N., et al. Original Research Article An intelligent technique for network resource management and analysis of 5G-IoT smart healthcare application. J. Auton. Intell. 7 (1) (2020).

Bhuiyan, M. N. et al. Design and implementation of a feasible model for the IoT based ubiquitous healthcare monitoring system for rural and urban areas. IEEE Access 10 , 91984–91997 (2022).

de Melo, P. H., Miani, R. S. & Rosa, P. F. FamilyGuard: A security architecture for anomaly detection in home networks. Sensors 22 (8), 2895 (2022).

Elhoseny, M. et al. Energy-efficient mobile agent protocol for secure iot sustainable applications. Sustainability 14 (14), 8960 (2022).

Njah, Y., Pham, C. & Cheriet, M. Service and resource aware flow management scheme for an SDN-based smart digital campus environment. IEEE Access 8 , 119635–119653 (2020).

Gordon, H., et al. Securing smart homes via software-defined networking and low-cost traffic classification. In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC) (IEEE, 2021).

Ganesan, E., et al. SDN-enabled FiWi-IoT smart environment network traffic classification using supervised ML models. In Photonics (MDPI, 2021).

Wang, Y. C. & Wang, Y. C. Efficient and low-cost defense against distributed denial-of-service attacks in SDN-based networks. Int. J. Commun. Syst. 33 (14), e4461 (2020).

Abid, M. A. et al. Evolution towards smart and software-defined internet of things. AI 3 (1), 100–123 (2022).

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Xu, H., Liu, Wd., Li, L. et al. An IoT-based low-cost architecture for smart libraries using SDN. Sci Rep 14 , 7022 (2024). https://doi.org/10.1038/s41598-024-57484-2

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A REVIEW PAPER ON "IOT" & FUTURE RESEARCH IN INTERNET APPLICATIONS

2023, IRJET

The Internet of Things (IOT), likewise referred to as the Internet of Everything or the Industrial Internet, is any other innovation worldview imagined troubles as an international agency of machines and devices prepared for speaking with one any other. "This gives the cap potential to degree, locate and apprehend herbal markers, from sensitive ecologies and simple sources for metropolitan conditions. Accordingly, a big degree of facts are being created, placed away, and that facts is being organized into precious sports that can "order and control" the matters to make our contains on with plenty easier and extra secure-and to reduce our impact at the climate. Web of Things (IOT), likewise referred to as the snare of the Industrial Internet, is probably any other innovation worldview imagined as an common agency of machines and devices suit for interfacing with every different. We plot protection stipulations for IOT along facet the not unusual place assaults, dangers, and reducing part preparations. In this paper we can test approximately what are the problems in Iot, Advantage and Disadvantage in Iot.

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Internet of Things (IoT) can also be a thinking that encompasses severa objects and approaches of verbal exchange to change info. these days IoT is a lot of a descriptive time period of a imaginative and prescient that the whole thing ought to be related to the web. IoT are going to be primary inside the future as a end result of the notion exposes possibilities for manufacturer new offerings and new innovations. All objects are going to be linked and geared up to speak with one another, whereas they function in unprotected environments. This later side outcomes in most important protection challenges. With the introduction of the net of Things (IoT), our verbal exchange capability may not be constrained to completely cell devices. Rather, it will increase to any or all matters with that we have a tendency to be. numerous research have referred to IoT-related offerings and platforms. However, there rectangular measure completely restrained discussions concerning the IoT network. at some point of this paper, we will completely analyze the technical small print involving the IoT network. supported our survey of papers, we are going to provide perception concerning the lengthy run IoT community and consequently the integral parts which will alter it. With the net of Things (IoT) bit by means of bit evolving due to the fact the succeeding area of the evolution of the web, it turns into integral to well known the different manageable domains for utility of IoT, and consequently the evaluation challenges that rectangular measure associated to these applications. Beginning from smart cities, to fitness care, good agriculture, imparting and retail, to even smart residing and good environments IoT is estimated to infiltrate into just about all components of way of life.

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The internet of Things is becoming one of the achievements in the age of networking that is going to dispose future of information technology. IOT avail connections to users on anywhere, anything and at any moment. IOT is a creative idea that alters the real world objects into virtual objects. IOT enables users to control over labeled items like door locks, lights, microwave, tv, coffeemaker, washing machine, window locks and so on and keeps up-to-date about the state. The description of a concept IOT represent various technologies that make the internet available to each real world tangible objects. In this paper we focus on the various applications of IOT like interoperability, smart cities, smart medicine, of ices, home, transportation, vegetable traceability system in agriculture, cyber security, ecommerce and so on. This paper also focus on the middleware which acts as a software layer between the IT infrastructures and also hides the implementation technalities of the programmers. This paper also puts light on how temperature affects IOT.

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Introduction Now a days Internet of Things is very important topic all over the technological world and has become very much popular on Social media and Press media. The Phrase Internet of Things is a combination of two: One is the Internet (Network) and second is Things (Any object). The internet of things is the network of physical devices or objects/Things like: Vehicles, Home appliances, Industrial equipment, Office devices and Hospitals machines and also other things rooted with electronic devices, software, sensor devices, actuators and network connectivity-that enables these objects to communicate over a network. The internet of Things is a system of interconnected computing devices that refers with Human-to-Human, Things-to-Things and Human-to-Things over a shared network. The Internet of Things technology has a wide range of networked products, systems, devices, sensors and objects having advantages of advancements in computing power, electronic miniaturization, and network interconnections to offer new capabilities not previously possible. In others word, the Internet of Things (IoT) refers to the capability of every day devices to connect to other devices and also people through the existing Internet Infrastructures. That devices connect and communicate in many ways. A huge scale of business proposals, business-conferences and debates has discussed that IoT is revolution for new technologies, Like; new market research and business models to concerns more about security, privacy, comfort, reliability and technical interoperability. For Example: like Smart devices interaction with another smart devices, Smartphones that interact with other smartphones over a network, Vehicle-to-Vehicle communication, Smartphones connected with home appliances, Smart Devices connected with video cameras, and medical devices.

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Do ＩＯＴ problem definition and research. Research on Internet of things, first research object, Re research alliance，Re study network. Objects are things in the Internet of things, Link is how objects connect to the network, Network is what this network is. Objective function is the key problem. Can start with simple and critical questions. Algorithm is the solution to the problem steps. What is the Internet of things, objects connected to the Internet is the Internet of things, cup networking, car networking. Things better than other networks, is composed of what objects, what composition, what nature, what innovation and superiority. Internet of things four key technologies are widely used, these four technologies are mainly RFID, WSN, M2M, as well as the integration of the two. RFID can be achieved using MATLAB, NS2, Android, WSN can use NS2, OMNET++ implementation, M2M can be developed using JAVA. Therefore, this paper focuses on the advantages of Internet of things than the internet.

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The Internet of Things (IoT ) refers to the wireless connection of ordinary objects, such as vehicles, cash machines, door locks, cameras, industrial controls, and municipal traffic systems, to ...
IEEE Internet of Things Journal
Purpose and Scope. The IEEE IoT Journal (IoT-J), launched in 2014 (" Genesis of the IoT-J "), publishes papers on the latest advances, as well as review articles, on the various aspects of IoT. Topics include IoT system architecture, IoT enabling technologies, IoT communication and networking protocols, IoT services and applications, and ...
Internet of Things
The Internet of Things (IoT) is an interconnected network of objects which range from simple sensors to smartphones and tablets ... The fifth generation (5G) of cellular networks will bring 10 Gb/s user speeds, 1000-fold increase in system capacity, and 100 times higher connection density. In response to these requirements, the 5G networks will ...
(PDF) IoT in Smart Cities: A Survey of Technologies ...
Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities ...
PDF Chapter 3: Internet of Things (IoT)
The Internet of Things (IoT) is the network of these connected devices. These smart, connected devices generate data that IoT applications use to aggregate, analyze, and deliver insight, which helps drive more informed decisions and actions. By 2030, consumers anticipate an IoT experience that is omnipresent, seamless and personalized:[4]
Publications
IEEE Internet of Things Journal (IoT-J) Launched in 2014, the IEEE IoT-J publishes papers on the latest advances, as well as review articles, on the various aspects of IoT from open call and special issues. Topics include IoT system architecture, IoT enabling technologies, IoT communication and networking protocols, IoT services and ...
Sensors
Download PDF Download PDF with Cover Download XML Download Epub. Browse Figures. ... and emerging application domains. We found a slew of surveys and research papers focusing on the IoT technology stack, integration with existing systems, and highly specialised application domains like the Industrial Internet of Things (IIoT), IoT for wearable ...
Different Applications and Technologies of Internet of Things (IoT)
order to get the full capability of IoT in changing society. This research paper addresses the key applications of IoT, the architecture of IoT, and the key issues affecting IoT. In addition, the paper ... This sage can be described as a free architecture that enables the utilization of technologies as well as huge computing power from ...
(PDF) Internet of Things (IoT): Research, Architectures and
Download Free PDF. Download Free PDF. Internet of Things (IoT): Research, Architectures and Applications ... Besides, IoT experiences several problems that need to be considered in order to get the full capability of IoT in changing society. This research paper addresses the key applications of IoT, the architecture of IoT, and the key issues ...
(PDF) Internet of Things (IOT): Research Challenges and Future
Download Free PDF. Internet of Things (IOT): Research Challenges and Future Applications. ... individuals, the society or communities and institutions. As discussed in the application section of this research paper, the IoT has without a doubt a massive capability to be a tremendously transformative force, which will, and to some extent does ...
iot applications Latest Research Papers
The Internet Of Things . Microstrip Array Antenna. This paper presents the design of 2*1 and 4*1 RFID reader microstrip array antenna at 2.4GHz for the Internet of things (IoT) networks which are Zigbee, Bluetooth and WIFI. The proposed antenna is composed of identical circular shapes radiating patches printed in FR4 substrate.
IoT based Smart Applications and Recent Research Trends
The Internet of Things (IoT) is a unique and prominent technology of the recent era which is in full swing and will have a phenomenal role in the market going onward. In this technology the devices which contain sensors, actuators and processors can communicate with each other and help us to work for our day to day actions which in result therefore reducing human effort. IoT is helping human ...
Future applications and research challenges of IOT
Internet of Things (IoT) extends the concept of a digital world into the physical world. This extension will lead the human to be more secure, comfortable and happier than before. The merger of the internet and things also influence the growth of the economy due to its numerous applications. IoT applications cover almost all aspects of human life and make the connectivity possible at anytime ...
IoT-enabled smart cities: a hybrid systematic analysis of key research
Cities are expected to face daunting challenges due to the increasing population in the near future, putting immense strain on urban resources and infrastructures. In recent years, numerous studies have been developed to investigate different aspects of implementing IoT in the context of smart cities. This has led the current body of literature to become fairly fragmented. Correspondingly ...
(PDF) Industrial Internet of Things: A Review
Download full-text PDF Read ... IoT and leverage its value. This research presents a comprehensive review to study state-of-the-art challenges and recommended technologies for enabling data ...
Top 20 Internet of Things (IoT) Journals
2. Elsevier - Internet of Things. Internet of Things; Engineering Cyber-Physical Human Systems is a comprehensive journal encouraging cross-collaboration between researchers, engineers, and practitioners in the field of IoT and cyber-physical Human Systems.The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal ...
Internet of Things for Smart Healthcare: Technologies, Challenges, and
Internet of Things (IoT) technology has attracted much attention in recent years for its potential to alleviate the strain on healthcare systems caused by an aging population and a rise in chronic illness. Standardization is a key issue limiting progress in this area, and thus this paper proposes a standard model for application in future IoT healthcare systems. This survey paper then presents ...
An IoT-based low-cost architecture for smart libraries using SDN
In the evolving landscape of smart libraries, this research pioneers an IoT-based low-cost architecture utilizing Software-Defined Networking (SDN). The increasing demand for more efficient and ...
(PDF) The Internet of Things for Healthcare: Applications, Selected
Eventually, in the healthcare field, IoT is revolutionizing the creation of effective healthcare delivery, creating a platform for communication between different health segments, providing ...
(Pdf) a Review Paper on "Iot" & Future Research in Internet
CHALLENGES IN IOT: In this part, the paper examines the bulk of widely known problems or widespread problems of the IoT climate; it likewise indicates the continuing exam headings for each subject. 2.1 Networking: Generally, the Networking problem has an brilliant importance with inside the Internet in view of it consists of a part of the ...
iot Internet of things IEEE PAPERS AND PROJECTS-2020
The Internet of things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. IoT based Clinical Sensor Data Management and ...
Smart Farming: The IoT based Future Agriculture
Agriculture is backbone of any country. About 60% of our country's population works in agriculture or the primary sector. It contributes more to our country's GDP. It employs the majority of India's population. The internet of things research presents a framework in which farmers may obtain extensive information on the soil, crops growing in specific areas, and agricultural yield and ...
(PDF) SMART AGRICULTURE USING IOT
Abstract. Agriculture industry is developed a lot with the help of technology, it became data-centered and smarter. The rapid growth of Internet of Things based technologies reshaped many ...

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Service Provisioning for Multi-source IoT Applications in Mobile Edge Computing

Design and Analysis of a RFID Reader Microstrip Array antenna for IoT Applications in Smart Cities

A Survey on Privacy Preservation in Fog-Enabled Internet of Things

Design and Deployment of Expressive and Correct Web of Things Applications

Design and Analysis of a RFID Reader Microstrip Array Antenna for IoT Applications in Smart Cities

Energy-Aware Security Adaptation for Low-Power IoT Applications

Motivating Users to Manage Privacy Concerns in Cyber-Physical Settings—A Design Science Approach Considering Self-Determination Theory

Preventing MQTT Vulnerabilities Using IoT-Enabled Intrusion Detection System

Proposed RPL routing protocol in the IoT applications

IoT-enabled smart cities: a hybrid systematic analysis of key research areas, challenges, and recommendations for future direction

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A Review of Dominant Issues, Multi-dimensions, and Future Research Directions for Smart Cities

Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review

Indicators for Smart Cities: Bibliometric and Systemic Search

1 Introduction

2 Methodology

2.1 Database development

2.2 Identification of key research themes

2.3 Content analysis

3 Results and discussions

3.2 Identification of key research areas using bibliometric analysis

3.3 Critical analysis of research themes

3.3.1.1 Applications of IoT data analysis in IESCs

3.3.2 Network and communication management and technologies in IESCs

3.3.2.1 Communication technologies in IESCs

3.3.3 Security and privacy management in IESCs

3.3.3.1 Managing and combating the issues

3.3.4 Data collection approach in IESCs

4 Challenges and recommendations

5 Conclusions

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Top 20 Internet of Things (IoT) Journals – 2024

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6. Springer – Wireless Networks (SpringerNature)

4. IEEE – Wireless Communications

5. IEEE – Transactions on Wireless Communications

10. MDPI – Sensors — Open Access Journal

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An IoT-based low-cost architecture for smart libraries using SDN

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An intelligent model for supporting edge migration for virtual function chains in next generation internet of things

An improved Lagrangian relaxation algorithm based SDN framework for industrial internet hybrid service flow scheduling

A data plane security model of segmented routing based on SDP trust enhancement architecture

Introduction

Related works

Proposed algorithm

RFID-based processes in the proposed architecture

SDN-based network configuration and routing

Construction of topology

Data routing using a structured structure

Result and discussion