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Joint Offloading and Resource Allocation Based on UAV-Assisted Mobile Edge Computing

Joint Offloading and Resource Allocation Based on UAV-Assisted Mobile Edge Computing Due to the birth of various new Internet of Things devices, the rapid increase of users, and the limited coverage of infrastructure, computing resources will inevitably become insufficient. Therefore, we consider an unmanned aerial vehicle (UAV)–assisted mobile edge computing system with multiple users, an edge server, a remote cloud server, and an UAV. A UAV, as a relay node, can provide users with extensive communications and certain computing capabilities. Our proposed scheme aims to optimize the unloading decision of the tasks among all users and the allocation of computing and communication resources to minimize overall energy consumption and costs of computing and maximum delay. To solve the joint optimization problem, we propose an efficient USS algorithm, which includes a UAV position optimization algorithm, semi-qualitative relaxation method, and self-adaptive adjustment method. Our numerical results show that the proposed algorithm can significantly reduce the unloading cost of multi-user tasks compared with four other unloading decisions, such as traditional cloud computing, which uses only the edge server. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Sensor Networks (TOSN) Association for Computing Machinery

Joint Offloading and Resource Allocation Based on UAV-Assisted Mobile Edge Computing

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2022 Association for Computing Machinery.
ISSN
1550-4859
eISSN
1550-4867
DOI
10.1145/3476512
Publisher site
See Article on Publisher Site

Abstract

Due to the birth of various new Internet of Things devices, the rapid increase of users, and the limited coverage of infrastructure, computing resources will inevitably become insufficient. Therefore, we consider an unmanned aerial vehicle (UAV)–assisted mobile edge computing system with multiple users, an edge server, a remote cloud server, and an UAV. A UAV, as a relay node, can provide users with extensive communications and certain computing capabilities. Our proposed scheme aims to optimize the unloading decision of the tasks among all users and the allocation of computing and communication resources to minimize overall energy consumption and costs of computing and maximum delay. To solve the joint optimization problem, we propose an efficient USS algorithm, which includes a UAV position optimization algorithm, semi-qualitative relaxation method, and self-adaptive adjustment method. Our numerical results show that the proposed algorithm can significantly reduce the unloading cost of multi-user tasks compared with four other unloading decisions, such as traditional cloud computing, which uses only the edge server.

Journal

ACM Transactions on Sensor Networks (TOSN) – Association for Computing Machinery

Published: Apr 18, 2022

Keywords: Mobile edge computing

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