TY - GEN
T1 - Social-Loc
T2 - 11th ACM Conference on Embedded Networked Sensor Systems, SenSys 2013
AU - Jun, Junghyun
AU - Gu, Yu
AU - Cheng, Long
AU - Lu, Banghui
AU - Sun, Jun
AU - Zhu, Ting
AU - Niu, Jianwei
PY - 2013
Y1 - 2013
N2 - Location-based services, such as targeted advertisement, geosocial 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.
AB - Location-based services, such as targeted advertisement, geosocial 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.
KW - Indoor Localization
KW - Middleware
KW - Social Interaction
UR - https://www.scopus.com/pages/publications/84905695725
U2 - 10.1145/2517351.2517352
DO - 10.1145/2517351.2517352
M3 - 会议稿件
AN - SCOPUS:84905695725
SN - 9781450320276
T3 - SenSys 2013 - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
BT - SenSys 2013 - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
PB - Association for Computing Machinery
Y2 - 11 November 2013 through 15 November 2013
ER -