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Smart Cities of the Future: Harnessing the Power of IoT, Blockchain, Machine Learning, and Digital Twin Technologies

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A special issue of Smart Cities (ISSN 2624-6511).

Deadline for manuscript submissions: 1 July 2024 | Viewed by 8877

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Special Issue Editors

Dr. Faisal Jamil

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Guest Editor

Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway
Interests: Internet of Things; blockchain; big data; machine learning; indoor navigation; digital twins; metaverse
Special Issues, Collections and Topics in MDPI journals

Dr. Zeinab Shahbazi

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Guest Editor

School of Information Technology, Halmstad University, Halmstad, ‎Sweden
Interests: machine learning; NLP; knowledge discovery; data mining; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the transformative potential of integrating IoT, blockchain, machine learning, and digital twin technologies, along with supply chain, hydrogen fuel, renewable energies, energy management, and indoor navigation, in the development and operation of smart cities. The convergence of these cutting-edge technologies holds potential for the creation of intelligent urban environments that enhance sustainability, efficiency, and the overall quality of life of citizens. This Special Issue will bring together researchers, practitioners, and policymakers to discuss the latest advancements, challenges, and opportunities in this multidisciplinary field. By shedding light on innovative applications, theoretical frameworks, and practical implementations, this Special Issue seeks to provide valuable insights and guidance for stakeholders involved in building the cities of tomorrow.

The scope of interest of this Special Issue includes, but is not limited to, the following topics:

Integration of IoT, blockchain, machine learning, and digital twin technologies for smart cities;
Data-driven decision making and predictive analytics in urban planning and management;
Smart infrastructure and resource management enabled by these technologies;
Security, privacy, and trust considerations in smart city systems;
Citizen engagement and participatory approaches using IoT, blockchain, and machine learning;
Real-time monitoring and control systems for urban mobility and transportation;
Energy-efficient and sustainable solutions for smart cities through advanced technologies;
Challenges and solutions in interoperability and standardization of smart city ecosystems;
Ethical and social implications of IoT, blockchain, machine learning, supply chain, hydrogen fuel, renewable energies, energy management, and digital twin technologies in urban environments;
Indoor navigation and location-based services for enhancing urban experiences and efficiency.

Dr. Faisal Jamil
Dr. Zeinab Shahbazi
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. Smart Cities 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 2000 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

smart cities
Internet of Things (IoT)
blockchain
machine learning
digital twin
urban management
data-driven technologies

Published Papers (4 papers)

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Research

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33 pages, 10626 KiB

Open AccessArticle

Secure Hydrogen Production Analysis and Prediction Based on Blockchain Service Framework for Intelligent Power Management System

by Harun Jamil, Faiza Qayyum, Naeem Iqbal, Murad Ali Khan, Syed Shehryar Ali Naqvi, Salabat Khan and Do Hyeun Kim

Smart Cities 2023, 6(6), 3192-3224; https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities6060142 - 22 Nov 2023

Viewed by 1614

Abstract

The rapid adoption of hydrogen as an eco-friendly energy source has necessitated the development of intelligent power management systems capable of efficiently utilizing hydrogen resources. However, guaranteeing the security and integrity of hydrogen-related data has become a significant challenge. This paper proposes a pioneering approach to ensure secure hydrogen data analysis by integrating blockchain technology, enhancing trust, transparency, and privacy in handling hydrogen-related information. Combining blockchain with intelligent power management systems makes the efficient utilization of hydrogen resources feasible. Using smart contracts and distributed ledger technology facilitates secure data analysis (SDA), real-time monitoring, prediction, and optimization of hydrogen-based power systems. The effectiveness and performance of the proposed approach are demonstrated through comprehensive case studies and simulations. Notably, our prediction models, including ABiLSTM, ALSTM, and ARNN, consistently delivered high accuracy with MAE values of approximately 0.154, 0.151, and 0.151, respectively, enhancing the security and efficiency of hydrogen consumption forecasts. The blockchain-based solution offers enhanced security, integrity, and privacy for hydrogen data analysis, thus advancing clean and sustainable energy systems. Additionally, the research identifies existing challenges and outlines future directions for further enhancing the proposed system. This study adds to the growing body of research on blockchain applications in the energy sector, specifically on secure hydrogen data analysis and intelligent power management systems. Full article

(This article belongs to the Special Issue Smart Cities of the Future: Harnessing the Power of IoT, Blockchain, Machine Learning, and Digital Twin Technologies)

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19 pages, 1229 KiB

Open AccessArticle

Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

by Zeinab Shahbazi and Slawomir Nowaczyk

Smart Cities 2023, 6(5), 2574-2592; https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities6050116 - 27 Sep 2023

Viewed by 1585

Abstract

In urban settings, the prevalence of traffic lights often leads to fluctuations in traffic patterns and increased energy utilization among vehicles. Recognizing this challenge, this research addresses the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs) through the introduction of a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD). This innovative strategy is designed to enhance various aspects of urban mobility, including vehicle energy efficiency, traffic flow optimization, and battery longevity, all while ensuring a satisfactory driving experience. The M-EAD strategy unfolds in two distinct stages: First, it optimizes eco-friendly green signal windows at traffic lights, with a primary focus on minimizing travel delays by solving the shortest path problem. Subsequently, it employs a receding horizon framework and leverages an iterative dynamic programming algorithm to refine speed trajectories. The overarching objective is to curtail energy consumption and reduce battery wear by identifying the optimal speed trajectory for EVs in urban environments. Furthermore, the research substantiates the real-world efficacy of this approach through on-road vehicle tests, attesting to its viability and practicality in actual road scenarios. In the proposed case, the simulation results showcase notable achievements, with energy consumption reduced by 0.92% and battery wear minimized to a mere 0.0017%. This research, driven by the pressing issue of urban traffic energy efficiency, not only presents a solution in the form of the M-EAD strategy but also contributes to the fields of sustainable urban mobility and EV performance optimization. By tackling the challenges posed by traffic lights, this work offers valuable insights and practical implications for improving the sustainability and efficiency of urban transportation systems. Full article

(This article belongs to the Special Issue Smart Cities of the Future: Harnessing the Power of IoT, Blockchain, Machine Learning, and Digital Twin Technologies)

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15 pages, 24156 KiB

Open AccessArticle

Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment

by Muhammad Nadeem, Naqqash Dilshad, Norah Saleh Alghamdi, L. Minh Dang, Hyoung-Kyu Song, Junyoung Nam and Hyeonjoon Moon

Smart Cities 2023, 6(5), 2245-2259; https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities6050103 - 28 Aug 2023

Cited by 3 | Viewed by 1682

Abstract

The recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resource-constraint devices rather than real-time fire detection in smart city contexts. To overcome this shortcoming, we presented a novel efficient lightweight network called FlameNet for fire detection in a smart city environment. Our proposed network works via two main steps: first, it detects the fire using the FlameNet; then, an alert is initiated and directed to the fire, medical, and rescue departments. Furthermore, we incorporate the MSA module to efficiently prioritize and enhance relevant fire-related prominent features for effective fire detection. The newly developed Ignited-Flames dataset is utilized to undertake a thorough analysis of several convolutional neural network (CNN) models. Additionally, the proposed FlameNet achieves 99.40% accuracy for fire detection. The empirical findings and analysis of multiple factors such as model accuracy, size, and processing time prove that the suggested model is suitable for fire detection. Full article

(This article belongs to the Special Issue Smart Cities of the Future: Harnessing the Power of IoT, Blockchain, Machine Learning, and Digital Twin Technologies)

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Review

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22 pages, 2242 KiB

Open AccessReview

A Review of Parking Slot Types and their Detection Techniques for Smart Cities

by Kamlesh Kumar, Vijander Singh, Linesh Raja and Swami Nisha Bhagirath

Smart Cities 2023, 6(5), 2639-2660; https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities6050119 - 02 Oct 2023

Viewed by 3080

Abstract

Smart parking system plays a critical role in the overall development of the cities. The capability to precisely detect an open parking space nearby is necessary for autonomous vehicle parking for smart cities. Finding parking spaces is a big issue in big cities. Many of the existing parking guidance systems use fixed IoT sensors or cameras that are unable to offer information from the perspective of the driver. Accurately locating parking spaces can be difficult since they come in a range of sizes and colors that are blocked by objects that seem different depending on the environmental lighting. There are numerous auto industry players engaged in the advanced testing of driverless cars. A vacant parking space must be found, and the car must be directed to park there in order for the operation to succeed. The machine learning-based algorithms created to locate parking spaces and techniques and methods utilizing dashcams and fish-eye cameras are reviewed in this study. In response to the increase in dashcams, neural network-based techniques are created for identifying open parking spaces in dashcam videos. The paper proposed the review of the existing parking slot types and their detection techniques. The review will highlight the importance and scope of a smart parking system for smart cities. Full article

(This article belongs to the Special Issue Smart Cities of the Future: Harnessing the Power of IoT, Blockchain, Machine Learning, and Digital Twin Technologies)

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