Towards adaptive clustering in self-monitoring multi-agent networks

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


A Decentralised Adaptive Clustering (DAC) algorithm for self-monitoring impact sensing networks is presented within the context of CSIRO-NASA Ageless Aero-space Vehicle project. DAC algorithm is contrasted with a Fixed-order Centralised Adaptive Clustering (FCAC) algorithm, developed to evaluate the comparative performance. A number of simulation experiments is described, with a focus on the scalability and convergence rate of the clustering algorithm. Results show that DAC algorithm scales well with increasing network and data sizes and is robust to dynamics of the sensor-data flux.

Original languageEnglish
Pages (from-to)796-805
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3682 LNAI
Publication statusPublished - 2005
Externally publishedYes


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