An Efficient Data Streams Mining Method for Wireless Sensor Network's Data Aggregation

Wireless distributed sensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. The data model generated by sensor network is data streams. Because of the rapid data arriving speed and huge size of data set in stream model, novel one-pass algorithms are devised to support data aggregation on demand. In this paper, we focus on data aggregation, which can have significant impact on sensor network. VFDT is one of the most successful algorithms for data streams mining, which uses Hoeffding inequality to achieve a probabilistic bound on the accuracy of the tree constructed; we revisit this problem and propose an efficient algorithm for handling wireless sensor network’s streaming data. In order to examine this algorithm, we study its performance with different data noise level, number of sensor network nodes and number of data. Overall, the techniques introduced here can handle wireless sensor network’s data efficiently.

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