Energy-Constrained Target Localization Scheme for Wireless Sensor Networks Using Radial Basis Function Neural Network

The indoor object tracking by utilizing received signal strength indicator (RSSI) measurements with the help of wireless sensor network (WSN) is an interesting and important topic in domain location-based applications. Without knowledge location, obtained WSN are no use. trilateration a widely used technique to get location updates target based on RSSI from WSN. However, it suffers high estimation errors arising due random variations measurements. This paper presents range-free radial basis function neural (RBFN) Kalman filtering- (KF-) algorithm named RBFN+KF. performance RBFN+KF evaluated using simulated RSSIs compared against trilateration, multilayer perceptron (MLP), RBFN-based estimations. simulation results reveal that proposed shows very low rest three approaches. Additionally, also seen approach more energy efficient than MLP-based localization