The active badge location system
ACM Transactions on Information Systems (TOIS)
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
The smart floor: a mechanism for natural user identification and tracking
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Range-free localization schemes for large scale sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Automatic Tracking of Human Motion in Indoor Scenes Across Multiple Synchronized Video Streams
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Counting People in Crowds with a Real-Time Network of Simple Image Sensors
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Robust statistical methods for securing wireless localization in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
An RF-Based System for Tracking Transceiver-Free Objects
PERCOM '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications
Challenges: device-free passive localization for wireless environments
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Smart cevices for smart environments: Device-free passive detection in real environments
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
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Recent years have witnessed increasing interests in passive intrusion detection for wireless environments, e.g., asset protection in industrial facilities and emergency rescue of trapped people. Most previous studies have focused primarily on exploiting a single intrusion indicator, such as moving variance, for capturing an intrusion pattern at a time. However, in real-world, there are many intrusion patterns which may be only detectable by combining different intrusion indicators and performing detection jointly. To this end, we propose a joint intrusion learning approach, which has the ability in combining the detection power of several complementary intrusion indicators and detects different intrusion patterns at the same time. We developed the GREEK algorithm, which utilizes grid-based clustering over Kneighborhood to effectively diagnose the presence of intrusions. Further, we show that the performance of intrusion detection can be enhanced by utilizing the collaborative detecting efforts among multiple transmitter-receiver pairs. To validate the effectiveness of the joint intrusion learning method, we conducted experiments in a real-office environment using an IEEE 802.15.4 (Zigbee) network. Our experimental results provide strong evidence of the effectiveness of our joint learning approach in performing passive intrusion detection with a minimized false positive rate.