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Feature Selection and Energy Management for Wireless Sensor Networks


Moh'd Alwadi, Girija Chetty


Vol. 12  No. 6  pp. 46-51


Energy efficiency is a key issue in wireless sensor networks where the energy sources and battery capacity are very limited. In this paper we propose a novel pattern recognition based formulation for minimizing the energy consumption in wireless sensor networks. The proposed scheme involves an algorithm to rank and select the sensors from the most significant to the least, and followed by a na?ve Bayes classification. Assuming that each feature represents a sensor in the wireless sensor network, various data sets with multiple features are considered to show that feature ranking and selection could play a key role for the energy management. We have examined Isolet, ionosphere and forest cover type datasets from the UCI repository to emulate the wireless sensor network scenario. From our simulation results, we show that it is possible to achieve two important objectives using the proposed scheme (1) Increase the lifetime of the wireless sensor network, by using optimal number of sensors, and (2) Manage sensor failures with optimal number of sensors without compromising the accuracy.


Wireless sensor networks, feature ranking, feature selection, data sets, accuracy, life time extension factor, WEKA machine learning framework