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 language | English |
---|---|
Title of host publication | 3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Pages | 329-334 |
ISBN (Electronic) | 978-1-7281-7638-3 |
ISBN (Print) | 978-1-7281-7639-0 |
DOIs | |
Publication status | Published - 13 Apr 2021 |
MoE publication type | A4 Article in a conference publication |
Event | 3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 - Jeju Island, Korea, Republic of Duration: 13 Apr 2021 → 16 Apr 2021 |
Conference
Conference | 3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 |
---|---|
Country/Territory | Korea, Republic of |
City | Jeju Island |
Period | 13/04/21 → 16/04/21 |
Funding
This work was supported by the European Commission in the framework of the H2020-EUJ-02-2018 project 5G-Enhance (Grant agreement no. 815056) and the Ministry of Internal Affairs and Communications (MIC) of Japan (Grant no. JPJ000595).
Keywords
- Q measurement
- Uncertainty
- Power demand
- Machine learning algorithms
- Computer simulation
- Decision making
- Reinforcement learning