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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
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Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
Carlos Lester Duenas Santos1, Ahmad Mohamad Mezher1, Juan Pablo Astudillo León2,3
1Electrical and Computer Engineering Department, University of New Brunswick, Fredericton, NB E3B 5A3, Canada.
Sensors (Basel, Switzerland)
|August 10, 2024
Summary
This study introduces Q-RPL, a Q-learning routing protocol for Smart Grid Advanced Metering Infrastructure (AMI). Q-RPL enhances data routing efficiency and reliability in wireless mesh networks.
Area of Science:
- Computer Science
- Electrical Engineering
- Network Engineering
Background:
- Efficient data routing is crucial for Smart Grid Advanced Metering Infrastructure (AMI) performance and resilience.
- Wireless mesh networks are commonly used in AMI deployments, presenting unique routing challenges.
- Existing routing protocols may not fully address the dynamic nature of AMI networks.
Purpose of the Study:
- To introduce Q-RPL, a novel Q-learning-based Routing Protocol for AMI.
- To enhance routing decisions and network performance in AMI using Reinforcement Learning (RL).
- To improve the reliability and efficiency of data transmission in Smart Grid networks.
Main Methods:
- Developed Q-RPL, integrating Q-learning with the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL).
- Utilized Reinforcement Learning (RL) principles for dynamic next-hop selection.
- Conducted extensive simulations in real map scenarios to evaluate performance.
Main Results:
- Q-RPL demonstrated significant improvements in packet delivery ratio and end-to-end delay.
- The protocol showed enhanced compliant factor compared to standard RPL and other benchmarks.
- Simulations confirmed Q-RPL's adaptability and robustness in dynamic network conditions.
Conclusions:
- Q-RPL offers a significant advancement for routing protocols in Smart Grid AMI.
- The protocol promises enhanced efficiency and reliability for intelligent energy systems.
- Reinforcement Learning holds substantial potential for improving future networking protocols.
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