Efficient tracing of failed nodes in sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Algorithms for Spatial Outlier Detection
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Detecting Spatial Outliers with Multiple Attributes
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Statistical tools for regional data analysis using GIS
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Distributed deviation detection in sensor networks
ACM SIGMOD Record
Fault Tolerance in Collaborative Sensor Networks for Target Detection
IEEE Transactions on Computers
IEEE Transactions on Computers
iTPS: an improved location discovery scheme for sensor networks with long-range beacons
Journal of Parallel and Distributed Computing
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Location discovery for sensor networks with short range beacons
International Journal of Ad Hoc and Ubiquitous Computing
Perimeter discovery in wireless sensor networks
Journal of Parallel and Distributed Computing
Minimum perimeter coverage of query regions in a heterogeneous wireless sensor network
Information Sciences: an International Journal
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This paper targets the identification of outlying sensors (that is, outlying reading sensors) and the detection of the reach of events in sensor networks. Typical applications include the detection of the transportation front line of some vegetation or animalcule's growth over a certain geographical region. We propose and analyze two novel algorithms for outlying sensor identification and event boundary detection. These algorithms are purely localized and, thus, scale well to large sensor networks. Their computational overhead is low, since only simple numerical operations are involved. Simulation results indicate that these algorithms can clearly detect the event boundary and can identify outlying sensors with a high accuracy and a low false alarm rate when as many as 20 percent sensors report outlying readings. Our work is exploratory in that the proposed algorithms can accept any kind of scalar values as inputs—a dramatic improvement over existing work, which takes only 0/1 decision predicates. Therefore, our algorithms are generic. They can be applied as long as "events” can be modeled by numerical numbers. Though designed for sensor networks, our algorithms can be applied to the outlier detection and regional data analysis in spatial data mining.