Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Localization from Connectivity in Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
A hub matrix theory and applications to wireless communications
EURASIP Journal on Applied Signal Processing
Localization with snap-inducing shaped residuals (SISR): coping with errors in measurement
Proceedings of the 15th annual international conference on Mobile computing and networking
A Spectral Clustering Approach to Validating Sensors via Their Peers in Distributed Sensor Networks
ICCCN '09 Proceedings of the 2009 Proceedings of 18th International Conference on Computer Communications and Networks
A spectral clustering approach to validating sensors via their peers in distributed sensor networks
International Journal of Sensor Networks
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In this paper we introduce a spectral-based method for validating sensor nodes in the field via clustering of sensors based on their measurement data. We formalize the notion of peer consistency in measurement data by introducing a notion called "sensor indexing" and model the problem of identifying bad sensors as a problem of detecting peer inconsistency. Suppose all sensors have peers. Then by examining a certain number of leading eigenvectors of the measurement data matrix, we can identify those bad sensors which are inconsistent to peer sensors in their reported measurements. Further, we show that by deemphasizing or removing measurements obtained from these bad sensors we can improve the performance of sensor-based applications. We have implemented this spectral-based peer validation method and measured its performance by simulation. We report the effectiveness of the method in identifying bad sensors, and demonstrate its use in deriving accurate solutions in a localization application.