A Real-Time Detection Method for Abnormal Data of Internet of Things Sensors Based on Mobile Edge Computing

Xuguang Liu,Yi-Zhang Jiang

doi:10.1155/2021/6655346

Abstract

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.

Highlights

With the continuous development and integration of technologies such as the Internet, IoT, and cloud computing, a large number of sensor devices have been widely used in different fields such as power systems and thermal systems [1, 2]
According to the wireless sensor network (WSN) characteristics, anomaly detection methods are divided into statistics-based, classification-based, clustering-based, and neighbor-based methods
(1) Using the anomaly detection algorithm proposed in this paper, local detection of sensor data including accumulated heat, thermal power, accumulated temperature, flow, and temperature difference is performed. e number of selected sensor data is

Summary

Introduction

With the continuous development and integration of technologies such as the Internet, IoT, and cloud computing, a large number of sensor devices have been widely used in different fields such as power systems and thermal systems [1, 2]. Multisensor data streams have both time and space correlation, and cluster-based methods are usually used for detection. Reference [9] proposed a new anomaly detection algorithm for time series data by constructing a distributed recursive computing strategy and KNN quick selection strategy. There may not be a clear correlation between time series data from different sensors, its inherent characteristics may have a high correlation If these data are uploaded to the data center for feature extraction, it will cause a lot of computational pressure on the data center. A distributed anomaly detection model for sensor data based on edge computing is proposed to process data on computing resources close to the data source as much as possible. Experiments have proved that the proposed algorithm has positive significance for improving algorithm detection accuracy and the overall data processing efficiency

Sensor Data Modeling Based on Time Series

Proposed Real-Time Detection Algorithm for Abnormal Sensor Data

Abnormal Sensor Data Detection Based on DBSCAN

Case Study and Discussion

Methods