Predicting Cluster Formation in Decentralized Sensor Grids

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 multiagent 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 synchron...

• Type: Journal
• Year: 2006
• Where: KES
• Authors: Astrid Zeman, Mikhail Prokopenko
• Keywords: Auto-correlation Function, Cluster Formation, Information Technology, KES 2006, Time Series