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IoT Enabling Technologies for Smart Cities: Challenges and Approaches

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (29 July 2022) | Viewed by 41722
Please contact the Guest Editor or the Section Managing Editor at ([email protected]) for any queries.

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Special Issue Editors

Dr. Laura Belli

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Guest Editor

Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy
Interests: Internet of Things; smart agriculture; smart cities; big stream; data
Special Issues, Collections and Topics in MDPI journals

Dr. Luca Davoli

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Guest Editor

Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy
Interests: Internet of Things; smart agriculture; software-defined networking; big stream; security
Special Issues, Collections and Topics in MDPI journals

Dr. Marco Martalò

E-Mail Website
Guest Editor

Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, Piazza d’Armi, 09123 Cagliari, Italy
Interests: Internet of Things; smart cities; localization; wireless networking
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gianluigi Ferrari

E-Mail Website
Guest Editor

Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy
Interests: Internet of Things; networking and communication; signal processing; smart systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ongoing diffusion of Internet of Things (IoT) technologies is opening new possibilities in different and heterogeneous fields, with remarkable applications being associated with the smart city paradigm, continuously evolving and representing the future of modern cities. On a wider perspective, the smart city concept involves the integration of IoT and Information Communication Technologies (ICT) into different aspects of the city’s management, with the aims of (i) addressing the exponential growth of urbanization and population and (ii) increasing people's life quality. Moreover, the smart city paradigm is also strictly connected to sustainability aspects, taking into account the environmental impact’s reduction of urban activities, the optimized management of energy resources, and the design of innovative services and solutions for citizens. Abiding by this new paradigm, several cities have started a process of strong innovation in different sectors, also on the basis of significant investments. Despite the progress achieved thus far, many challenges are currently open, due to the complexity of sustainable smart city scenarios, which require integration and cooperation between a multitude of actors and technologies.

This Special Issue thus encourages authors, from academia and industry, to submit new research providing a novel insight into the challenges and the approaches in the development of IoT infrastructures for future sustainable smart cities.

Dr. Laura Belli
Dr. Luca Davoli
Prof. Dr. Marco Martalò
Prof. Dr. Gianluigi Ferrari
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

The Special Issue topics include but are not limited to:

IoT technology integration for smart cities
IoT architectures and infrastructures for smart cities
Blockchain technologies applied to smart cities
Machine-learning-based and Artificial-Intelligence-oriented technologies applied to smart cities
Smart urban mobility and transportation in smart cities
IoT-aided localization techniques in smart cities
IoT applications for tourism and education scenarios in smart cities
Edge/cloud-computing-based solutions for IoT smart city data
Big data in smart cities
Smart city services based on IoT data
Sustainable resource management in smart cities
Environmental monitoring in smart cities
Smart waste management in smart cities
Vehicular networking for traffic management in smart cities
Cryptography, security, and privacy issues and challenges in IoT-enabled smart cities
Governance and regulation innovation in smart cities
UAV-supported IoT systems for Smart Cities
IoT-enhanced Smart Transport
Artificial Intelligence of Things (AIoT) for Smart Cities

Published Papers (14 papers)

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Research

Jump to: Review

21 pages, 3296 KiB

Open AccessArticle

A Throughput Request Satisfaction Method for Concurrently Communicating Multiple Hosts in Wireless Local Area Network

by Md. Mahbubur Rahman, Nobuo Funabiki, Kwenga Ismael Munene, Sujan Chandra Roy, Minoru Kuribayashi, Melki Mario Gulo and Wen-Chung Kao

Sensors 2022, 22(22), 8823; https://0-doi-org.brum.beds.ac.uk/10.3390/s22228823 - 15 Nov 2022

Cited by 1 | Viewed by 1091

Abstract

Nowadays, the IEEE 802.11 wireless local area network (WLAN) has been widely used for Internet access services around the world. Then, the unfairness or insufficiency in meeting the throughput request can appear among concurrently communicating hosts with the same access point (AP), which should be solved by sacrificing advantageous hosts. Previously, we studied the fairness control method by adopting packet transmission delay at the AP. However, it suffers from slow convergence and may not satisfy different throughput requests among hosts. In this paper, we propose a throughput request satisfaction method for providing fair or different throughput requests when multiple hosts are concurrently communicating with a single AP. To meet the throughput request, the method (1) measures the single and concurrent throughput for each host, (2) calculates the channel occupying time from them, (3) derives the target throughput to achieve the given throughput request, and (4) controls the traffic by applying traffic shaping at the AP. For evaluations, we implemented the proposal in the WLAN testbed system with one Raspberry Pi AP and up to five hosts, and conducted extensive experiments in five scenarios with different throughput requests. The results confirmed the effectiveness of our proposal. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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28 pages, 4815 KiB

Open AccessArticle

Design and Implementation of SEMAR IoT Server Platform with Applications

by Yohanes Yohanie Fridelin Panduman, Nobuo Funabiki, Pradini Puspitaningayu, Minoru Kuribayashi, Sritrusta Sukaridhoto and Wen-Chung Kao

Sensors 2022, 22(17), 6436; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176436 - 26 Aug 2022

Cited by 16 | Viewed by 2247

Abstract

Nowadays, rapid developments of Internet of Things (IoT) technologies have increased possibilities of realizing smart cities where collaborations and integrations of various IoT application systems are essential. However, IoT application systems have often been designed and deployed independently without considering the standards of devices, logics, and data communications. In this paper, we present the design and implementation of the IoT server platform called Smart Environmental Monitoring and Analytical in Real-Time (SEMAR) for integrating IoT application systems using standards. SEMAR offers Big Data environments with built-in functions for data aggregations, synchronizations, and classifications with machine learning. Moreover, plug-in functions can be easily implemented. Data from devices for different sensors can be accepted directly and through network connections, which will be used in real-time for user interfaces, text files, and access to other systems through Representational State Transfer Application Programming Interface (REST API) services. For evaluations of SEMAR, we implemented the platform and integrated five IoT application systems, namely, the air-conditioning guidance system, the fingerprint-based indoor localization system, the water quality monitoring system, the environment monitoring system, and the air quality monitoring system. When compared with existing research on IoT platforms, the proposed SEMAR IoT application server platform offers higher flexibility and interoperability with the functions for IoT device managements, data communications, decision making, synchronizations, and filters that can be easily integrated with external programs or IoT applications without changing the codes. The results confirm the effectiveness and efficiency of the proposal. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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11 pages, 722 KiB

Open AccessArticle

IEEE P2668 Compatible Evaluation Strategy for Smart Battery Management Systems

by Hao Wang, Kim Fung Tsang, Chung Kit Wu, Yang Wei, Yucheng Liu and Chun Sing Lai

Sensors 2022, 22(16), 6057; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166057 - 13 Aug 2022

Cited by 1 | Viewed by 1687

Abstract

In smart cities and smart industry, a Battery Management System (BMS) focuses on the intelligent supervision of the status (e.g., state of charge, temperature) of batteries (e.g., lithium battery, lead battery). Internet of Things (IoT) integration enhances the system’s intelligence and convenience, making it a Smart BMS (SBMS). However, this also raises concerns regarding evaluating the SBMS in the wireless context in which these systems are installed. Considering the battery application, in particular, the SBMS will depend on several wireless communication characteristics, such as mobility, latency, fading, etc., necessitating a tailored evaluation strategy. This study proposes an IEEE P2668-Compatible SBMS Evaluation Strategy (SBMS-ES) to overcome this issue. The SBMS-ES is based on the IEEE P2668 worldwide standard, which aims to assess IoT solutions’ maturity. It evaluates the characteristics of the wireless environment for SBMS while considering battery factors. The SBMS-ES scores the candidates under numerous scenarios with various characteristics. A final score between 0 and 5 is given to indicate the performance of the SBMS regarding the application demands. The disadvantages of the SBMS solution and the most desired candidate can be found with the evaluated score. SBMS-ES provides guidance to avoid potential risks and mitigates the issues posed by an inadequate or unsatisfactory SBMS solution. A case study is depicted for illustration. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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20 pages, 5471 KiB

Open AccessArticle

Self-Sufficient Sensor Node Embedding 2D Visible Light Positioning through a Solar Cell Module

by Irene Cappelli, Federico Carli, Ada Fort, Federico Micheletti, Valerio Vignoli and Mara Bruzzi

Sensors 2022, 22(15), 5869; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155869 - 05 Aug 2022

Cited by 3 | Viewed by 1782

Abstract

Nowadays, indoor positioning (IP) is a relevant aspect in several scenarios within the Internet of Things (IoT) framework, e.g., Industry 4.0, Smart City and Smart Factory, in order to track, amongst others, the position of vehicles, people or goods. This paper presents the realization and testing of a low power sensor node equipped with long range wide area network (LoRaWAN) connectivity and providing 2D Visible Light Positioning (VLP) features. Three modulated LED (light emitting diodes) sources, the same as the ones commonly employed in indoor environments, are used. The localization feature is attained from the received light intensities performing optical channel estimation and lateration directly on the target to be localized, equipped with a low-power microcontroller. Moreover, the node exploits a solar cell, both as optical receiver and energy harvester, provisioning energy from the artificial lights used for positioning, thus realizing an innovative solution for self-sufficient indoor localization. The tests performed in a ~1 m² area reveal accurate positioning results with error lower than 5 cm and energy self-sufficiency even in case of radio transmissions every 10 min, which are compliant with quasi-real time monitoring tasks. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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16 pages, 422 KiB

Open AccessArticle

Time-Constrained Node Visit Planning for Collaborative UAV–WSN Distributed Applications

by Andrea Augello, Salvatore Gaglio, Giuseppe Lo Re and Daniele Peri

Sensors 2022, 22(14), 5298; https://doi.org/10.3390/s22145298 - 15 Jul 2022

Cited by 2 | Viewed by 1326

Abstract

Unmanned Aerial Vehicles (UAVs) are often studied as tools to perform data collection from Wireless Sensor Networks (WSNs). Path planning is a fundamental aspect of this endeavor. Works in the current literature assume that data are always ready to be retrieved when the UAV passes. This operational model is quite rigid and does not allow for the integration of the UAV as a computational object playing an active role in the network. In fact, the UAV could begin the computation on a first visit and retrieve the data later. Potentially, the UAV could orchestrate the distributed computation to improve its performance, change its parameters, and even upload new applications to the sensor network. In this paper, we analyze a scenario where a UAV plays an active role in the operation of multiple sensor networks by visiting different node clusters to initiate distributed computation and collect the final outcomes. The experimental results validate the effectiveness of the proposed method in optimizing total flight time, Average Age of Information, Average cluster computation end time, and Average data collection time compared to prevalent approaches to UAV path-planning that are adapted to the purpose. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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22 pages, 3046 KiB

Open AccessArticle

Integrating Deep Learning-Based IoT and Fog Computing with Software-Defined Networking for Detecting Weapons in Video Surveillance Systems

by Cherine Fathy and Sherine Nagy Saleh

Sensors 2022, 22(14), 5075; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145075 - 06 Jul 2022

Cited by 21 | Viewed by 2675

Abstract

Due to the widespread proliferation of multimedia traffic resulting from Internet of Things (IoT) applications and the increased use of remote multimedia-based applications, as a consequence of COVID-19, there is an urgent need to develop intelligent adaptive techniques that improve the Quality of Service (QoS) perceived by end-users. In this work, we investigate the integration of deep learning techniques with Software-Defined Network (SDN) architecture to support delay-sensitive applications in IoT environments. Weapon detection in real-time video surveillance applications is deployed as our case study upon which multiple deep learning-based models are trained and evaluated for detection using precision, recall, and mean absolute precision. The deep learning model with the highest performance is then deployed within a proposed artificial intelligence model at the edge to extract the first detected video frames containing weapons for quick transmission to authorities, thus helping in the early detection and prevention of different kinds of crimes, and at the same time decreasing the bandwidth requirements by offloading the communication network from massive traffic transmission. Performance improvement is achieved in terms of delay, throughput, and bandwidth requirements by dynamically programming the network to provide different QoS based on the type of offered traffic and current traffic load, and based on the destination of the traffic. Performance evaluation of the proposed model was carried out using the mininet emulator, which revealed improvement of up to 75.0% in terms of average throughput, up to 14.7% in terms of mean jitter, and up to 32.5% in terms of packet loss. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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23 pages, 723 KiB

Open AccessArticle

A SHA-256 Hybrid-Redundancy Hardware Architecture for Detecting and Correcting Errors

by Ignacio Algredo-Badillo, Miguel Morales-Sandoval, Alejandro Medina-Santiago, Carlos Arturo Hernández-Gracidas, Mariana Lobato-Baez and Luis Alberto Morales-Rosales

Sensors 2022, 22(13), 5028; https://0-doi-org.brum.beds.ac.uk/10.3390/s22135028 - 03 Jul 2022

Cited by 2 | Viewed by 2187

Abstract

In emergent technologies, data integrity is critical for message-passing communications, where security measures and validations must be considered to prevent the entrance of invalid data, detect errors in transmissions, and prevent data loss. The SHA-256 algorithm is used to tackle these requirements. Current hardware architecture works present issues regarding real-time balance among processing, efficiency and cost, because some of them introduce significant critical paths. Besides, the SHA-256 algorithm itself considers no verification mechanisms for internal calculations and failure prevention. Hardware implementations can be affected by diverse problems, ranging from physical phenomena to interference or faults inherent to data spectra. Previous works have mainly addressed this problem through three kinds of redundancy: information, hardware, or time. To the best of our knowledge, pipelining has not been previously used to perform different hash calculations with a redundancy topic. Therefore, in this work, we present a novel hybrid architecture, implemented on a 3-stage pipeline structure, which is traditionally used to improve performance by simultaneously processing several blocks; instead, we propose using a pipeline technique for implementing hardware and time redundancies, analyzing hardware resources and performance to balance the critical path. We have improved performance at a certain clock speed, defining a data flow transformation in several sequential phases. Our architecture reported a throughput of 441.72 Mbps and 2255 LUTs, and presented an efficiency of 195.8 Kbps/LUT. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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32 pages, 4394 KiB

Open AccessArticle

An Integrated Success Model of Internet of Things (IoT)-Based Services in Facilities Management for Public Sector

by Norliza Sidek, Nor’ashikin Ali and Gamal Alkawsi

Sensors 2022, 22(9), 3207; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093207 - 22 Apr 2022

Cited by 3 | Viewed by 2605

Abstract

The rapid growth of the Internet of Things (IoT) has vigorously affected government by enhancing quality and efficiency of public services. However, the application of IoT-based services in public sectors is slow, despite its benefits to citizens. Research is needed to deepen understanding of the factors that influence the successful implementation of facilities management as the Internet-of-Things-based services in public sectors. An integrated model is developed and validated to extend the DeLone and McLean IS success model by including technology readiness and other identified factors which impact the use of facilities management of IoT-based services in public sectors from the perspective of employees. An online questionnaire was developed and distributed to employees from all local authorities throughout Malaysia, and 187 usable responses were collected. The partial least squares structural equation modelling approach was used to test the model, with 90.8% of the variance in IoT-based services, suggesting an acceptable model fit with seven out of nine hypotheses were supported. Thus, the empirical evidence exerts significant effects of technology readiness towards the success of IoT-based facility management in the public sector. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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13 pages, 1962 KiB

Open AccessArticle

An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection

by Shabana Habib, Majed Alsanea, Mohammed Aloraini, Hazim Saleh Al-Rawashdeh, Muhammad Islam and Sheroz Khan

Sensors 2022, 22(7), 2602; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072602 - 29 Mar 2022

Cited by 25 | Viewed by 3394

Abstract

Since December 2019, the COVID-19 pandemic has led to a dramatic loss of human lives and caused severe economic crises worldwide. COVID-19 virus transmission generally occurs through a small respiratory droplet ejected from the mouth or nose of an infected person to another person. To reduce and prevent the spread of COVID-19 transmission, the World Health Organization (WHO) advises the public to wear face masks as one of the most practical and effective prevention methods. Early face mask detection is very important to prevent the spread of COVID-19. For this purpose, we investigate several deep learning-based architectures such as VGG16, VGG19, InceptionV3, ResNet-101, ResNet-50, EfficientNet, MobileNetV1, and MobileNetV2. After these experiments, we propose an efficient and effective model for face mask detection with the potential to be deployable over edge devices. Our proposed model is based on MobileNetV2 architecture that extracts salient features from the input data that are then passed to an autoencoder to form more abstract representations prior to the classification layer. The proposed model also adopts extensive data augmentation techniques (e.g., rotation, flip, Gaussian blur, sharping, emboss, skew, and shear) to increase the number of samples for effective training. The performance of our proposed model is evaluated on three publicly available datasets and achieved the highest performance as compared to other state-of-the-art models. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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17 pages, 554 KiB

Open AccessArticle

A Comparative Study of Traffic Classification Techniques for Smart City Networks

by Razan M. AlZoman and Mohammed J. F. Alenazi

Sensors 2021, 21(14), 4677; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144677 - 08 Jul 2021

Cited by 43 | Viewed by 5147

Abstract

Smart city networks involve many applications that impose specific Quality of Service (QoS) requirements, thus representing a challenging scenario for network management. Solutions aiming to guarantee QoS support have not been deployed in large-scale networks. Traffic classification is a mechanism used to manage different aspects, including QoS requirements. However, conventional traffic classification methods, such as the port-based method, are inefficient because of their inability to handle dynamic port allocation and encryption. Traffic classification using machine learning has gained research interest as an alternative method to achieve high performance. In fact, machine learning embeds intelligence into network functions, thus improving network management. In this study, we apply machine learning algorithms to predict network traffic classification. We apply four supervised learning algorithms: support vector machine, random forest, k-nearest neighbors, and decision tree. We also apply a port-based method of traffic classification based on applications’ popular assigned port numbers. Then, we compare the results of this method to those obtained from the machine learning algorithms. The evaluation results indicate that the decision tree algorithm provides the highest average accuracy among the evaluated algorithms, at 99.18%. Moreover, network traffic classification using machine learning provides more accurate results and higher performance than the port-based method. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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18 pages, 6735 KiB

Open AccessArticle

LoRaWAN and Urban Waste Management—A Trial

by Nuno Cruz, Nuno Cota and João Tremoceiro

Sensors 2021, 21(6), 2142; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062142 - 18 Mar 2021

Cited by 11 | Viewed by 3088

Abstract

The city of Lisbon, as any other capital of a European country, has a large number of issues regarding managing waste and recycling containers spread throughout the city. This document presents the results of a study promoted by the Lisbon City Council for trialing LPWAN (Low-Power Wide-Area Network) technology for the waste management vertical under the Lisbon Smart City initiative. Current waste management is done using GSM (Global System for Mobile communications) sensors, and the municipality aims to use LPWAN in order to improve range and reduce costs and provisioning times when changing the communications provider. After an initial study, LoRa (Long Range) and LoRAWAN (LoRa Wide Area Network) as its network counterpart, were selected as the LPWAN technology for trials considering several use cases, exploring multiple distances, types of recycling waste containers, placements (underground or surface) and kinds of commercially available waste level measurement LoRa sensors. The results showed that the underground waste containers proved to be, as expected, the most difficult to operate correctly, where the container itself imposed attenuation levels of 26 dB on the LoRa link budget. The successful results were used to promote the deployment of a city-wide LoRa network, available to all the departments inside the Lisbon City Council. Considering the network capacity, the municipality also decided to make the network freely available to citizens. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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Review

Jump to: Research

20 pages, 4814 KiB

Open AccessReview

A Step toward Next-Generation Advancements in the Internet of Things Technologies

by Farhan Amin, Rashid Abbasi, Abdul Mateen, Muhammad Ali Abid and Salabat Khan

Sensors 2022, 22(20), 8072; https://0-doi-org.brum.beds.ac.uk/10.3390/s22208072 - 21 Oct 2022

Cited by 18 | Viewed by 3090

Abstract

The Internet of Things (IoT) devices generate a large amount of data over networks; therefore, the efficiency, complexity, interfaces, dynamics, robustness, and interaction need to be re-examined on a large scale. This phenomenon will lead to seamless network connectivity and the capability to provide support for the IoT. The traditional IoT is not enough to provide support. Therefore, we designed this study to provide a systematic analysis of next-generation advancements in the IoT. We propose a systematic catalog that covers the most recent advances in the traditional IoT. An overview of the IoT from the perspectives of big data, data science, and network science disciplines and also connecting technologies is given. We highlight the conceptual view of the IoT, key concepts, growth, and most recent trends. We discuss and highlight the importance and the integration of big data, data science, and network science along with key applications such as artificial intelligence, machine learning, blockchain, federated learning, etc. Finally, we discuss various challenges and issues of IoT such as architecture, integration, data provenance, and important applications such as cloud and edge computing, etc. This article will provide aid to the readers and other researchers in an understanding of the IoT’s next-generation developments and tell how they apply to the real world. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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46 pages, 3897 KiB

Open AccessReview

A Comparative Analysis on Blockchain versus Centralized Authentication Architectures for IoT-Enabled Smart Devices in Smart Cities: A Comprehensive Review, Recent Advances, and Future Research Directions

by Usman Khalil, Owais Ahmed Malik, Mueen Uddin and Chin-Ling Chen

Sensors 2022, 22(14), 5168; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145168 - 10 Jul 2022

Cited by 13 | Viewed by 4002

Abstract

Smart devices have become an essential part of the architectures such as the Internet of Things (IoT), Cyber-Physical Systems (CPSs), and Internet of Everything (IoE). In contrast, these architectures constitute a system to realize the concept of smart cities and, ultimately, a smart planet. The adoption of these smart devices expands to different cyber-physical systems in smart city architecture, i.e., smart houses, smart healthcare, smart transportation, smart grid, smart agriculture, etc. The edge of the network connects these smart devices (sensors, aggregators, and actuators) that can operate in the physical environment and collects the data, which is further used to make an informed decision through actuation. Here, the security of these devices is immensely important, specifically from an authentication standpoint, as in the case of unauthenticated/malicious assets, the whole infrastructure would be at stake. We provide an updated review of authentication mechanisms by categorizing centralized and distributed architectures. We discuss the security issues regarding the authentication of these IoT-enabled smart devices. We evaluate and analyze the study of the proposed literature schemes that pose authentication challenges in terms of computational costs, communication overheads, and models applied to attain robustness. Hence, lightweight solutions in managing, maintaining, processing, and storing authentication data of IoT-enabled assets are an urgent need. From an integration perspective, cloud computing has provided strong support. In contrast, decentralized ledger technology, i.e., blockchain, light-weight cryptosystems, and Artificial Intelligence (AI)-based solutions, are the areas with much more to explore. Finally, we discuss the future research challenges, which will eventually help address the ambiguities for improvement. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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34 pages, 820 KiB

Open AccessReview

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

by Abbas Shah Syed, Daniel Sierra-Sosa, Anup Kumar and Adel Elmaghraby

Sensors 2022, 22(12), 4380; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124380 - 09 Jun 2022

Cited by 8 | Viewed by 3345

Abstract

One of the prime aims of smart cities has been to optimally manage the available resources and systems that are used in the city. With an increase in urban population that is set to grow even faster in the future, smart city development has been the main goal for governments worldwide. In this regard, while the useage of Artificial Intelligence (AI) techniques covering the areas of Machine and Deep Learning have garnered much attention for Smart Cities, less attention has focused towards the use of combinatorial optimization schemes. To help with this, the current review presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things (IoT). A mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. This review will help researchers by providing them a consolidated starting point for research in the domain of smart city application optimization. Full article

(This article belongs to the Special Issue IoT Enabling Technologies for Smart Cities: Challenges and Approaches)

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