Analyzing data mining techniques for wireless sensor network protocols

In this paper, a structure for extracting and mining wireless sensor data. This structure consists of a new formulation for data mining technique using the association rule by distributed extraction method. The new design express the sequential relations between wireless sensors and association rules using data mining technique for estimating the value of missed events and also to improve the performance. The proposed distributed method is designed with wireless sensor network to improve the network life time by minimizing number of messages required for the mining process. The experiment results have shown that our methodology can reduce the number of exchanged messages at least by 40% compared to the existing other methods. The wireless sensor network automatically collects the information or data. The compressed representation structure (CRS) is able to compress the data and sensor network provides easy and fast collection of data. The mining process of the CRS is compared and analyzed with the FP-Growth, the existing mining algorithm, results have shown that our method is better than FP-Growth.