Dynamic path determination of mobile beacons employing reinforcement learning for wireless sensor localization

Li, S; Kong, X; Lowe, D

Dynamic path determination of mobile beacons employing reinforcement learning for wireless sensor localization

Li, S Kong, X

Lowe, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 26th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012, 2012, pp. 760 - 765
Issue Date:: 2012-05-14

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Field	Value	Language
dc.contributor.author	Li, S	en_US
dc.contributor.author	Kong, X https://orcid.org/0000-0001-7855-858X	en_US
dc.contributor.author	Lowe, D	en_US
dc.date.issued	2012-05-14	en_US
dc.identifier.citation	Proceedings - 26th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012, 2012, pp. 760 - 765	en_US
dc.identifier.isbn	9780769546520	en_US
dc.identifier.uri	http://hdl.handle.net/10453/21966
dc.description.abstract	Wireless sensor networks (WSN) are extensively applied in civil and military areas. Localization is an essential prerequisite for many WSN applications, and is often based on beacons that provide geographical information in real time. Mobile Beacons (MB) can be used to replace many static beacons with paths that can be controlled in real-time. Robotic and/or flight vehicles can work as MBs. In this paper we consider the use of reinforcement learning (RL) (a significant branch of machine learning) to control MBs. Usually, RL needs an infinite series of episodes to determine an optimal policy. We propose however a method of localization employing mobile beacon whose behavior will be controlled by an adapted RL algorithm. A MB learns and makes decisions based on weighted information collected from unknown sensors. Simulation results show that the adapted RL algorithm provides sufficient information to the MB to localise unknown sensors in a lightweight but effective way. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - 26th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012	en_US
dc.relation.isbasedon	10.1109/WAINA.2012.67	en_US
dc.title	Dynamic path determination of mobile beacons employing reinforcement learning for wireless sensor localization	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	Fukuoka, Japan	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 - CRIN - Realtime Information Networks
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Wireless sensor networks (WSN) are extensively applied in civil and military areas. Localization is an essential prerequisite for many WSN applications, and is often based on beacons that provide geographical information in real time. Mobile Beacons (MB) can be used to replace many static beacons with paths that can be controlled in real-time. Robotic and/or flight vehicles can work as MBs. In this paper we consider the use of reinforcement learning (RL) (a significant branch of machine learning) to control MBs. Usually, RL needs an infinite series of episodes to determine an optimal policy. We propose however a method of localization employing mobile beacon whose behavior will be controlled by an adapted RL algorithm. A MB learns and makes decisions based on weighted information collected from unknown sensors. Simulation results show that the adapted RL algorithm provides sufficient information to the MB to localise unknown sensors in a lightweight but effective way. © 2012 IEEE.

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http://hdl.handle.net/10453/21966