Accurate indoor localization based on RSSI with adaptive environmental parameters in wireless sensor networks

Abstract

Most of the applications in wireless sensor networks require accurate estimation of the location of a user or a mobile robot. However indoor localization cannot be effectively done using Global Positioning Systems (GPS). Recently wireless sensor networks are concerned with estimating the location of sensors using Received Signal Strength Indication (RSSI), although all the existing techniques that use RSSI have a bad accuracy of estimating the location. In this paper we have introduced a new algorithm using anew empirical method (RIMO's Empirical method) that significantly increases the accuracy of localization with minimum processing time and minimum power consumption. Our new empirical method can be extended to be used for outdoor applications with very high accuracy.