Classifying environmental monitoring data to improve wireless sensor networks management

Abstract

Wireless sensor network is considered as the most useful way for collecting data and monitoring the environment. Owing to the large amount of data produced from wireless sensor network, data mining techniques are required to get interesting knowledge. This paper presents the effectiveness of using data mining techniques to discover knowledge that can improve the management of wireless sensor networks in environmental monitoring. Data reduction in wireless sensor network increases the network's lifetime. The classification model can predict the effect of sensed data, which is used to reduce the number of readings that are reported to the sink, in order to improve wireless sensor network management. In this paper, we demonstrate the efficiency and accuracy of using data mining classifiers in predicting the effect of sensed data. The results show that the accuracy of the J48 classification model, multilayer perceptron and REP tree classifiers reached 90%. Using the classification model, the results show that the number of reported readings decreased by 37%. Hence, this significant reduction increases the wireless sensor network's lifetime by reducing the consumed energy, i.e., the total energy dissipated.