Analysis of Radial Basis Function network for localization framework in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are nowadays extensively preferred for collecting data in the field of disaster management, military operations, habitat monitoring, medical monitoring, and environment monitoring. The location of the sensor which is sending this data is very important for developing efficient routing algorithms, energy efficient communication protocols, and other Quality of services (QoS). Localization is the process by which the sensor motes in the network can identify their own location in the overall network. In this paper, we analyse Radial Basis Function (RBF) Network for developing localization framework in WSNs. RBF based localization framework is to be analysed for faster speed of convergence and low cost of computation. We present analysis of RBF through probabilistic neural network and generalized regression neural network in this paper. The proposed method can be used for designing cost-effective localization framework.