Q-Learning ADR Agent for LoRaWAN Optimization

Rodrigo Carvalho, Faroq Al-Tam, Noelia Correia

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

9 Citations (SciVal)

Abstract

LoRaWAN has emerged as one of the most popular technologies in the LPWAN industry due to its low cost and straightforward management. Despite its relatively simple architecture, LoRaWAN is able to optimize energy, data rate, and time on-air by means of an adaptive data rate mechanism. In this paper, a reinforcement learning agent is designed to contrast with the central ADR component. This new agent operates seamlessly to all end nodes while still reacting quickly to changes. A comparative analysis between the classic ADR and the proposed RL-based ADR agent is done using discrete event simulation. Results show that the new ADR mechanism can determine the best configuration and that the proposed reward function fits the intended learning process.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2021
PublisherIEEE Press
Pages104-108
Number of pages5
ISBN (Electronic)9781665444941
DOIs
Publication statusPublished - 27 Jul 2021
Event2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2021 - Virtual, Bandung, Indonesia
Duration: 27 Jul 202128 Jul 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2021

Conference

Conference2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2021
Country/TerritoryIndonesia
CityVirtual, Bandung
Period27 Jul 202128 Jul 2021

Keywords

  • Adaptive data rate
  • IoT
  • LoRaWAN
  • LPWAN
  • Q-learning

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