@inproceedings{94ef4176b167416e80550219a2d8afce,
title = "Predicting cluster formation in decentralized sensor grids",
abstract = "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 synchronous message monitoring method.",
author = "Astrid Zeman and Mikhail Prokopenko",
year = "2006",
language = "English",
isbn = "3540465421",
volume = "4253 LNAI - III",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "324--332",
booktitle = "Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings",
address = "United States",
note = "10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2006 ; Conference date: 09-10-2006 Through 11-10-2006",
}