On decentralised clustering in self-monitoring networks

Piraveenan Mahendra Rajah, Mikhail Prokopenko*, Peter Wang, Don Price

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

A Decentralised Adaptive Clustering (DAC) algorithm for multiagent networks is contrasted with a Fixed-order Centralised Adaptive Clustering algorithm (FCAC). The clustering is done on sensor readings detected within a self-monitoring impact sensing network. Simulation results show that DAC algorithm scales well with increasing network and data sizes and in some cases outperforms FCAC algorithm. While the common-sense intuition suggests that centralised algorithm is always superior, we support the simulation results with a simple counter-example.

Original languageEnglish
Title of host publicationProceedings of the 4th International Conference on Autonomous Agents and Multi agent Systems, AAMAS 05
EditorsF. Dignum, V. Dignum, S. Koenig, S. Kraus, M. Pechoucek, M. Singh, D. Steiner, S. Thompson, M. Wooldridge
Place of PublicationNew York
PublisherACM
Pages1279-1280
Number of pages2
ISBN (Electronic)1595930930, 9781595930934
Publication statusPublished - 2005
Externally publishedYes
Event4th International Conference on Autonomous Agents and Multi agent Systems, AAMAS 05 - Utrecht, Netherlands
Duration: 25 Jul 200529 Jul 2005

Other

Other4th International Conference on Autonomous Agents and Multi agent Systems, AAMAS 05
CountryNetherlands
CityUtrecht
Period25/07/0529/07/05

Keywords

  • Clustering
  • Scalability
  • Sensor networks

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