Special Issues
Table of Content

Edge Computing in Advancing the Capabilities of Smart Cities

Submission Deadline: 03 April 2024 (closed) View: 12

Guest Editors

Dr. Peiying Zhang, China University of Petroleum (East China), China
Prof. Mohsen Guizani, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), United Arab Emirates
Dr. Laith Abualigah, Al Al-Bayt University, Jordan
Dr. Alireza Goli, University of Isfahan, Iran


The rapid growth of Information and Communication Technologies (ICT) has enabled the socio and economic development of smart cities with a wide range of automated devices. Due to increased population and urbanization, smart cities have been initiated by various governments to meet the sustainable development goal of the UN and it provides citizens comfort and better quality of life.  Smart city adds intelligence to the urban world through a wireless network of connected sensors and devices in order to solve public problems. The massive deployment of IoT sensors and devices generally collects the data from various locations of smart cities and then it is transmitted to the cloud storage over the internet for further analysis and processing. However, processing the data every time from the centralized storage system becomes difficult and there may be challenges such as network latency, increased cost, and delay. Since these devices are resource-constrained in nature, edge computing has been adopted to the smart environments which can process the data closer to the source of data (at the device location) without the need of sending it into the cloud. In edge computing, devices are typically built-in with persistent storage resources and analytics capabilities. The major benefits are increased efficiency, faster data processing, reduced network latency, improved decision making, scalability, mobility support, and cost-effectiveness. Most importantly, edge devices can process and store the data locally even if network access is unreliable. Hence, edge computing has become crucial for a real-time application like smart cities as it requires low latency devices with quick responses.


Edge computing plays a vital role in streamlining the physical infrastructure of smart cities such as transportation, water resources management, energy, waste management, smart home automation, city parking system, weather monitoring system, environment monitoring, and telecommunication. For instance, in smart transportation, edge computing helps to optimize traffic signal controls in order to reduce traffic congestion and the amount of waiting time. Moreover, smart surveillance cameras play a major role in the detection of operational hazards in densely populated areas in the city.  In smart water management, it can detect water leakage and send an immediate notification to the concerned authority to fix the problem. The application of edge computing and low-cost sensors provides a method for more efficient waste management by monitoring the garbage level throughout the city. As more and more IoT devices generate a vast amount of data, out of which only a small amount of data is useful. Therefore, it is essential to apply data filtering and adding intelligence to the edge device that runs analytical algorithms for efficient data processing.


However, the implementation of edge computing for smart cities has some significant challenges such as security breaches, data losses, limitations of the hardware. In this blog, we intend to bring out the role of edge computing devices in smart cities to improve the well-being and quality of life of citizens. Furthermore, the future research direction is to design and develop energy-efficient, low latency, low-cost, and security-enhanced edge computing devices for smart cities. We welcome researchers and practitioners to present their novel contributions in this regard.  


List of interested topics include, but not limited to:

Edge computing architecture, frameworks, and applications.

Edge computing for smart IoT networks.

Wireless networking architecture and communication protocols for edge computing in smart networks.

Edge computing enabled smart cities: Benefits and risks.

Role of edge enabled IoT networks for transport optimization.

Convergence of IoT, cloud computing, and edge computing for smart cities.

Energy-efficient and low-latency communication edge devices for smart cities.

Security and privacy challenges of edge computing in the context of smart cities.

Novel techniques and future perspective of edge computing.

Improved route planning through edge computing in smart transport management.

Role of edge enabled AI for real-time traffic monitoring applications.

Smart water management system using edge computing and IoT.

Dynamic resource allocation and management of edge computing devices in a smart city environment.

Role of edge enabled IoT sensors for environment monitoring and control.


Semantic Computing, Future Network Architecture, Deep Reinforcement Learning, UAV, Edge Computing, Wireless Network, Blockchain, Internet of Things

Published Papers

  • Open Access


    A Unified Model Fusing Region of Interest Detection and Super Resolution for Video Compression

    Xinkun Tang, Feng Ouyang, Ying Xu, Ligu Zhu, Bo Peng
    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3955-3975, 2024, DOI:10.32604/cmc.2024.049057
    (This article belongs to the Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)
    Abstract High-resolution video transmission requires a substantial amount of bandwidth. In this paper, we present a novel video processing methodology that innovatively integrates region of interest (ROI) identification and super-resolution enhancement. Our method commences with the accurate detection of ROIs within video sequences, followed by the application of advanced super-resolution techniques to these areas, thereby preserving visual quality while economizing on data transmission. To validate and benchmark our approach, we have curated a new gaming dataset tailored to evaluate the effectiveness of ROI-based super-resolution in practical applications. The proposed model architecture leverages the transformer network framework,… More >

  • Open Access


    Safety-Constrained Multi-Agent Reinforcement Learning for Power Quality Control in Distributed Renewable Energy Networks

    Yongjiang Zhao, Haoyi Zhong, Chang Cyoon Lim
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 449-471, 2024, DOI:10.32604/cmc.2024.048771
    (This article belongs to the Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)
    Abstract This paper examines the difficulties of managing distributed power systems, notably due to the increasing use of renewable energy sources, and focuses on voltage control challenges exacerbated by their variable nature in modern power grids. To tackle the unique challenges of voltage control in distributed renewable energy networks, researchers are increasingly turning towards multi-agent reinforcement learning (MARL). However, MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase. This unpredictability can lead to unsafe control measures. To mitigate these safety concerns in MARL-based voltage control, our study introduces a novel… More >

  • Open Access


    Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

    Kai Wei, Song Yu, Qingxian Pan
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 607-622, 2024, DOI:10.32604/cmc.2024.048240
    (This article belongs to the Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)
    Abstract Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation. While traditional methods for task allocation can help reduce costs and improve efficiency, they may encounter challenges when dealing with abnormal data flow nodes, leading to decreased allocation accuracy and efficiency. To address these issues, this study proposes a novel two-part invalid detection task allocation framework. In the first step, an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data. Compared to the baseline method, the model achieves an approximately 4% increase in the F1 value More >

  • Open Access


    Smartphone-Based Wi-Fi Analysis for Bus Passenger Counting

    Mohammed Alatiyyah
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 875-907, 2024, DOI:10.32604/cmc.2024.047790
    (This article belongs to the Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)
    Abstract In the contemporary era of technological advancement, smartphones have become an indispensable part of individuals’ daily lives, exerting a pervasive influence. This paper presents an innovative approach to passenger counting on buses through the analysis of Wi-Fi signals emanating from passengers’ mobile devices. The study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels, thereby addressing a crucial aspect of public transportation. The proposed system comprises three crucial elements: Signal capture, data filtration, and the calculation and estimation of passenger numbers. The pivotal findings reveal that the system demonstrates commendable… More >

Share Link