Social-Loc

TLDR

Location‑based services such as targeted advertising, geo‑social networking, and emergency services are increasingly popular, yet indoor localization remains challenging because GPS is accurate only outdoors and indoor methods typically require additional infrastructure or extensive training. The authors propose Social‑Loc, a middleware that uses social encounter and non‑encounter events to calibrate estimation errors of any underlying indoor localization system. Social‑Loc was fully implemented on Android and evaluated on two systems—dead‑reckoning and WiFi fingerprinting. Experiments show Social‑Loc improves WiFi fingerprint accuracy by at least 22 % and dead‑reckoning by at least 37 %, and simulations confirm its scalability, long‑term accuracy, and robustness to measurement errors.

Abstract

Location-based services, such as targeted advertisement, geo-social networking and emergency services, are becoming increasingly popular for mobile applications. While GPS provides accurate outdoor locations, accurate indoor localization schemes still require either additional infrastructure support (e.g., ranging devices) or extensive training before system deployment (e.g., WiFi signal fingerprinting). In order to help existing localization systems to overcome their limitations or to further improve their accuracy, we propose Social-Loc, a middleware that takes the potential locations for individual users, which is estimated by any underlying indoor localization system as input and exploits both social encounter and non-encounter events to cooperatively calibrate the estimation errors. We have fully implemented Social-Loc on the Android platform and demonstrated its performance on two underlying indoor localization systems: Dead-reckoning and WiFi fingerprint. Experiment results show that Social-Loc improves user's localization accuracy of WiFi fingerprint and dead-reckoning by at least 22% and 37%, respectively. Large-scale simulation results indicate Social-Loc is scalable, provides good accuracy for a long duration of time, and is robust against measurement errors.