Artificial Intelligence Review

, Volume 43, Issue 2, pp 193–228

Characteristics and classification of outlier detection techniques for wireless sensor networks in harsh environments: a survey

Authors

    • Department of Electrical EngineeringSchool of Science and Engineering, Lahore University of Management Sciences
  • Ijaz Haider Naqvi
    • Department of Electrical EngineeringSchool of Science and Engineering, Lahore University of Management Sciences
  • Saad Bin Qaisar
    • NUST School of Electrical Engineering and Computer Science
Article

DOI: 10.1007/s10462-012-9370-y

Cite this article as:
Shahid, N., Naqvi, I.H. & Qaisar, S.B. Artif Intell Rev (2015) 43: 193. doi:10.1007/s10462-012-9370-y

Abstract

Wireless sensor networks (WSNs) have received considerable attention for multiple types of applications. In particular, outlier detection in WSNs has been an area of vast interest. Outlier detection becomes even more important for the applications involving harsh environments, however, it has not received extensive treatment in the literature. The identification of outliers in WSNs can be used for filtration of false data, find faulty nodes and discover events of interest. This paper presents a survey of the essential characteristics for the analysis of outlier detection techniques in harsh environments. These characteristics include, input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types(local and global), type of approach(distributed/centralized), outlier identification(event or error), outlier degree, outlier score, susceptibility to dynamic topology, non-stationarity and inhomogeneity. Moreover, the prioritization of various characteristics has been discussed for outlier detection techniques in harsh environments. The paper also gives a brief overview of the classification strategies for outlier detection techniques in WSNs and discusses the feasibility of various types of techniques for WSNs deployed in harsh environments.

Keywords

Wireless sensor networksHarsh environmentsOutlier detection Event detection

Copyright information

© Springer Science+Business Media Dordrecht 2013