The active badge location system
ACM Transactions on Information Systems (TOIS)
Location-based authentication: grounding cyberspace for better security
Internet besieged
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
PinPoint: An Asynchronous Time-Based Location Determination System
Proceedings of the 4th international conference on Mobile systems, applications and services
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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 to accommodate to different indoor environments. In this paper, we propose a statistical indoor localization method for supporting location-based access control. First, in an offline training phase, we fit a locally weighted regression and smoothing scatterplots (LOESS) model on the signal strength received at different training locations, and build a radio map that contains the distribution of signal strength. Then, in an online estimation phase, we determine the locations of unknown points using maximum likelihood estimation (MLE) based on the measured signal strength and the stored distribution. In addition, we provide a 95% confidence interval to our estimation using a Bootstrapping module. Compared with other approaches, our method is simpler, more systematic and more accurate. Experimental results show that the estimation error of our method is less than 2m. Hence, it can better support LBAC applications than others.