WicLoc: An indoor localization system based on WiFi fingerprints and crowdsourcing

Abstract

WiFi fingerprint-based indoor localization techniques have been proposed and widely used in recent years. Most solutions need a site survey to collect fingerprints from interested locations to construct the fingerprint database. However, the site survey is labor-intensive and time-consuming. To overcome this shortcoming, we record user motions as well as WiFi signals without the active participation of the users to construct the fingerprint database, in place of the previous site survey. In this paper, we develop an indoor localization system called WicLoc, which is based on WiFi fingerprinting and crowdsourcing. We design a fingerprint model to form fingerprints of each location of interest after fingerprint collection. We propose a weighted KNN (K-Nearest Neighbor) algorithm to assign different weights to APs and achieve room-level localization. To obtain the absolute coordinate of users, we design a novel MDS (Multi-Dimensional Scaling) algorithm called MDS-C (Multi-Dimensional Scaling with Calibrations) to calculate coordinates of interested locations in the corridor and rooms, where anchor points are used to calibrate absolute coordinates of users. Experimental results show that our system can achieve a competitive localization accuracy compared with state-of-the-art WiFi fingerprint-based methods while avoiding the labor-intensive site survey.