DQR: a double Q learning multi agent routing protocol for wireless medical sensor network.

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

Abstract

Wireless Medical Sensor Network (WMSN) offers innovative solutions in the healthcare domain. It alleviates the patients' everyday life difficulties and supports the already overloaded medical staff with continuous monitoring tools. However, widespread adoption of these advancements is still restrained by security concerns and limitations of existing routing protocols. Routing is challenging in WMSN owing to the fact that some critical requirements, such as reliable delivery, have been neglected. To address these challenges, this paper proposes DQR, a double Q-learning routing protocol to meet WMSN requirements and overcome the positive bias estimation problem of the Q-learning based routing protocols. DQR uses a novel Reinforcement Learning (RL) model to reduce computational and communication overheads. It is combined with an effective trust management system to ensure a reliable data transfer and defeat packet dropping attacks. The experimental results demonstrate robust performance under various attacks with minimal resource footprint and efficient energy consumption.

Citation

HAJAR, M.S., KALUTARAGE, H. and AL-KADRI, M.O. 2023. DQR: a double Q learning multi agent routing protocol for wireless medical sensor network. In Li, F., Liang, K., Lin, Z. and Katsikas, S.K. (eds.) Security and privacy in communication networks: proceedings of the 18th EAI (European Alliance for Innovation) Security and privacy in communication networks 2022 (EAI SecureComm 2022), 17-19 October 2022, Kansas City, USA. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), 462. Cham: Springer [online], pages 611-629. Available from: https://doi.org/10.1007/978-3-031-25538-0_32

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 18th EAI (European Alliance for Innovation) Security and privacy in communication networks 2022 (EAI SecureComm 2022)
• Start Date: Oct 17, 2022
• End Date: Oct 19, 2022
• Acceptance Date: Jun 23, 2022
• Online Publication Date: Feb 4, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Mar 14, 2023
• Publicly Available Date: Feb 5, 2024
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 611-629
• Series Title: Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST)
• Series Number: 462
• Series ISSN: 1867-8211; 1867-822X
• Book Title: Security and privacy in communication networks: proceedings of the 18th EAI (European Alliance for Innovation) Security and privacy in communication networks 2022 (EAI SecureComm 2022)
• ISBN: 9783031255373
• DOI: https://doi.org/10.1007/978-3-031-25538-0_32
• Keywords: Double Q-learning; Routing; Reinforcement learning; Trust management; Blackhole attack; Selective forwarding attack; Sinkhole attack
• Public URL: https://rgu-repository.worktribe.com/output/1880298