Relay Nodes Selection Using Reinforcement Learning

Haesik Kim, Takeo Fujii, Kenta Umebayashi

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

1 Citation (Scopus)

Abstract

In IoT networks, the nodes work cooperatively. They receive data packets and re-transmit to the sink node (or fusion node) via multiple relay nodes. In order to reduce the loss of packets as well as power consumption, it is important to transmit data packet successfully and find an optimal path from source node to sink node. Relay node selection is one of key research challenges in IoT networks. The reinforcement learning (RL) deals with sequential decision making problem under uncertainty. The goal of sequential decision making problem is to select actions to maximize long term rewards. The RL has emerged as a powerful method for many different areas. In this paper, relay node selection problem in IoT networks with channel measurement data is formulated as a Markov decision process (MDP) problem. The relay node selection problem is solved using Q learning when a local channel measurement map is given. We find an optimal relay node selection path.
Original languageEnglish
Title of host publication3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages329-334
Number of pages6
ISBN (Electronic)978-1-7281-7638-3
ISBN (Print)978-1-7281-7639-0
DOIs
Publication statusPublished - 13 Apr 2021
MoE publication typeA4 Article in a conference publication
Event3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 - Jeju Island, Korea, Republic of
Duration: 13 Apr 202116 Apr 2021

Conference

Conference3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021
Country/TerritoryKorea, Republic of
CityJeju Island
Period13/04/2116/04/21

Keywords

  • Q measurement
  • Uncertainty
  • Power demand
  • Machine learning algorithms
  • Computer simulation
  • Decision making
  • Reinforcement learning

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