An empirical analysis of the impact of RSS to distance mapping on localization in WSNs

RSS-based localization is one of the most predominant practical techniques for localization in Wireless Sensor Networks (WSNs). However, it is known to be inaccurate due to high RSS variability. In this paper, we experimentally analyze and illustrate the problem of RSS-based localization in WSNs, and we propose a simple Kalman-Filter smoothing technique to reduce RSS variability for the sake of improving the localization accuracy. To evaluate its performance, we investigate our proposed Kalman Filter and a Moving Average Filter to devise a mapping between Smoothed RSS and distance. We show that the localization error is almost less with Kalman Filter than with Moving Average Filter.