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Abstract Details
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Activity Number:
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137
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #302809 |
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Title:
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Pattern Discovery and Anomaly Detection in Sensor Networks
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Author(s):
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James Shine*+ and James E. Gentle
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Companies:
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U.S. Army ERDC and George Mason University
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Address:
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, , ,
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Keywords:
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sensors ;
pattern discovery ;
anomaly detection
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Abstract:
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This paper describes an extension of sensor network research performed last year. We have looked in more detail at such issues as differences between groups of sensors, effects of perturbing the data, and modeling normal distributions through the compilation of statistics on sensor activations and interarrival times. We have also modeled different time intervals as independent Poisson processes to look more specifically at times of day that show anomalies, and we have also compared days of the week and times of year. We have also begun work on spatial grouping of sensors and dynamic updating of thresholds and intervals to distinguish anomalies. Results will be presented and discussed.
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