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

Saihua Cai,Baoquan Yin,Chi Zhang,Haibo Chen,Jingyi Chen,Jinfu Chen,Min Lin,Ruizhi Sun,Shifeng Sun

doi:10.1155/2022/3062541

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.

Highlights

In recent years, wireless sensor networks (WSNs) have been widely used in a variety of applications, such as object positioning [1], health management [2], industrial safety control [3], and social media [4,5,6]
WSNs usually consist of lots of low-cost sensor nodes distributed over a wide area, which leads to the data in WSNs being generated in the form of streams. at is, the data in WSNs are arriving instantly and continuously, and the generating speed of the data is very quick. e use of a large number of sensors makes the real-time processing of data face more severe challenges, while edge computing [7, 8] provides flexible and on-demand processing power to quickly process the data in WSNs
E ODNDC approach contains three algorithms, including outlier detection based on neighbor difference (ODND), outlier detection based on clustering (ODC), and outlier sources identification based on correlation (OSIC)

Summary

Introduction

Wireless sensor networks (WSNs) have been widely used in a variety of applications, such as object positioning [1], health management [2], industrial safety control [3], and social media [4,5,6]. To solve the problems existing in clustering-based outlier detection approaches, with the use of w-k-means algorithm (which assigns different weights to each attribute according to the correlation between attributes to reduce the impact of irrelevant attributes on the clustering results and solving the problem of other k-means algorithms for their high false alarms caused by the irrelevant data), this paper proposes an efficient outlier detection approach based on neighbor difference and clustering, namely, ODNDC, to accurately detect the outliers and identify the source of outliers in the streaming sensor data collected from WSNs. e major contributions of this work are concluded as follows:. (3) Based on a synthetic dataset and a real dataset, we conduct extensive experiments to evaluate the efficiency of the ODNDC approach, and the experimental result verifies that the proposed ODNDC approach can accurately detect potential outliers from streaming sensor data and identify the sources of outliers as well as cost in a short time.

Related Works

Preliminaries

Proposed Approach

Experimental Results

Conclusions