Robust Distributed Learning in Wireless Sensor Network using Efficient Step Size

Abstract

The distributed learning methods in wireless sensor network are giving better performance when the noise is uniformly distributed in all the sensor nodes. In general practice the noise variance at different nodes varies non uniformly. If constant step size is used for all the nodes then the learning performance will be poor. In order to improve the robustness of incremental least mean squares (ILMS) adaptive learning algorithm against the spatial variation of observation noise statistics over the network, an efficient step–size assignment is presented here. When the noise variance information of all the nodes are available, then the step size parameter in the adaptive algorithm can adjust to obtain better performance. The proposed distributed algorithm is simulated in MATLAB and the performance is evaluated in terms of mean square deviation (MSD), excess mean square error (EMSE) and mean square error (MSE). The simulation result shows the robust against spatial noise variance.