Data reduction using incremental Naive Bayes Prediction (INBP) in WSN

A Wireless Sensor Network (WSN) consists of spatially distributed autonomous sensor nodes for monitoring environmental conditions. Energy saving by data reduction in WSN is an emerging trend. Energy saving is essential in WSN as sensor nodes are low powered as they are battery operated. Data reduction is technique of data mining, which identifies the redundant data and remove it. Proposed work combines data mining with Wireless Sensor Network using Incremental Naive Bayes Prediction, to remove the redundant data based on prediction. This helps to reduce the number of data entities to be transferred to sink. This is beneficial for saving the energy required for transmission of data to sink. INBP model is compared with two techniques which are simple naive Bayes prediction model and normal transmission model. Weather forecasting data is used as input in this work Proposed work increases the lifetime of the sensor network by considerable amount energy saving.