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Mahmood, O.A.;  Abdellah, A.R.;  Muthanna, A.;  Koucheryavy, A. Distributed Edge Computing in IoT-Based Smart Cities. Encyclopedia. Available online: (accessed on 17 June 2024).
Mahmood OA,  Abdellah AR,  Muthanna A,  Koucheryavy A. Distributed Edge Computing in IoT-Based Smart Cities. Encyclopedia. Available at: Accessed June 17, 2024.
Mahmood, Omar Abdulkareem, Ali R. Abdellah, Ammar Muthanna, Andrey Koucheryavy. "Distributed Edge Computing in IoT-Based Smart Cities" Encyclopedia, (accessed June 17, 2024).
Mahmood, O.A.,  Abdellah, A.R.,  Muthanna, A., & Koucheryavy, A. (2022, July 20). Distributed Edge Computing in IoT-Based Smart Cities. In Encyclopedia.
Mahmood, Omar Abdulkareem, et al. "Distributed Edge Computing in IoT-Based Smart Cities." Encyclopedia. Web. 20 July, 2022.
Distributed Edge Computing in IoT-Based Smart Cities

Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their various quality of service (QoS) constraints, servers face the challenge of allocating limited resources across all Internet-based applications to achieve an efficient per-formance. The presence of a cloud in the IoT system of a smart city results in high energy con-sumption and delays in the network. Edge computing is based on a cloud computing framework where computation, storage, and network resources are moved close to the data source. The IoT framework is identical to cloud computing. The critical issue in edge computing when executing tasks generated by IoT systems is the efficient use of energy while maintaining delay limitations.

5G IoT edge computing auctionable approach

1. Introduction

Smart cities are technologically advanced metropolitan areas that use information and communication technology to solve their particular challenges and to help implement sustainable social, economic, or environmental development. In recent years, transforming urban areas into smart cities has become an objective for many cities worldwide, which have developed specific strategies that can be used in different ways to achieve this goal. Deploying 5G technology as part of a smart city approach enhances productivity and the standard of living and reduces costs and resource consumption. The significant advantages of intelligent services are that they provide more accommodation, shared infrastructure, resources, and flexibility, improved quality of service (QoS), and quality of experience (QoE) methods, depending on the required service and infrastructure location. These services improve citizens’ quality of life and provide a better user experience [1][2].
IoT systems at home benefit energy demand and make life more comfortable and enjoyable. In an IoT-based smart city, many facilities are controlled by IoT devices, and the various resources required to run the services are deployed using distant cloud services. The QoS conditions for deployment of diverse, intelligent city services include excellent computation capability, exact delay limitations, efficient power use, etc.; i.e., there is a hard limit on the tolerable delay in virtual reality. In contrast, in video monitoring, the unavoidable delay is adaptable. Moreover, if the processing of a task in an intelligent traffic application exceeds the desired time, an accident may occur. IoT-based smart cities comprise a variety of use cases, such as smart home IoT systems that have beneficial effects on energy consumption and provide a more relaxed and enjoyable life. Smart cities use many IoT systems to control many facilities, and the various resources required to run the facilities are stored on remote cloud servers. Intelligent buildings help improve occupant comfort and reduce unnecessary energy consumption, e.g., in banks, businesses, shopping malls, restaurants, universities, etc. Intelligent medical systems permit physicians to observe diseases by utilizing smart devices anywhere. This process lowers the cost of medical treatment and enhances the physician and patient experience. Intelligent implementation of transportation, vehicle identification, and route monitoring improve cities’ transportation network security and capability [3][4][5][6].
The growth of IoT systems and the variety of intelligent city-based IoT applications, with their diverse QoS requirements, make it difficult to operate these systems efficiently [7]. The large amounts of information produced by these systems make withdrawing money from a remote data center costly and inefficient. The information produced by IoT systems and transmitted over the internet to a distant cloud requires an abundance of bandwidth and power in the IoT network. Furthermore, this leads to an increased load on the cellular network as these systems access cloud servers. Using core cloud services over a cellular network can result in restrictions for IoT application areas requiring lower latency and higher efficiency. This highlights the significance of edge computing patterns with MEC quality. The basic idea of MEC is to increase cloud capacity at the edges of mobile networks. Theoretically, this can be achieved by locating storage and computing resources at the edge of the radio access network (RAN) on demand; some of the capabilities provided by the cloud are shifted to these extra resources at the edge. Standard features of MEC technology include extremely low latency, location information, and background information about the network. The typical cloud computing model encounters difficulties, such as communication costs and undesirable delays, because of the restricted computing capacity of intelligent IoT systems and the remotely accessed cloud server. These challenges affect delayed operations and applications involving IoT sensing devices. One primary concern is to avoid long delays during the execution of IoT systems due to the problem of information congestion in IoT-enabled smart cities [8][9][10].
The RAN is an essential element of a wireless telecommunications system connecting various devices to other network parts through a radio link. The RAN connects client devices, such as cell phones, computers, or remotely controlled machines, via a fiber-optic or wireless backhaul link. This connection links to the core network, which manages subscriber information and location data, among other things [11]. Modern computational techniques are an appropriate solution in this context, as they allow storage and computational resources to be moved closer to IoT systems, as represented by base stations (BSs). The edge computing used in UAVs [12], a fundamental level in the IoT framework, reduces the delay between IoT systems and the computational backend infrastructure (mini-cloud). The UAV level adjacent to the IoT system offers more minor delays; however, it offers fewer resources than the use of BSs and a mini-cloud. Therefore, an effective, modern edge resource allocation system ensuring QoS for various smart city use cases is required. Auctionable methods are appropriate for dealing with the difficulty of allocating multiple conditional resources. Allocating resources in environmental IoT systems requires numerous entities, such as virtual machines (VMs), to complete the most significant tasks and undertake VM distribution. Moreover, the balance point of the simulation game may be superior resource allocation for specific tasks and VMs, where original resource allocation optimization occurs locally at each edge’s site, balancing the minimization of energy consumption and delay for QoS assurance. A distributed technique can be applied to forward the overflow workload, thus balancing resource usage and achieving superior resource management [13][14].

2. Distributed Edge Computing in Smart Cities Based on the IoT

Many researchers have proposed edge solutions for resource allocation problems, including energy consumption and service delays, in smart cities based on IoT systems to make people’s lives comfortable. Some of these solutions are described below.
Yang et al. [15] studied the effective use of energy for resource distribution in a UAV-based MEC network and solved the network’s energy reduction issues. Energy efficiency in a UAV-based MEC was investigated in [16] through a functional migration method and route planning. In [17], edge resources were allocated to minimize average latency while numerous IoT systems powered multiple smart city facilities and met the edge server capacity limits. Another article [18] proposed an algorithm for offloading tasks with low complexity and balancing energy efficiency based on two-sided correspondence in an IoT system; the suggested method is innovative in dynamically balancing energy efficiency and delay and stably offloading tasks with low complexity.
Abdullah et al. [19] investigated the influence of the edge network’s computation offloading framework on original applications and proposed a light-effect migration-based framework for computation offloading. Another article [20] proposed a resource-based task allocation method and executed and examined it to obtain enhanced efficiency in a heterogeneous multi-cloud network. The suggested task distribution method reduced the consumed energy and decreased the time between tasks. A joint algorithm for consumer choice and resource allocation in MEC has been proposed to optimize energy capacity [21]. MEC offers cloud services at the edge of the cellular network. With ultra-low latency and significant bandwidth, the use of edge computing to support IoT systems is the backbone for advancing intelligent systems and use cases, such as smart homes, competent healthcare, intelligent traffic control, intelligent farming, and smart cities [22].
Anagnostopoulos et al. [23] examined specific techniques for offloading computational power at the network edge to self-computing nodes, a context characterized by insufficient resources and many restrictions that can be violated when performing analytical tasks. Chen et al. [24] proposed a model that could improve the allocation of system resources and efficiently optimize the objective function and customer satisfaction for computation offloading methods in edge computing networks, considering UAV transmission and clean energy.
Attiya et al. [25] suggested another task scheduler for managing IoT application tasks using a CCE. Specifically, they suggested a new hybrid swarm intelligence method, based on a modified manta ray foraging optimization (MRFO) algorithm and the salp swarm algorithm (SSA), to process the scheduling of IoT tasks in cloud computing. Sahraoui et al. [26] discussed the role of IoT systems in social relationship management, including the issue of the bursting of social relationships in IoT systems, and reviewed the suggested ASI-based solutions, such as social-oriented machine-learning and deep-learning techniques. A modified Harris hawks optimization (HHO) algorithm using simulated annealing (SA) for scheduling jobs in the cloud environment was presented in [27]. Iman El-Dessouki et al. [28] focused on combining a smart grid with smart cities with 5G technology and demonstrated the benefits of network slicing and of adopting virtual MPN systems for smart cities. Aamir Anwar et al. [29] proposed a framework for achieving an optimal solution for smart, secure parking that has broader implications for applying 5G in smart cities.


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