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论文类型：会议论文

发表时间：2016-01-01

收录刊物：CPCI-S

页面范围：649-657

关键字：WSNs; Anomaly Detection; Hypercube; KNN; Events; Spatiotemporal Correlations

摘要：The past few years have seen an increased interest in the potential use of wireless sensor networks (WSNs) in applications. Unreliability and a dynamic nature are frequently present in the field of WSN, making anomaly detection necessary. Although events are often functions of more than one attribute and the energy in sensors is limited, the combination of data fusion and spatiotemporal correlations can overcome these limitations effectively. In this paper, we propose a spatiotemporal correlation-based anomaly detection model to differentiate normal and abnormal events in WSNs. The update phase occurs when abnormal events are detected. We demonstrate the usability and advantages of applying the spatiotemporal relevance in anomaly detection. Experimental results indicate its high performance in handling multi-dimensional sensor data.