Sensor-assisted wi-fi indoor location system for adapting to environmental dynamics
MSWiM '05 Proceedings of the 8th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems
IEEE Transactions on Knowledge and Data Engineering
Activity recognition via user-trace segmentation
ACM Transactions on Sensor Networks (TOSN)
Improving Location Fingerprinting through Motion Detection and Asynchronous Interval Labeling
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
Transferring localization models over time
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Co-localization from labeled and unlabeled data using graph Laplacian
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Accurate and low-cost location estimation using kernels
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A taxonomy for radio location fingerprinting
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
AWESOM: automatic discrete partitioning of indoor spaces for wifi fingerprinting
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
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WLAN location estimation based on 802.11 signal strength is becoming increasingly prevalent in todayýs pervasive computing applications. As an alternative to the well-established deterministic approaches, probabilistic location determination techniques show good performance and thus become increasingly popular. For these techniques to achieve a high level of accuracy, however, adequate training samples should be collected offline for calibration. As a result, a great amount of manual effort is incurred. In this paper, we aim to solve the problem by reducing both the sampling time and the number of locations sampled in constructing the radio map. A learning algorithm is proposed to build location estimation systems based on a small fraction of the calibration data that traditional techniques require and a collection of user traces that can be cheaply obtained. Our experiments show that unlabeled user traces can be used to compensate for the effects of reducing calibration effort and can even improve the system performance. Consequently, manual effort can be significantly reduced while a high level of accuracy is still achieved.