Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2013

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.

Keywords

Indoor Localization, Social Interaction, Middleware

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 11th ACM Conference on Embedded Network Sensor Systems, SenSys '13, Rome, Italy, 2013 November 11-15

First Page

1

Last Page

14

ISBN

9781450320276

Identifier

10.1145/2517351.2517352

Publisher

ACM

City or Country

Rome, Italy

Additional URL

https://doi.org/10.1145/2517351.2517352

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