Data Mining and Fusion Techniques for Wireless Intelligent Sensor Networks

The Intelligent Wireless sensor networks (WSNs) are autonomous sensing devices that can quickly sense or monitor physical or environmental conditions from the distributed networks. WSN is an integral part of the research area for the real-time system. The Intelligent wireless sensor networks are to accomplish the vast volume actual-time data to take agreement making process that improves the computational technology. That inclines the analysis of the state to traverse the data mining and fusion proficiencies concerning obtaining perception from vast sustained approaching data from intelligent wireless sensor networks. In recent years the intelligent system had been implemented on various techniques similar to data mining and fusion, potency alive routing, task scheduling, reliability, and restriction. In this chapter, we explain the data mining and data fusion technique based on the different types of intelligent wireless sensor networks that detect forest fire. The suggested model is based on the rate of data fusion and the level of information fusion. Information resources are gathered from the intelligent heterogeneous sensors from the forest at the data fusion stage. The fire can be identified in the stage of information fusion by calculating the probabilities of data fusion. The process of fire detection in the forest will be completed with the help of the data that is collected from intelligent wireless sensors. Afterward, it is implemented by the data mining algorithm. We examined the performance of the scheduled data fusion access radically and analyzed it with other measured approaches. Finally, we got the performance of the data mining and data fusion techniques as an intelligent wireless sensor network has improved as compared to others. Besides, we explain the advantages and disadvantages of data mining and data fusion techniques over traditional WSN and intelligent WSN.