The University of Southampton
University of Southampton Institutional Repository

A Decentralised Coordination Algorithm for Mobile Sensors

A Decentralised Coordination Algorithm for Mobile Sensors
A Decentralised Coordination Algorithm for Mobile Sensors
We present an on-line decentralised algorithm for coordinating mobile sensors for a broad class of information gathering tasks. These sensors can be deployed in unknown and possibly hostile environments, where uncertainty and dynamism are endemic. Such environments are common in the areas of disaster response and military surveillance. Our coordination approach itself is based on work by Stranders et al. (2009), that uses the max-sum algorithm to coordinate mobile sensors for monitoring spatial phenomena. In particular, we generalise and extend their approach to any domain where measurements can be valued. Also, we introduce a clustering approach that allows sensors to negotiate over paths to the most relevant locations, as opposed to a set of fixed directions, which results in a significantly improved performance. We demonstrate our algorithm by applying it to two challenging and distinct information gathering tasks. In the first–pursuit-evasion (PE)–sensors need to capture a target whose movement might be unknown. In the second–patrolling (P)–sensors need to minimise loss from intrusions that occur within their environment. In doing so, we obtain the first decentralised coordination algorithms for these domains. Finally, in each domain, we empirically evaluate our approach in a simulated environment, and show that it outperforms two state of the art greedy algorithms by 30% (PE) and 44% (P), and an existing approach based on the Travelling Salesman Problem by 52% (PE) and 30% (P).
874-880
Stranders, Ruben
cca79d07-0668-4231-a80f-5fae6617644c
Delle Fave, Francesco Maria
1a71a79a-fb96-4bfc-9158-36f8dcb5d96f
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Stranders, Ruben
cca79d07-0668-4231-a80f-5fae6617644c
Delle Fave, Francesco Maria
1a71a79a-fb96-4bfc-9158-36f8dcb5d96f
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Stranders, Ruben, Delle Fave, Francesco Maria, Rogers, Alex and Jennings, Nick (2010) A Decentralised Coordination Algorithm for Mobile Sensors. Twenty-Fourth AAAI Conference on Artificial Intelligence, Atlanta, USA, Georgia. 11 - 15 Jul 2010. pp. 874-880 .

Record type: Conference or Workshop Item (Paper)

Abstract

We present an on-line decentralised algorithm for coordinating mobile sensors for a broad class of information gathering tasks. These sensors can be deployed in unknown and possibly hostile environments, where uncertainty and dynamism are endemic. Such environments are common in the areas of disaster response and military surveillance. Our coordination approach itself is based on work by Stranders et al. (2009), that uses the max-sum algorithm to coordinate mobile sensors for monitoring spatial phenomena. In particular, we generalise and extend their approach to any domain where measurements can be valued. Also, we introduce a clustering approach that allows sensors to negotiate over paths to the most relevant locations, as opposed to a set of fixed directions, which results in a significantly improved performance. We demonstrate our algorithm by applying it to two challenging and distinct information gathering tasks. In the first–pursuit-evasion (PE)–sensors need to capture a target whose movement might be unknown. In the second–patrolling (P)–sensors need to minimise loss from intrusions that occur within their environment. In doing so, we obtain the first decentralised coordination algorithms for these domains. Finally, in each domain, we empirically evaluate our approach in a simulated environment, and show that it outperforms two state of the art greedy algorithms by 30% (PE) and 44% (P), and an existing approach based on the Travelling Salesman Problem by 52% (PE) and 30% (P).

Text
main.pdf - Other
Download (207kB)

More information

Published date: 2010
Additional Information: Event Dates: 11 - 15 July, 2010
Venue - Dates: Twenty-Fourth AAAI Conference on Artificial Intelligence, Atlanta, USA, Georgia, 2010-07-11 - 2010-07-15
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 270810
URI: http://eprints.soton.ac.uk/id/eprint/270810
PURE UUID: 1cb1a770-2ad4-4f11-a6b2-eb0e61996306

Catalogue record

Date deposited: 08 Apr 2010 14:12
Last modified: 14 Mar 2024 09:16

Export record

Contributors

Author: Ruben Stranders
Author: Francesco Maria Delle Fave
Author: Alex Rogers
Author: Nick Jennings

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.

×