Study of Use of Classification Techniques in WSN Data Mining for Resource Optimization - Parbat, B.A; Dhuware, R.K (International Journal of Computer Sciences and Engineering, 2025)
With the wide application of Wireless Sensor Network Technology, a large volume of data is generated. For extracting knowledgeful, understandable and valid patterns from this data, data mining techniques are used. This Wireless Sensor Network Data Mining may use Centralized Mining Approach or Distributed Mining approach. Distributed mining, mining is applied on sensor nodes. After that mined data are sent to sink node. But, in centralized approach whole data from sensor nodes are collected at sink node then mining is applied on dataset. This paper focuses on Centralized Data Mining Approach to mine dataset. Here, Classification Techniques, SVM (support Vector Machine) and KNN (K-Nearest Neighbour), are applied on this collected dataset with taking concentration on optimization of CPU cycle as compressible resource. For this execution time to classify data is used here. For this real dataset, it is resulting that KNN is giving better performace than SVM. The dataset is gathered from a real time data acquisition system based on wireless sensor network that is implemented using XBee Digi modules and open source hardware platform Arduino. It is trying to make a hybrid framework, combination of Distributed Approach and Centralized Approach, for this real time deployment of WSN as a future work.