Voronoi Diagram based Indoor Localization in Wireless Sensor Networks

The indoor location fingerprint technique that infers the location based on the received signal strength (RSS) has been adopted in many localization applications, due to its high accuracy and low cost. However, there still lacks an analytical model that can be used to reduce the amount of fingerprints and improve the design of indoor localization system. In this paper, we propose a Voronoi analytical model based on graph theory, and apply this model to analyze the fingerprint structure, yield proximity information and compute the centroid of the Voronoi vertex in the Voronoi region. Furthermore, we compare the measured location and the actual location. Based on the comparison results, we select the smallest Euclidean distance between the two locations as the approximation of the actual location. In order to validate the performance of the analytical model on efficiency and reliability, we conduct an extensive experiment in an indoor parking lot, where it is convenient to deploy the access points (APs). The simulation results illustrate that the mean distance error decreases with the number of access points and collected samples.