An Entropic Approach to Data Aggregation with Divergence Measure Based Clustering in Sensor Network
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In wireless sensor network, data fusion is considered an essential part for preserving energy. Periodic data sampling leads to enormous collection of raw facts, the transmission of which would rapidly deplete the sensor power. In this paper, we have performed data aggregation on the basis of entropy of the sources. The entropy is computed from the local and global probability models. The models provide assistance in extracting high precision data from the sensor nodes. Further, we have proposed an energy efficient method for clustering the sensor nodes. Initially the sensors sensing same category of data are placed within a distinct cluster. The remaining unclustered sensors estimate their divergence with respect to the clustered neighbors and ultimately join the least-divergent cluster. The performance of our proposed methods is evaluated using ns-2 simulator in terms of entropy, aggregation cycles and energy utilization. The simulation results confirm the validity and efficiency of our approach.
KeywordsWireless sensor network node clustering Kullback-Leibler directed divergence measure Jeffrey’s symmetric divergence measure data aggregation entropy fuzzy-entropy local and global probability measure
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