Classification techniques for sensor data and clustering architecture for wireless sensor networks

Wireless sensor node is composed of computational unit, sensing unit and a radio unit for communication, all embedded in a tiny unit. Maximum battery power is consumed by communication unit. Battery power is the prime source for wireless sensor node to function. Hence every aspects of wireless sensor network (WSN) are designed with energy constraints. ART1 and Fuzzy ART Neural Network models can be used very efficiently for developing Real time Classifier. Wireless sensor networks demand for the real time classification of sensor data. In this paper classification and clustering techniques using ART1 and Fuzzy ART is discussed. The proposed classifier can be a part of embedded microsensor. The paper discusses classification technique, which can reduce the energy need for communication. Three different clustering architecture are discussed which works at node level, as clustered group of nodes and for extracting several parameters with different sensitivity.