Data sets collected from wireless sensor networks (WSN) are usually considered unreliable and subject to errors due to limited sensor capabilities and hard environmental resulting in a subset of the sensors data called outlier data. This paper proposes a technique to detect outlier data base on spatial-temporal similarity among data collected by geographically distributed sensors. The proposed technique is able to identify an abnormal subset of data collected by sensor node as outlier data. Moreover the proposed technique is able to classify this abnormal observation, an error data set or event affected set. Simulation result shows that high detection rate is achieved compared to conventional outlier detection techniques while preserving low positive false alarm rate.
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