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Towards Adaptive Clustering in Self-monitoring Multi-agent Networks

  • Piraveenan Mahendra rajah
  • Mikhail Prokopenko
  • Peter Wang
  • Don Price
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3682)

Abstract

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.

Keywords

Array Size Forward Message Quality Ratio Singleton Cluster Concept Demonstrator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Piraveenan Mahendra rajah
    • 1
  • Mikhail Prokopenko
    • 2
  • Peter Wang
    • 2
  • Don Price
    • 3
  1. 1.University of AdelaideAdelaideAustralia
  2. 2.CSIRO Information and Communication Technology Centre 
  3. 3.CSIRO Industrial PhysicsNorth RydeAustralia

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