Dynamic WLAN fingerprinting RadioMap for adapted indoor positioning model

Abstract

As a result of Smartphone usage increment a sharp growth in demand for indoor environment computing especially for Location Based Services (LBS) has been occurred. The basic concept of LBS is to determine the mobile users’ location, which is important for services such as tracking or navigation in Civil defense and Healthcare. Currently, there are many techniques used to locate a mobile user in indoor environment. WLAN is considered as one of the best choices for indoor positioning due to its low cost, simple configuration and high accuracy. Although the WLAN Received Signal Strength Indicator (RSSI) fingerprinting method is the most accurate positioning method, it has a serious drawback because it’s Radio Map (RM) become outdated when environmental change occurs. In addition, recalibrating the RM is a time consuming process. This paper presents a novel adapted indoor positioning model which uses the path loss propagation model of the wireless signal to overcome the outdated RM. The experimental results demonstrate that the proposed adapted model is highly efficient in solving the problems mentioned especially in a dynamically changing environment.