creators_name: Tran-Thanh, Long
creators_name: Rogers, Alex
creators_name: Jennings, Nicholas R
type: article
datestamp: 2011-11-14 09:58:05
lastmod: 2012-05-16 10:07:22
metadata_visibility: show
title: Long–Term Information Collection with Energy Harvesting Wireless Sensors: A Multi–Armed Bandit Based Approac
ispublished: inpress
subjects: ABC
subjects: app
subjects: ie
full_text_status: public
abstract: This paper reports on the development of a multi–agent approach to long-term information collection in networks of energy harvesting wireless sensors. In particular, we focus on developing energy management and data routing policies that adapt their behaviour according to the energy that is harvested, in order to maximise the amount of information collected given the available energy budget. In so doing, we introduce a new energy management technique, based on multi–armed bandit learning, that allows each agent to adaptively allocate its energy budget across the tasks of data sampling, receiving and transmitting. By using this approach, each agent can learn the optimal energy budget settings that give it efficient information collection in the long run. Then, we propose two novel decentralised multi–hop algorithms for data routing. The first proveably maximises the information throughput in the network, but can sometimes involve high communication cost. The second algorithm provides near–optimal performance, but with reduced computational and communication costs. Finally, we demonstrate that, by using our approaches for energy management and routing, we can achieve a 120% improvement in long term information collection against state–of–the–art benchmarks.
date: 2012
date_type: published
publication: Journal of Autonomous Agents and Multi-agent Systems
volume: 25
number: 2
pagerange: 352-394
refereed: TRUE
citation:   Tran-Thanh, Long and Rogers, Alex and Jennings, Nicholas R  (2012) Long–Term Information Collection with Energy Harvesting Wireless Sensors: A Multi–Armed Bandit Based Approac.  Journal of Autonomous Agents and Multi-agent Systems, 25 (2).  pp. 352-394.     (In Press)  
document_url: http://www.orchid.ac.uk/eprints/12/1/LTT_JAAMAS2010.pdf