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

Muhammad Shadi Hajar*, Omar Al Kadri, Harsha Kalutarage

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    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.
    Original languageEnglish
    Title of host publicationRRP: A Reliable Reinforcement Learning Based Routing Protocol for Wireless Medical Sensor Networks
    Pages781-789
    Number of pages8
    DOIs
    Publication statusPublished (VoR) - 17 Mar 2023

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