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Long–term information collection with energy harvesting wireless sensors: a multi–armed bandit based approach

Long–term information collection with energy harvesting wireless sensors: a multi–armed bandit based approach
Long–term information collection with energy harvesting wireless sensors: a multi–armed bandit based approach
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.
1387-2532
352-394
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Tran-Thanh, Long, Rogers, Alex and Jennings, Nick (2012) Long–term information collection with energy harvesting wireless sensors: a multi–armed bandit based approach. Autonomous Agents and Multi-Agent Systems, 25 (2), 352-394. (doi:10.1007/s10458-011-9179-0).

Record type: Article

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.

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e-pub ahead of print date: 30 June 2012
Published date: 1 September 2012
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 272587
URI: https://eprints.soton.ac.uk/id/eprint/272587
ISSN: 1387-2532
PURE UUID: 66fa1dfb-52e3-4ce8-acf2-e17d53c6e06d
ORCID for Long Tran-Thanh: ORCID iD orcid.org/0000-0003-1617-8316

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Date deposited: 20 Jul 2011 14:21
Last modified: 20 Jul 2019 00:48

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Contributors

Author: Long Tran-Thanh ORCID iD
Author: Alex Rogers
Author: Nick Jennings

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