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

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

Authors

M. Omar Al-Kadri



Contributors

Fengjun Li
Editor

Kaitai Liang
Editor

Zhiqiang Lin
Editor

Sokratis K. Katsikas
Editor

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

Conference Name 18th EAI (European Alliance for Innovation) Security and privacy in communication networks 2022 (EAI SecureComm 2022)
Conference Location Kansas City, USA
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
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

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.





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