Adaptive Principal Component Analysis-Based Outliers Detection Through Neighborhood Voting in Wireless Sensor Networks

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Author(s): Ekaterina Aleksandrova (University of Glasgow, UK)and Christos Anagnostopoulos (University of Glasgow, UK)
Copyright: 2019
Pages: 31
Source title: Next-Generation Wireless Networks Meet Advanced Machine Learning Applications
Source Author(s)/Editor(s): Ioan-Sorin Comşa (Brunel University London, UK)and Ramona Trestian (Middlesex University, UK)
DOI: 10.4018/978-1-5225-7458-3.ch011

Keywords: Information Science Reference / Media & Communications / Networking and Telecommunications / Wireless Systems

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Abstract

This chapter introduces statistical learning methods and findings of a group decision-making algorithm in internet of things (IoT) and edge computing environments. The discussed methodology locally detects outliers in an on-line and adaptive mode. It is driven by three perspectives—opinion, confidence, and independence—and exploits the incremental principal component analysis using the power method for eigenvector and eigenvalue estimation and Knuth and Welford's online algorithms for variance estimation. The methodology is implemented and evaluated over real contextual data in a wireless network of environmental sensors from where appropriate conclusions are drawn about the capabilities and limitations of the proposed solution in IoT environments.

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Adaptive Principal Component Analysis-Based Outliers Detection Through Neighborhood Voting in Wireless Sensor Networks

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