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Self-organising Sensors for Wide Area Surveillance Using the Max-sum Algorithm

Self-organising Sensors for Wide Area Surveillance Using the Max-sum Algorithm
Self-organising Sensors for Wide Area Surveillance Using the Max-sum Algorithm
In this paper, we consider the self-organisation of sensors within a network deployed for wide area surveillance. We present a decentralised coordination algorithm based upon the max-sum algorithm and demonstrate how self-organisation can be achieved within a setting where sensors are deployed with no a priori information regarding their local environment. These energy-constrained sensors first learn how their actions interact with those of neighbouring sensors, and then use the max-sum algorithm to coordinate their sense/sleep schedules in order to maximise the effectiveness of the sensor network as a whole. In a simulation we show that this approach yields a 30% reduction in the number of vehicles that the sensor network fails to detect (compared to an uncoordinated network), and this performance is close to that achieved by a benchmark centralised optimisation algorithm (simulated annealing).
84-100
Springer
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Weyns, Danny
Malek, Sam
de Lemos, Rogério
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Weyns, Danny
Malek, Sam
de Lemos, Rogério

Rogers, Alex, Farinelli, Alessandro and Jennings, Nick (2010) Self-organising Sensors for Wide Area Surveillance Using the Max-sum Algorithm. In, Weyns, Danny, Malek, Sam and de Lemos, Rogério (eds.) LNCS 6090 Lecture Notes in Computer Science. Self-Organizing Architectures. Springer, pp. 84-100.

Record type: Book Section

Abstract

In this paper, we consider the self-organisation of sensors within a network deployed for wide area surveillance. We present a decentralised coordination algorithm based upon the max-sum algorithm and demonstrate how self-organisation can be achieved within a setting where sensors are deployed with no a priori information regarding their local environment. These energy-constrained sensors first learn how their actions interact with those of neighbouring sensors, and then use the max-sum algorithm to coordinate their sense/sleep schedules in order to maximise the effectiveness of the sensor network as a whole. In a simulation we show that this approach yields a 30% reduction in the number of vehicles that the sensor network fails to detect (compared to an uncoordinated network), and this performance is close to that achieved by a benchmark centralised optimisation algorithm (simulated annealing).

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Published date: 2010
Organisations: Agents, Interactions & Complexity

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Local EPrints ID: 271579
URI: https://eprints.soton.ac.uk/id/eprint/271579
PURE UUID: c76f0229-9081-4ac6-8c75-8fabbf0b4406

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Date deposited: 22 Sep 2010 12:19
Last modified: 23 Sep 2019 18:58

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