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
Summary
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.
Keywords
Semantic Computing, Future Network Architecture, Deep Reinforcement Learning, UAV, Edge Computing, Wireless Network, Blockchain, Internet of Things
Published Papers
Login
Register
Submit a Paper
Propose a Special lssue