Intelligent user-collaborative edge device APC-based MEC 5G IoT for computational offloading and resource allocation

Chidiebere Sunday Chidume, Christantus O. Nnamani

Research output: Contribution to journalArticlepeer-review

Abstract

Mobile edge computing (MEC) supports delay-sensitive and excellent processing capacity services on the 5G Internet of Things (IoT) network. This research proposes an intelligent resource allocation policy to minimize average service latency and average energy consumption for an IoT system while maximizing the available processing capacity (APC). We express the APC as a function of communication and computation resources at the user and task edge devices. We demonstrated that reducing execution delay and energy usage could improve overall system service performance. We evaluate the savings in average latency and average energy consumption when we reduce execution delay by allocating resources to maximize APC. The 5G IoT network uses natural actor-critic deep reinforcement learning to tackle complicated resource allocation decisions, and the simulation shows that reducing execution time improves overall system performance. Our results improved execution time and energy usage compared to random search, greedy search, and deep Q-learning. In addition, our single centralized agent DRL outperforms Multi-agent DRL for the number of rewards and completed task achievable under different episodes.

Original languageEnglish
Pages (from-to)286-300
Number of pages15
JournalJournal of Parallel and Distributed Computing
Volume169
Early online date22 Jul 2022
DOIs
Publication statusE-pub ahead of print - 22 Jul 2022

Keywords

  • Available processing capacity (APC)
  • Internet of Things (IoT)
  • Mobile edge computing (MEC)
  • Natural actor-critic deep reinforcement learning
  • Resource allocation

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications

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