3R: a reliable multi agent reinforcement learning based routing protocol for wireless medical sensor networks.

Hajar, Muhammad Shadi; Kalutarage, Harsha Kumara; Al-Kadri, M. Omar

Abstract

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraints, there is a need to develop a routing protocol to fulfill WMSN requirements in terms of delivery reliability, attack resiliency, computational overhead, and energy efficiency. This paper proposes 3R, a reliable multi agent reinforcement learning routing protocol for WMSN. 3R uses a novel resource-conservative Reinforcement Learning (RL) model to reduce the computational overhead, along with two updating methods to speed up the algorithm convergence. The reward function is re-defined as a punishment, combining the proposed trust management system to defend against well-known dropping attacks. Furthermore, an energy model is integrated with the reward function to enhance the network lifetime and balance energy consumption across the network. The proposed energy model only uses local information to avoid the resource burdens and the security concerns of exchanging energy information. Experimental results prove the lightweightness, attacks resiliency and energy efficiency of 3R, making it a potential routing candidate for WMSN.

Citation

HAJAR, M.S., KALUTARAGE, H.K. and AL-KADRI, M.O. 2023. 3R: a reliable multi agent reinforcement learning based routing protocol for wireless medical sensor networks. Computer networks [online], 237, article number 110073. Available from: https://doi.org/10.1016/j.comnet.2023.110073

• Journal Article Type: Article
• Acceptance Date: Oct 17, 2023
• Online Publication Date: Oct 21, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Oct 23, 2023
• Publicly Available Date: Oct 23, 2023
• Journal: Computer networks
• Print ISSN: 1389-1286
• Electronic ISSN: 1872-7069
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 237
• Article Number: 110073
• DOI: https://doi.org/10.1016/j.comnet.2023.110073
• Keywords: Routing; Reinforcement learning; Trust management; Energy; Blackhole attacks; Selective forwarding attacks; Sinkhole attacks; On-off attacks; Security; Q-learning
• Public URL: https://rgu-repository.worktribe.com/output/2120239