A deep reinforcement learning-based wireless body area network offloading optimization strategy for healthcare services

Yingqun Chen; Shaodong Han; Guihong Chen; Jiao Yin; Kate Nana Wang; Jinli Cao

doi:10.1007/s13755-023-00212-3

A deep reinforcement learning-based wireless body area network offloading optimization strategy for healthcare services

Health Inf Sci Syst. 2023 Jan 28;11(1):8. doi: 10.1007/s13755-023-00212-3. eCollection 2023 Dec.

Authors

Yingqun Chen¹, Shaodong Han¹, Guihong Chen^{1

2}, Jiao Yin³, Kate Nana Wang⁴, Jinli Cao⁵

Affiliations

¹ School of Cyber Security, Guangdong Polytechnic Normal University, Guangzhou, 510000 China.
² School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641 China.
³ Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, 3011 Australia.
⁴ School of Health and Biomedical Sciences, RMIT University, Melbourne, 3082 Australia.
⁵ Department of Computer Science and Information Technology, La Trobe University, Melbourne, 3086 Australia.

PMID: 36721639
PMCID: PMC9884307 (available on 2024-12-01)
DOI: 10.1007/s13755-023-00212-3

Abstract

Wireless body area network (WBAN) is widely adopted in healthcare services, providing remote real-time and continuous healthcare monitoring. With the massive increase of detective sensor data, WBAN is largely restricted by limited storage and computation capacity, resulting in severely decreased efficiency and reliability. Mobile edge computing (MEC) technique can be combined with WBAN to resolve this issue. This paper studies the joint optimization problem of computational offloading and resource allocation (JCORA) in MEC for healthcare service scenarios. We formulate JCORA as a Markov decision process and propose a deep deterministic policy gradient-based WBAN offloading strategy (DDPG-WOS) to optimize time delay and energy consumption in interfered transmission channels. This scheme employs MEC to mitigate the computation pressure on a single WBAN and increase the transmission ability. Further, DDPG-WOS optimizes the offloading strategy-making process by considering the channel condition, transmission quality, computation ability and energy consumption. Simulation results verify the effectiveness of the proposed optimization schema in reducing energy consumption and computation latency and increasing the utility of WBAN compared to two competitive solutions.

Keywords: Deep reinforcement learning; Mobile edge computing; Offloading policy; Wireless body area networks.

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