Dynamic power control in wireless body area networks using reinforcement learning with approximation

Kazemi, R; Vesilo, R; Dutkiewicz, E; Liu, R

Dynamic power control in wireless body area networks using reinforcement learning with approximation

Kazemi, R Vesilo, R Dutkiewicz, E

Liu, R

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Publication Type:: Conference Proceeding
Citation:: IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2011, pp. 2203 - 2208
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Kazemi, R	en_US
dc.contributor.author	Vesilo, R	en_US
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286	en_US
dc.contributor.author	Liu, R https://orcid.org/0000-0001-7001-6305	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2011, pp. 2203 - 2208	en_US
dc.identifier.isbn	9781457713484	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120500
dc.description.abstract	A Wireless Body Area Network (WBAN) is made up of multiple tiny physiological sensors implanted in/on the human body with each sensor equipped with a wireless transceiver that communicates to a coordinator in a star topology. Energy is the scarcest resource in WBANs. Power control mechanisms to achieve a certain level of utility while using as little power for transmission as possible can play an important role in reducing energy consumption in such very energy-constrained networks. In this paper, we propose a novel power controller to mitigate internetwork interference in WBANs and increase the maximum achievable throughput with the minimum energy consumption. The proposed power controller employs reinforcement learning with approximation to learn from the environment and improve its performance. We compare the performance of the proposed controller to two other power controllers, one based on game theory and the other one based on fuzzy logic. Simulation results show that compared to the other two approaches, RLPC provides a substantial saving in energy consumption per bit, with a substantial increase in network lifetime. © 2011 IEEE.	en_US
dc.relation.ispartof	IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC	en_US
dc.relation.isbasedon	10.1109/PIMRC.2011.6139908	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Dynamic power control in wireless body area networks using reinforcement learning with approximation	en_US
dc.type	Conference Proceeding
utslib.for	100510 Wireless Communications	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

A Wireless Body Area Network (WBAN) is made up of multiple tiny physiological sensors implanted in/on the human body with each sensor equipped with a wireless transceiver that communicates to a coordinator in a star topology. Energy is the scarcest resource in WBANs. Power control mechanisms to achieve a certain level of utility while using as little power for transmission as possible can play an important role in reducing energy consumption in such very energy-constrained networks. In this paper, we propose a novel power controller to mitigate internetwork interference in WBANs and increase the maximum achievable throughput with the minimum energy consumption. The proposed power controller employs reinforcement learning with approximation to learn from the environment and improve its performance. We compare the performance of the proposed controller to two other power controllers, one based on game theory and the other one based on fuzzy logic. Simulation results show that compared to the other two approaches, RLPC provides a substantial saving in energy consumption per bit, with a substantial increase in network lifetime. © 2011 IEEE.

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