Anomaly Detection in Wireless Sensor Networks in a Non-Stationary Environment
Open Access
- 14 January 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Communications Surveys & Tutorials
- Vol. 16 (3), 1413-1432
- https://doi.org/10.1109/surv.2013.112813.00168
Abstract
Anomaly detection in a WSN is an important aspect of data analysis in order to identify data items that significantly differ from normal data. A characteristic of the data generated by a WSN is that the data distribution may alter over the lifetime of the network due to the changing nature of the phenomenon being observed. Anomaly detection techniques must be able to adapt to a non-stationary data distribution in order to perform optimally. In this survey, we provide a comprehensive overview of approaches to anomaly detection in a WSN and their operation in a non-stationary environment.Keywords
This publication has 67 references indexed in Scilit:
- Differential Kullback-Leibler Divergence Based Anomaly Detection Scheme in Sensor NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Robust Recursive Eigendecomposition and Subspace-Based Algorithms With Application to Fault Detection in Wireless Sensor NetworksIEEE Transactions on Instrumentation and Measurement, 2012
- Incremental Learning of Concept Drift in Nonstationary EnvironmentsIEEE Transactions on Neural Networks, 2011
- Anomaly detection in streaming environmental sensor data: A data-driven modeling approachEnvironmental Modelling & Software, 2010
- Hardware implementation of a Kullback-Leibler Divergence based signal anomaly detectorPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Separating the Wheat from the Chaff: Practical Anomaly Detection Schemes in Ecological Applications of Distributed Sensor NetworksPublished by Springer Science and Business Media LLC ,2007
- Statistical en-route filtering of injected false data in sensor networksIEEE Journal on Selected Areas in Communications, 2005
- Principal Component Analysis for Distributed Data Sets with UpdatingLecture Notes in Computer Science, 2005
- LOCI: fast outlier detection using the local correlation integralPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Learning in the presence of concept drift and hidden contextsMachine Learning, 1996