A Fuzzy Logic-Based Approach for Node Localization in Mobile Sensor Networks

In most range-based localization methods, inferring distance from radio signal strength using mathematical modeling becomes increasingly unreliable and complicated in indoor and extreme environments, due to effects such as multipath propagation and signal interference. We propose FuzLoc, a range-based, anchor-based, fuzzy logic enabled system system for localization. Quantities like RSS and distance are transformed into linguistic variables such as Low, Medium, High etc. by binning. The location of the node is then solved for using a nonlinear system in the fuzzy domain itself, which outputs the location of the node as a pair of fuzzy numbers. An included destination prediction system activates when only one anchor is heard; it localizes the node to an area. It accomplishes this using the theoretical construct of virtual anchors, which are calculated when a single anchor is in the node’s vicinity. The fuzzy logic system is trained during deployment itself so that it learns to associate an RSS with a distance, and a set of distances to a probability vector. We implement the method in a simulator and compare it against other methods like MCL, Centroid and Amorphous. Extensive evaluation is done based on a variety of metrics like anchor density, node density etc

A fuzzy logic approach to localisation in wireless local area networks

This thesis examines the use and value of fuzzy sets, fuzzy logic and fuzzy inference in wireless positioning systems and solutions. Various fuzzy-related techniques and methodologies are reviewed and investigated, including a comprehensive review of fuzzy-based positioning and localisation systems. The thesis is aimed at the development of a novel positioning technique which enhances well-known multi-nearest-neighbour (kNN) and fingerprinting algorithms with received signal strength (RSS) measurements. A fuzzy inference system is put forward for the generation of weightings for selected nearest-neighbours and the elimination of outliers. In this study, Monte Carlo simulations of a proposed multivariable fuzzy localisation (MVFL) system showed a significant improvement in the root mean square error (RMSE) in position estimation, compared with well-known localisation algorithms. The simulation outcomes were confirmed empirically in laboratory tests under various scenarios. The proposed technique uses available indoor wireless local area network (WLAN) infrastructure and requires no additional hardware or modification to the network, nor any active user participation. The thesis aims to benefit practitioners and academic researchers of system positioning