RRP: A Reliable Reinforcement Learning Based Routing Protocol for Wireless Medical Sensor Networks

Hajar, Muhammad Shadi and Kalutarage, Harsha and Al-Kadri, M. Omar (2023) RRP: A Reliable Reinforcement Learning Based Routing Protocol for Wireless Medical Sensor Networks. In: 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC), 08th - 11th January 2023, Las Vegas, NV, USA.

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Abstract

Wireless medical sensor networks (WMSNs) offer innovative healthcare applications that improve patients' quality of life, provide timely monitoring tools for physicians, and support national healthcare systems. However, despite these benefits, widespread adoption of WMSN advancements is still hampered by security concerns and limitations of routing protocols. Routing in WMSNs is a challenging task due to the fact that some WMSN requirements are overlooked by existing routing proposals. To overcome these challenges, this paper proposes a reliable multi-agent reinforcement learning based routing protocol (RRP). RRP is a lightweight attacks-resistant routing protocol designed to meet the unique requirements of WMSN. It uses a novel Q-learning model to reduce resource consumption combined with an effective trust management system to defend against various packet-dropping attacks. Experimental results prove the lightweightness of RRP and its robustness against blackhole, selective forwarding, sinkhole and complicated on-off attacks.

Item Type:	Conference or Workshop Item (Paper)
Identification Number:	10.1109/CCNC51644.2023.10060225
Dates:	Date Event 1 January 2023 Accepted 17 March 2023 Published Online
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Gemma Tonks
Date Deposited:	21 Apr 2023 13:22
Last Modified:	21 Apr 2023 13:22
URI:	https://www.open-access.bcu.ac.uk/id/eprint/14351

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