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Agent-based decentralised coordination for sensor networks using the max-sum algorithm

Agent-based decentralised coordination for sensor networks using the max-sum algorithm
Agent-based decentralised coordination for sensor networks using the max-sum algorithm
In this paper, we consider the generic problem of how a network of physically distributed, computationally constrained devices can make coordinated decisions to maximise the effectiveness of the whole sensor network. In particular, we propose a new agent-based representation of the problem, based on the factor graph, and use state-of-the-art DCOP heuristics (i.e., DSA and the max-sum algorithm) to generate sub-optimal solutions. In more detail, we formally model a specific real-world problem where energy-harvesting sensors are deployed within an urban environment to detect vehicle movements. The sensors coordinate their sense/sleep schedules, maintaining energy neutral operation while maximising vehicle detection probability. We theoretically analyse the performance of the sensor network for various coordination strategies and show that by appropriately coordinating their schedules the sensors can achieve significantly improved system-wide performance, detecting up to 50% of the events that a randomly coordinated network fails to detect. Finally, we deploy our coordination approach in a realistic simulation of our wide area surveillance problem, comparing its performance to a number of benchmarking coordination strategies. In this setting, our approach achieves up to a 57% reduction in the number of missed vehicles (compared to an uncoordinated network). This performance is close to that achieved by a benchmark centralised algorithm (simulated annealing) and to a continuously powered network (which is an unreachable upper bound for any coordination approach).
1387-2532
337-380
Farinelli, A.
eda89a3c-23fa-4862-997c-995769ffe747
Rogers, A.
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, N.R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Farinelli, A.
eda89a3c-23fa-4862-997c-995769ffe747
Rogers, A.
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, N.R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Farinelli, A., Rogers, A. and Jennings, N.R. (2014) Agent-based decentralised coordination for sensor networks using the max-sum algorithm. Autonomous Agents and Multi-Agent Systems, 28 (3), 337-380. (doi:10.1007/s10458-013-9225-1).

Record type: Article

Abstract

In this paper, we consider the generic problem of how a network of physically distributed, computationally constrained devices can make coordinated decisions to maximise the effectiveness of the whole sensor network. In particular, we propose a new agent-based representation of the problem, based on the factor graph, and use state-of-the-art DCOP heuristics (i.e., DSA and the max-sum algorithm) to generate sub-optimal solutions. In more detail, we formally model a specific real-world problem where energy-harvesting sensors are deployed within an urban environment to detect vehicle movements. The sensors coordinate their sense/sleep schedules, maintaining energy neutral operation while maximising vehicle detection probability. We theoretically analyse the performance of the sensor network for various coordination strategies and show that by appropriately coordinating their schedules the sensors can achieve significantly improved system-wide performance, detecting up to 50% of the events that a randomly coordinated network fails to detect. Finally, we deploy our coordination approach in a realistic simulation of our wide area surveillance problem, comparing its performance to a number of benchmarking coordination strategies. In this setting, our approach achieves up to a 57% reduction in the number of missed vehicles (compared to an uncoordinated network). This performance is close to that achieved by a benchmark centralised algorithm (simulated annealing) and to a continuously powered network (which is an unreachable upper bound for any coordination approach).

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More information

e-pub ahead of print date: 23 May 2013
Published date: May 2014
Additional Information: The software associated with this paper can be downloaded from: http://profs.scienze.univr.it/~farinelli/pubs/sensorcoverage-release.zip
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 350670
URI: https://eprints.soton.ac.uk/id/eprint/350670
ISSN: 1387-2532
PURE UUID: 13c6d840-84bb-4612-acb0-5808ea1a292c

Catalogue record

Date deposited: 27 Mar 2013 22:09
Last modified: 19 Jul 2019 21:40

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