Abstract
A key issue in wireless sensor network applications is how to accurately detect anomalies in an unstable environment and determine whether an event has occurred. This instability includes the harsh environment, node energy insufficiency, hardware and software breakdown, etc. In this paper, a fault-tolerant anomaly detection method (FTAD) is proposed based on the spatial-temporal correlation of sensor networks. This method divides the sensor network into a fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood and other regions for anomaly detection, respectively, to achieve fault tolerance. The results of experiment show that under the condition that 45% of sensor nodes are failing, the hit rate of event detection remains at about 97% and the false negative rate of events is above 92%.
Highlights
In recent years, wireless sensor networks (WSN) have been widely used in defense, military, healthcare, environmental monitoring, manufacturing, and many other fields [1,2]
In temporal correlation of sensor network, we propose the PCM and interval methods; In spatial correlation we divide the sensor network into fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood, and other regions for anomaly detection, respectively, to achieve fault tolerance
According to the definition of neighborhood, the sensor network is divided into a fault neighborhood, event and fault mixed neighborhoods, event boundary neighborhoods, and other areas to detect anomalies, respectively, and the event nodes and faulty nodes are distinguished by the minimum Bayesian risk decision
Summary
Wireless sensor networks (WSN) have been widely used in defense, military, healthcare, environmental monitoring, manufacturing, and many other fields [1,2] They consist of a large number of nodes distributed in a geographical area, and are usually limited by the energy, storage capacity, computing power, and communication bandwidth. We propose a fault-tolerant anomaly detection method (FTAD). In temporal correlation of sensor network, we propose the PCM and interval methods; In spatial correlation we divide the sensor network into fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood, and other regions for anomaly detection, respectively, to achieve fault tolerance. The fourth section introduces the detection method of fault-tolerance of wireless sensor networks.
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