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Sequential Anomaly Detection Using Wireless Sensor Networks in Unknown Environment
[chapter]
2014
Human Behavior Understanding in Networked Sensing
Anomaly detection is an important problem for environment, fault diagnosis and intruder detection in Wireless Sensor Networks (WSNs). A key challenge is to minimize the communication overhead and energy consumption in the network when identifying these abnormal events. We present a machine learning (ML) framework that is suitable for WSNs to sequentially detect sensory level anomalies and time-related anomalies in an unknown environment. Our system consists of a set of modular, unsupervised,
doi:10.1007/978-3-319-10807-0_5
fatcat:64w4xspjlrftzjirybgnafn5gy