Predicting Cluster Formation in Decentralized Sensor Grids

  • Astrid Zeman
  • Mikhail Prokopenko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4253)


This paper investigates cluster formation in decentralized sensor grids and focusses on predicting when the cluster formation converges to a stable configuration. The traffic volume of inter-agent communications is used, as the underlying time series, to construct a predictor of the convergence time. The predictor is based on the assumption that decentralized cluster formation creates multi-agent chaotic dynamics in the communication space, and estimates irregularity of the communication-volume time series during an initial transient interval. The new predictor, based on the auto-correlation function, is contrasted with the predictor based on the correlation entropy (generalized entropy rate). In terms of predictive power, the auto-correlation function is observed to outperform and be less sensitive to noise in the communication space than the correlation entropy. In addition, the preference of the auto-correlation function over the correlation entropy is found to depend on the synchronous message monitoring method.


Cluster Formation Convergence Time Communication Space Generalize Entropy Rate Volume Volume 
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 2006

Authors and Affiliations

  • Astrid Zeman
    • 1
  • Mikhail Prokopenko
    • 1
  1. 1.CSIRO Information and Communication Technology CentreNorth RydeAustralia

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