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Decentralised Coordination of Mobile Sensors Using the Max-Sum Algorithm

Decentralised Coordination of Mobile Sensors Using the Max-Sum Algorithm
Decentralised Coordination of Mobile Sensors Using the Max-Sum Algorithm
In this paper, we introduce an on-line, decentralised coordination algorithm for monitoring and predicting the state of spatial phenomena by a team of mobile sensors. These sensors have their application domain in disaster response, where strict time constraints prohibit path planning in advance. The algorithm enables sensors to coordinate their movements with their direct neighbours to maximise the collective information gain, while predicting measurements at unobserved locations using a Gaussian process. It builds upon the max-sum message passing algorithm for decentralised coordination, for which we present two new generic pruning techniques that result in speed-up of up to 92% for 5 sensors. We empirically evaluate our algorithm against several on-line adaptive coordination mechanisms, and report a reduction in root mean squared error up to 50% compared to a greedy strategy.
299-304
Stranders, Ruben
cca79d07-0668-4231-a80f-5fae6617644c
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Stranders, Ruben
cca79d07-0668-4231-a80f-5fae6617644c
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Stranders, Ruben, Farinelli, Alessandro, Rogers, Alex and Jennings, Nick (2009) Decentralised Coordination of Mobile Sensors Using the Max-Sum Algorithm. Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, United States. pp. 299-304 .

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we introduce an on-line, decentralised coordination algorithm for monitoring and predicting the state of spatial phenomena by a team of mobile sensors. These sensors have their application domain in disaster response, where strict time constraints prohibit path planning in advance. The algorithm enables sensors to coordinate their movements with their direct neighbours to maximise the collective information gain, while predicting measurements at unobserved locations using a Gaussian process. It builds upon the max-sum message passing algorithm for decentralised coordination, for which we present two new generic pruning techniques that result in speed-up of up to 92% for 5 sensors. We empirically evaluate our algorithm against several on-line adaptive coordination mechanisms, and report a reduction in root mean squared error up to 50% compared to a greedy strategy.

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Published date: 2009
Venue - Dates: Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, United States, 2009-01-01
Organisations: Agents, Interactions & Complexity

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Local EPrints ID: 267268
URI: https://eprints.soton.ac.uk/id/eprint/267268
PURE UUID: 1ce3991c-69cc-43c5-b6b8-aaf2a39089a4

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Date deposited: 08 Apr 2009 16:40
Last modified: 28 Aug 2019 18:59

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