Open AccessSmart outlier detection of wireless sensor network
Author(s) -
Sahar Mohamed Kamal,
Rabie A. Ramadan,
Fawzy EL-Refai
Publication year - 2016
Publication title -
facta universitatis. series electronics and energetics/facta universitatis. series: electronics and energetics
Language(s) - English
Resource type - Journals
eISSN - 2217-5997
pISSN - 0353-3670
DOI - 10.2298/fuee1603383k
Subject(s) - wireless sensor network , outlier , anomaly detection , computer science , constant false alarm rate , data mining , data set , set (abstract data type) , pattern recognition (psychology) , real time computing , artificial intelligence , computer network , programming language
Data sets collected from wireless sensor networks (WSN) are usually considered unreliable and subject to errors due to limited sensor capabilities and hard environment 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.