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

Jernej Hribar, Ryoichi Shinkuma, George Iosifidis, Ivana Dusparic

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728181042
DOI
出版ステータスPublished - 2021
イベント2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
継続期間: 2021 12月 72021 12月 11

出版物シリーズ

名前2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings

Conference

Conference2021 IEEE Global Communications Conference, GLOBECOM 2021
国/地域Spain
CityMadrid
Period21/12/721/12/11

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • ハードウェアとアーキテクチャ
  • 情報システムおよび情報管理
  • 安全性、リスク、信頼性、品質管理
  • 健康情報学

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