Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

Abstract

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. In this paper, we study a multicriteria optimization approach for resource allocation with distributed edge computing in IoT-based smart cities. We present a three-layer network architecture for IoT-based smart cities. An edge resource allocation scheme based on an auctionable approach is proposed to ensure efficient resource computation for delay-sensitive tasks.

5G Technology based Edge Computing in UAV Networks for Resource Allocation with Routing using Federated Learning Access Network and Trajectory Routing Protocol

“…The goal of resource management in MEC is to minimise system latency [7], energy consumption [8], and overall method latency and/or energy consumption costs [9]. In [10], the tradeoff problem is examined for computing networks with fog node cooperation with goal of reducing fog node reaction time within a specified power efficiency restriction.In order to reduce computation delay while maintaining a low overall computation energy consumption, work [11] studied joint service caching as well as task offloading problem in dense network.With goal of reducing the overall job duration while adhering to energy budget restrictions, author [12] looked into the MEC task offloading issue in software-described ultra-dense network.For minimising system latency of all mobile devices, author in [13] developed a joint communication as well as computation RAissue under collaboration of CC and EC.In order to investigate energy-delay tradeoff dilemma in a MECCmethod, work [14] developed a multiuser evaluation offloading game.In order to reduce total energy cost as well as less delay among all users, author [15] jointly optimised the offloading decisions of all users as well as resource allocation (RA).To reduce overhead of local energy consumption as well as simulation time costs, work [16] presented a distributed joint computation offloading as well as RA optimization strategy in heterogeneous networks with MEC. Particularly, case where number of MUs enhances explosively or network facilities are sparsely dispersed does not apply to the existing MEC approaches [17].Authors in [18] have been summarized journey of ML in the last thirty years and roles for the next generation wireless network as road for best optimization technique.…”

Centralized Cloud Service Providers in Improving Resource Allocation and Data Integrity by 4G IoT Paradigm

“…Consider that there exist n IoT devices and several delay-sensitive processes at UAV or IoT or cloud levels should be locally tackled [18]. 𝑈 𝑖 task generated in an IoT scheme (𝐷 𝑖 ), whereas 𝑖 = 1,2, … , 𝑛, has the subsequent features: task size (𝑈 𝑠𝑧𝑖 ), (𝐸 𝑖,𝑙 ) energy consumed or CPU cycle, computation capability f 𝑖 ) concerning deadline (𝑑𝑙 𝑖 )and CPU cycles or seconds.…”

“…report a DEL examination of the LAARSS-DI and existing techniques under varying tasks[18,19]. The results inferred that the LAARSS-DI system has achieved effectual results with minimal DEL values.…”

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

“…Therefore, cloud computing can be considered as the most suitable platform that provides solutions for the existing smart city applications (e.g., smart healthcare, smart transport and street lighting, smart environmental monitoring, and smart waste management). Usually, in the smart city, various IoT devices such as driverless cars, mobiles, and surveillance devices are connected to the cloud to use their computing services, which puts a huge load on the cloud networks to meet all the requirements of the IoT applications [1]. This could lead to a poor user experience due to the delays in the execution of IoT applications.…”

“…This could lead to a poor user experience due to the delays in the execution of IoT applications. As a result, cloud performance in the context of smart cities may suffer from different issues, such as service latency, load balancing, resource optimization, energy consumption, and cost optimization [1,2].…”