Reinforcement Learning Based Energy Management in Wireless Body Area Network: A Survey

In modern life, personal health-care awareness is a fast-growing revolution. In which, Wireless Body Area Network (WBAN) allows inexpensive health-care services with the evaluation of modern devices. In particular, WBAN devices such as in-body sensors and coordinator become more decentralized and au...

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Bibliographic Details
Published in2019 IEEE Conference on Information and Communication Technology pp. 1 - 6
Main Authors Gupta, Arti, Chaurasiya, Vijay Kumar
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
Access control
Body area networks
energy harvesting
Learning (artificial intelligence)
m-health
Power control
Q-learning
Reinforcement Learning
Sensors
WBAN
Wireless communication
Wireless sensor networks
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Summary:In modern life, personal health-care awareness is a fast-growing revolution. In which, Wireless Body Area Network (WBAN) allows inexpensive health-care services with the evaluation of modern devices. In particular, WBAN devices such as in-body sensors and coordinator become more decentralized and autonomous. Moreover, Reinforcement Learning (RL) type of machine learning is formulated to lead the WBAN devices to make an autonomous decision such as sensor access control, transmit power control, security against attack to improve the network performance, quality of service (QoS) and increase the overall utility of the network in an optimized way. In this paper, we provide a literature survey about WBAN and its application, challenges and issues. Finally, we present the application of RL has appeared with the sophisticated solution in the WBAN.
DOI:10.1109/CICT48419.2019.9066260