An Efficient Outlier Detection Approach for Streaming Sensor Data Based on Neighbor Difference and Clustering

Abstract

In wireless sensor networks (WSNs), the widely distributed sensors make the real-time processing of data face severe challenges, which prompts the use of edge computing. However, some problems that occur during the operation of sensors will cause unreliability of the collected data, which can result in inaccurate results of edge computing-based processing; thus, it is necessary to detect potential abnormal data (also known as outliers) in the sensor data to ensure their quality. Although the clustering-based outlier detection approaches can detect outliers from the static data, the feature of streaming sensor data requires the detection operation in a one-pass fashion; in addition, the clustering-based approaches also do not consider the time correlation among the streaming sensor data, which leads to its low detection accuracy. To solve these problems, we propose an efficient outlier detection approach based on neighbor difference and clustering, namely, ODNDC, which not only quickly and accurately detects outliers but also identifies the source of outliers in the streaming sensor data. Experiments on a synthetic dataset and a real dataset show that the proposed ODNDC approach achieves great performance in detecting outliers and identifying their sources, as well as the low time consumption.

TimeTector: A Twin-Branch Approach for Unsupervised Anomaly Detection in Livestock Sensor Noisy Data (TT-TBAD)

“…Therefore, there is a need for a dependable mechanism that enables precise identification and interpretation of such data. Several approaches for analyzing spatial dependencies in multiple sensors have been studied [ 7 , 8 ]. To deal with such challenges, researchers have developed various methods, e.g., data-driven anomaly detection [ 9 ] and maximum mean discrepancy (MMD) [ 10 ].…”