Abstract: With the rapid development of Internet of things (IoT), we have gradually entered into the IoE (Internet of everything) era. In face of the low quality of real-time gathering sensor data in IoT, this paper proposes a novel real-time anomaly detection algorithm based on edge computing for streaming sensor data. This algorithm firstly expresses the corresponding sensor data in the form of time series and establishes the distributed sensing data anomaly detection model based on edge computation. Secondly, this algorithm utilizes the continuity of single-source time series and the correlation between multi-source time series to detect anomaly data from streaming sensor data effectively and respectively. The corresponding anomaly detection result sets are also generated in the same process. Finally, the above two anomaly detection result sets would be effectively fused in a certain way so as to obtain more accurate detection result. In other words, this algorithm achieves a higher detection rate compared with other traditional methods. Extensive experiments on the real-world dataset of household heating data from the Jinan municipal steam heating system, which collects monitoring data from 3084 apartments of 394 buildings, have been conducted to demonstrate the advantages of our algorithm.

Key words: Internet of things, edge computing, time series data, correlation, outlier distance