Deep Reinforcement Learning based Green Resource Allocation Mechanism in Edge Computing driven Power Internet of Things

Mo Yang, Peng Yu, Ying Wang, Xiuli Huang, Weiwei Miu, Pengfei Yu, Wei Li, Ruxia Yang, Minxing Tao, Lei Shi

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

7 Citations (Scopus)

Abstract

Smart grid deploys a large number of smart terminals and sensing devices to form an edge network, as well as a virtual network of information space and the power Internet of Things. As a key component of 5G and future network, the latency of end-to-end and the traffic of backhaul link could be reduced by edge network. Nevertheless, the function of storage and computing are moved down to the edge nodes in mobile edge network which increases the complexity of resource management. So it is an important issue to find out a more effectively resources allocation mechanism as well as meeting the requirements of each user. Edge computing refers to the processing of large amounts of edge data in the edge space in the edge network, thereby reducing dependence on the data center, achieving limited self-governance of the edge network, and reducing off-line threats. Although Deep Reinforcement Learning (DRL) has been applied to many of the work related to edge networks, there lacks the applications for green resource allocation. A Deep Reinforcement Learning (DRL) based green resource allocation mechanism is proposed in this paper which aims at efficiently allocating the resources while satisfying the needs of mobile users. The value of energy efficiency can be obtained when the algorithm achieves convergence according to the simulation results. The efficiency of the DRL-based mechanism and its effectiveness in meeting user requirements and implementing green resource allocation are validated.

Original languageEnglish
Title of host publication2020 International Wireless Communications and Mobile Computing, IWCMC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages388-393
Number of pages6
ISBN (Electronic)9781728131290
DOIs
Publication statusPublished - Jun 2020
Event16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020 - Limassol, Cyprus
Duration: 15 Jun 202019 Jun 2020

Publication series

Name2020 International Wireless Communications and Mobile Computing, IWCMC 2020

Conference

Conference16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020
Country/TerritoryCyprus
CityLimassol
Period15/06/202019/06/2020

Keywords

  • Deep Reinforcement Learning
  • Edge Computing
  • Green Resource Allocation
  • Power Internet of Things

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