Performing joint learning for passive intrusion detection in pervasive wireless environments
INFOCOM'10 Proceedings of the 29th conference on Information communications
Mobile Networks and Applications
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Journal of Network and Computer Applications
SCPL: indoor device-free multi-subject counting and localization using radio signal strength
Proceedings of the 12th international conference on Information processing in sensor networks
Radio tomographic imaging and tracking of stationary and moving people via kernel distance
Proceedings of the 12th international conference on Information processing in sensor networks
Device-free people counting and localization
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
From RSSI to CSI: Indoor localization via channel response
ACM Computing Surveys (CSUR)
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In traditional radio-based localization methods, the target object has to carry a transmitter (e.g., active RFID), a receiver (e.g., 802.11x detector), or a transceiver (e.g., sensor node). However, in some applications, such as safe guard systems, it is not possible to meet this precondition. In this paper, we propose a model of signal dynamics to allow tracking of transceiver- free objects. Based on Radio Signal Strength Indicator (RSSI), which is readily available in wireless communication, three tracking algorithms are proposed to eliminate noise behaviors and improve accuracy. The midpoint and intersection algorithms can be applied to track a single object without calibration, while the best-cover algorithm has potential to track multiple objects but requires calibration. Our experimental test-bed is a grid sensor array based on MICA2 sensor nodes. The experimental results show that the best side length between sensor nodes in the grid is 2 meters and the best-cover algorithm can reach localization accuracy to 0.99m.