Computation Offloading and Resource Allocation in IoT-Based Mobile Edge Computing Systems

Bintao Hu*, Yuan Gao, Wenzhang Zhang, Dongyao Jia, Hengyan Liu

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

With the rise in popularity of artificial intelligence (AI) and internet of things (IoT) technologies, advanced AI technologies have been widely applied to support delay/time-sensitive tasks of IoT-based user equipment (UE) in IoT systems, which allows IoT-based UEs to offload their tasks to a remote fog, edge or cloud computing server. To reduce the consumption of delays (which may include transmission delays, queueing delays, and processing delays) while efficiently allocating the computation resource at a remote server, an efficient offloading decision solution needs to be proposed. In this paper, an IoT-based network system consisting of two layers will be proposed, where The bottom layer is the IoT-based UE layer, which includes multiple IoT-based UEs, and the top layer is the mobile edge computing (MEC) layer, which includes an edge node embedded with the base station. We propose a double Q-Learning-based offloading decision and computation resource allocation optimisation algorithm (DQOCA), which aims to jointly optimise the offloading decisions among all IoT-based UEs and optimise computation resource at the MEC server to reduce the maximum delay consumption among all IoT-based UEs. Simulation findings show that, in comparison to benchmarks (i.e., local processing and edge processing schemes), our proposed approach greatly minimises the maximum delay consumption.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Smart Internet of Things, SmartIoT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages119-123
Number of pages5
ISBN (Electronic)9798350316575
DOIs
Publication statusPublished - 1 Nov 2023
Event7th IEEE International Conference on Smart Internet of Things, SmartIoT 2023 - Xining, China
Duration: 25 Aug 202327 Aug 2023

Publication series

NameProceedings - 2023 IEEE International Conference on Smart Internet of Things, SmartIoT 2023

Conference

Conference7th IEEE International Conference on Smart Internet of Things, SmartIoT 2023
Country/TerritoryChina
CityXining
Period25/08/2327/08/23

Keywords

  • double Q-learning
  • edge computing
  • Internet of Things
  • resource allocation

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