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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RRP_A_Reliable_Reinforcement_Learning_Based_Routing_Protocol_for_Wireless_Medical_Sensor_Networks_CCNC_2023_.pdf - Accepted Version Download (1MB) |
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) | ||||||
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| Identification Number: | https://doi.org/10.1109/CCNC51644.2023.10060225 | ||||||
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| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science | ||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| 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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