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Clustering is an established data mining technique for grouping objects based on similarity. For sensor networks one aims at grouping sensor measurements in groups of similar measurements. As sensor networks have limited resources in terms of available memory and energy, a major task sensor clustering is efficient computation on sensor nodes. As a dominating energy consuming task, communication has to be reduced for a better energy efficiency. Considering memory, one has to reduce the amount of stored information on each sensor node.

For in-network clustering, k-center based approaches provide k representatives out of the collected sensor measurements. We propose EDISKCO, an outlier aware incremental method for efficient detection of k-center clusters. Our novel approach is energy aware and reduces amount of required transmissions while producing high quality clustering results. In thorough …