Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids-Reference-Cited by-同舟云学术

Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids

Published:2024-07-25 Issue:15 Volume:24 Page:4818
ISSN:1424-8220
Container-title:Sensors
language:en
Short-container-title:Sensors

Author:

Duenas Santos Carlos Lester¹^ORCID,Mezher Ahmad Mohamad¹^ORCID,Astudillo León Juan Pablo²³^ORCID,Cardenas Barrera Julian¹^ORCID,Castillo Guerra Eduardo¹^ORCID,Meng Julian¹^ORCID

Affiliation:

1. Electrical and Computer Engineering Department, University of New Brunswick, Fredericton, NB E3B 5A3, Canada

2. School of Mathematical and Computational Sciences, Yachay Tech University, Urcuquí 100115, Ecuador

3. Communication Networks and Intelligent Services Research Group (ComNet Innova YT), Yachay Tech University, Urcuquí 100115, Ecuador

Abstract

Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh technologies. Q-RPL leverages the principles of Reinforcement Learning (RL) to dynamically select optimal next-hop forwarding candidates, adapting to changing network conditions. The protocol operates on top of the standard IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), integrating it with intelligent decision-making capabilities. Through extensive simulations carried out in real map scenarios, Q-RPL demonstrates a significant improvement in key performance metrics such as packet delivery ratio, end-to-end delay, and compliant factor compared to the standard RPL implementation and other benchmark algorithms found in the literature. The adaptability and robustness of Q-RPL mark a significant advancement in the evolution of routing protocols for Smart Grid AMI, promising enhanced efficiency and reliability for future intelligent energy systems. The findings of this study also underscore the potential of Reinforcement Learning to improve networking protocols.

Funder

Atlantic Canada Opportunities Agency

Publisher

MDPI AG

Link

https://www.mdpi.com/1424-8220/24/15/4818/pdf

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