Artificial Intelligence Review

, Volume 43, Issue 4, pp 515–563

One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments

  • Nauman Shahid
  • Ijaz Haider Naqvi
  • Saad Bin Qaisar
Article

DOI: 10.1007/s10462-013-9395-x

Cite this article as:
Shahid, N., Naqvi, I.H. & Qaisar, S.B. Artif Intell Rev (2015) 43: 515. doi:10.1007/s10462-013-9395-x

Abstract

Machine learning, like its various applications, has received a great interest in outlier detection in Wireless Sensor Networks. Support Vector Machines (SVM) are a special type of Machine learning techniques which are computationally inexpensive and provide a sparse solution. This work presents a detailed analysis of various formulations of one-class SVMs, like, hyper-plane, hyper-sphere, quarter-sphere and hyper-ellipsoidal. These formulations are used to separate the normal data from anomalous data. Various techniques based on these formulations have been analyzed in terms of a number of characteristics for harsh environments. These characteristics include input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types, outlier identification(event/error), outlier degree, susceptibility to dynamic topology, non-stationarity and inhomogeneity. A tabular description of improvement and feasibility of various techniques for deployment in the harsh environments has also been presented.

Keywords

Wireless sensor networks Harsh environments Outlier detection  Event detection 

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Nauman Shahid
    • 1
  • Ijaz Haider Naqvi
    • 1
  • Saad Bin Qaisar
    • 2
  1. 1.Department of Electrical Engineering, School of Science and EngineeringLahore University of Management SciencesLahore CanttPakistan
  2. 2.Bitsym ResearchBitsym LLCBitsymUSA

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