Analyse or Transmit: Utilising Correlation at the Edge with Deep Reinforcement Learning

Jernej Hribar, Ryoichi Shinkuma, George Iosifidis, Ivana Dusparic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Millions of sensors, cameras, meters, and other edge devices are deployed in networks to collect and analyse data. In many cases, such devices are powered only by Energy Harvesting (EH) and have limited energy available to analyse acquired data. When edge infrastructure is available, a device has a choice: to perform analysis locally or offload the task to other resource-rich devices such as cloudlet servers. However, such a choice carries a price in terms of consumed energy and accuracy. On the one hand, transmitting raw data can result in a higher energy cost in comparison to the required energy to process data locally. On the other hand, performing data analytics on servers can improve the task's accuracy. Additionally, due to the correlation between information sent by multiple devices, accuracy might not be affected if some edge devices decide to neither process nor send data and preserve energy instead. For such a scenario, we propose a Deep Reinforcement Learning (DRL) based solution capable of learning and adapting the policy to the time-varying energy arrival due to EH patterns. We leverage two datasets, one to model energy an EH device can collect and the other to model the correlation between cameras. Furthermore, we compare the proposed solution performance to three baseline policies. Our results show that we can increase accuracy by 15% in comparison to conventional approaches while preventing outages.

Original languageEnglish
Title of host publication2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728181042
DOIs
Publication statusPublished - 2021
Event2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
Duration: 2021 Dec 72021 Dec 11

Publication series

Name2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings

Conference

Conference2021 IEEE Global Communications Conference, GLOBECOM 2021
Country/TerritorySpain
CityMadrid
Period21/12/721/12/11

Keywords

  • Data-analytics
  • Deep Reinforcement Learning
  • Edge Computing
  • Energy-harvesting
  • Green Commu-nications

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Health Informatics

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