The University of Southampton
University of Southampton Institutional Repository

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

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).

PDF soar_text.pdf - Other
Download (691kB)

More information

Published date: 2010
Organisations: Agents, Interactions & Complexity

Identifiers

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

Catalogue record

Date deposited: 22 Sep 2010 12:19
Last modified: 18 Jul 2017 06:41

Export record

Contributors

Author: Alex Rogers
Author: Alessandro Farinelli
Author: Nick Jennings
Editor: Danny Weyns
Editor: Sam Malek
Editor: Rogério de Lemos

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×