Comprehensive Outlier Detection in Wireless Sensor Network with Fast Optimization Algorithm of Classification Model

Haiqing Yao,Heng Cao,Jin Li

doi:10.1155/2015/398761

Abstract

Since the nonstationary distribution of the detected objects is general in the real world, the accurate and efficient outlier detection for data analysis within wireless sensor network (WSN) is a challenge. Recently, with high classification precision and affordable complexity, one-class quarter-sphere support vector machine (QSSVM) has been introduced to deal with the online and adaptive outlier detection in WSN. Regarding the one-sided consideration of optimization or iterative updating algorithm for QSSVM model within current techniques, we have proposed comprehensive outlier detection methods in WSN based on the QSSVM algorithm. To reduce the complexity of optimization algorithm for QSSVM model in existing techniques, a fast optimization algorithm based on average Euclidean distance has been developed and employed to the comprehensive outlier detection method. Evaluated by real and synthetic WSN data sets, our methods have shown an excellent outlier detection performance, and they have been proved to meet the requirements of online adaptive outlier detection in the case of nonstationary detection tasks of WSN.

Highlights

Due to the development of modern information and electronics technology, wireless sensor network (WSN) has been widely used in smart home, logistics, industrial detection, and automation fields with the advantages of low cost and miniaturization
We will further evaluate the performance of Comprehensive Outlier Detection (COD) and Fast Comprehensive Outlier Detection (FCOD) algorithms based on quarter-sphere support vector machine (QSSVM) with linear kernel function from the aspects of outlier detection rate (DR), false positive rate (FPR), and computation complexity
Outlier detection rate is the rate of true positives to outlier, and false positive rate is the rate of false positives to normal data

Summary

Introduction

Due to the development of modern information and electronics technology, WSN has been widely used in smart home, logistics, industrial detection, and automation fields with the advantages of low cost and miniaturization. To ensure high outlier detection accuracy and efficiency under these situations, online adaptive outlier detection algorithms with model update, optimization capability, and low computation complexity are required. As an unsupervised learning algorithm, QSSVM has excellent classification accuracy and moderate computation complexity and has been introduced into the adaptive outlier recognition in WSN. In QSSVM, parameter ] can significantly affect the classification accuracy and further represent the outlier rate of training set (ORTS) [6]. The optimal parameter ] can be obtained by Golden Section Search algorithm on the standard deviation (SD) of normal data in training set [7]. Considering that outlier is minority in WSN data set and has temporal continuity, accurate and efficient model updating strategies should be involved in outlier detection algorithm. The two strategies still have some limitations [7, 8], they represent the latest development of outlier detection algorithms based on QSSVM

Related Works

Problem Statement and Formulations

Comprehensive Outlier Detection Algorithm in WSN

Experimental Results and Evaluation

Conclusions