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A distributed sensing data anomaly detection scheme
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DOI：10.1016/j.cose.2020.101960.png)
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With the continuous development of Internet of Things, the information society has gradually entered a new era of the Internet of everything. Sensor nodes are important sources of data in the Internet of Things. The abnormal and failure of sensing data in the Internet of Things will affect the connectivity of the network. If the accuracy and reliability of the corresponding perception data can be effectively improved, we can timely and accurately find out the emergency and monitor the working status of the network. Therefore, it is of great significance to detect the abnormal data of data streams in the sensor network nodes and confirm its source. For the low quality of sensor data collected in real time in IoT, this paper proposes an anomaly detection method for sensing data streams based on edge computing. In this algorithm, the sensor data is expressed in the form of time series. On the edge computing based sensor data anomaly detection model, the improved confidence interval is used to detect whether the data is abnormal. The concept of interval difference is proposed as the judgment of the source of the anomaly. The accuracy and effectiveness of the algorithm are verified by experiments. The results show that the detection rate of abnormal data is above 98%, which indicates that the algorithm has certain practicability. (c) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Internet of things
Time series
Distributed anomaly detection
Confidence interval
Data stream
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