The active badge location system
ACM Transactions on Information Systems (TOIS)
The anatomy of a context-aware application
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Securing context-aware applications using environment roles
SACMAT '01 Proceedings of the sixth ACM symposium on Access control models and technologies
VOR base stations for indoor 802.11 positioning
Proceedings of the 10th annual international conference on Mobile computing and networking
The Horus WLAN location determination system
Proceedings of the 3rd international conference on Mobile systems, applications, and services
Supporting location-based conditions in access control policies
ASIACCS '06 Proceedings of the 2006 ACM Symposium on Information, computer and communications security
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Location awareness is critical for supporting location-based access control (LBAC). The challenge is how to determine locations accurately and efficiently in indoor environments. Existing solutions based on WLAN signal strength either cannot provide high accuracy, or are too complicated in general indoor environments. In this paper, we propose a statistical indoor localization method for supporting location-based access control. In an offline phase, we fit a LOESS [3, 4, 16] local regression model on a training set to build a radio map containing the distribution of signal strength. In an online phase, we estimate locations using Maximum Likelihood Estimation (MLE) [7, 8, 9] based on the measured signal strength and the stored distribution. A Bootstrapping method [11] is further exploited to give a confidence interval of estimation. Compared with others, our method is simpler, more systematic and more accurate. Experimental results show that the average error of our method is less than 2m. Hence, it can better support LBAC applications.