Aiming at the anomaly detection problem in sensor data, traditional algorithms usually only focus on the continuity of single-source data and ignore the spatiotemporal correlation between multisource data, which reduces detection accuracy to a certain extent. Besides, due to the rapid growth of sensor data, centralized cloud computing platforms cannot meet the real-time detection needs of large-scale abnormal data. In order to solve this problem, a real-time detection method for abnormal data of IoT sensors based on edge computing is proposed. Firstly, sensor data is represented as time series; K-nearest neighbor (KNN) algorithm is further used to detect outliers and isolated groups of the data stream in time series. Secondly, an improved DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm is proposed by considering spatiotemporal correlation between multisource data. It can be set according to sample characteristics in the window and overcomes the slow convergence problem using global parameters and large samples, then makes full use of data correlation to complete anomaly detection. Moreover, this paper proposes a distributed anomaly detection model for sensor data based on edge computing. It performs data processing on computing resources close to the data source as much as possible, which improves the overall efficiency of data processing. Finally, simulation results show that the proposed method has higher computational efficiency and detection accuracy than traditional methods and has certain feasibility.

中文翻译：

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基于移动边缘计算的物联网传感器异常数据实时检测方法

针对传感器数据中的异常检测问题，传统算法通常只关注单源数据的连续性，而忽略多源数据之间的时空相关性，在一定程度上降低了检测精度。此外，由于传感器数据的快速增长，集中式云计算平台无法满足大规模异常数据的实时检测需求。为了解决这个问题，提出了一种基于边缘计算的物联网传感器异常数据实时检测方法。首先，传感器数据表示为时间序列；K最近邻（KNN）算法还用于检测时间序列中的异常值和孤立的数据流组。其次，考虑到多源数据之间的时空相关性，提出了一种改进的基于密度的应用噪声空间聚类算法。可以根据窗口中的样本特征进行设置，克服了全局参数和大样本的收敛速度慢的问题，然后充分利用数据相关性完成异常检测。此外，本文提出了一种基于边缘计算的分布式传感器数据异常检测模型。它尽可能在靠近数据源的计算资源上执行数据处理，从而提高了数据处理的整体效率。仿真结果表明，与传统方法相比，该方法具有更高的计算效率和检测精度，具有一定的可行性。