Uncertainty Handling for Sensor Location Estimation in Wireless Sensor Networks using Probabilistic Fuzzy System

Abstract

With the increasing use of location-sensitive applications, a variety of localization techniques in wireless sensor networks (WSN) have been introduced to date; each aiming to improve accuracy of location estimation. However, most of these studies do not consider the factor of uncertainty which is naturally present in the localization problem. Focusing on range-free localization, this research examines RSS-based location estimation models. Due to environmental conditions, RSS can be influenced by several stochastic factors at the receiving node. In this research, we propose an algorithm using Probabilistic Fuzzy Logic Systems (PFLS) for the first time to address randomness and uncertainty in the location estimation problem. After optimizing PFLS by Genetic Algorithm (GA), the performance of both PFLS and the GA-optimized PFLS are compared with several other competing strategies under varying noise levels. Experimental results clearly indicate the improved efficiency of the proposed algorithm, particularly at higher noise levels.