RLENS: RL-based Energy-Efficient Network Selection Framework for IoMT

Amr Abo-Eleneen, Alaa Awad Abdellatif, Amr Mohamed, Aiman Erbad

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

1 Citation (Scopus)

Abstract

With the emergence of smart health (s-health) applications and services, several requirements for quality have arisen to foresee and react instantaneously to emergency circumstances. Such requirements demand fast-acting wireless networks while adapting to various types of applications and environment dynamics, encouraging network operators to leverage the spectrum of wireless signals across various radio access networks. Yet, this requires implementing intelligent network selection schemes that account for heterogeneous networks characteristics and applications' QoS requirements. Thus, this paper tackles this problem by adopting an intelligent Reinforcement Learning (RL)-based network selection scheme. Specifically, we leverage edge computing capabilities to implement an efficient user-centric network selection algorithm at the Internet of Medical Things (IoMT) level to adjust the compression ratio and select the most suitable radio access network (RAN) to transfer the acquired data while considering patient state, battery life and networks dynamics. Our results demonstrate the efficiency of the proposed approach in outperforming the state-of-the-art techniques in terms of battery life by more than 500% while reaching almost 85-90% of the optimal algorithm's performance in delay and distortion.

Original languageEnglish
Title of host publication2022 Wireless Telecommunications Symposium, WTS 2022
PublisherIEEE Computer Society
ISBN (Electronic)9781728186788
DOIs
Publication statusPublished - 2022
Event21st Annual Wireless Telecommunications Symposium, WTS 2022 - Virtual, Online, United States
Duration: 6 Apr 20228 Apr 2022

Publication series

NameWireless Telecommunications Symposium
Volume2022-April
ISSN (Print)1934-5070

Conference

Conference21st Annual Wireless Telecommunications Symposium, WTS 2022
Country/TerritoryUnited States
CityVirtual, Online
Period6/04/228/04/22

Keywords

  • Internet of Things
  • energy efficiency
  • network selection
  • reinforcement learning
  • smart health

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