BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
A method for decentralized clustering in large multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Complexity Metrics for Self-monitoring Impact Sensing Networks
EH '05 Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
Self-Organizing Hierarchies in Sensor and Communication Networks
Artificial Life
On convergence of dynamic cluster formation in multi-agent networks
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Towards adaptive clustering in self-monitoring multi-agent networks
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Adaptive clustering for mobile wireless networks
IEEE Journal on Selected Areas in Communications
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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.