Zoning-based Localization in Indoor Sensor Networks Using Belief Functions Theory

Abstract

Localization is an essential issue in wireless sensor networks to process the information retrieved by sensor nodes. This paper presents an indoor zoning-based localization technique that works efficiently in real environments. The targeted area is composed of several zones, the objective being to find the zone where the mobile node is instantly located. The proposed approach collects first strengths of received WiFi signals from neighboring access points and builds a fingerprints database. It then uses belief functions theory to combine all measured data and define an evidence framework, to be used afterwards for estimating the most probable node's zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.