Highly efficient distance-based anomaly detection through univariate with PCA in wireless sensor networks
MetadataShow full item record
Unsupervised anomaly detection (UAD) techniques have received increasing attention in wireless sensor networks (WSNs). However, the high dimensional training data often make sensor nodes unable to sustain in computation, and result in quite expensive communication overhead. The feature reduction techniques make great sense through the reduction of the dimensionality when the features are strongly interrelated. Among these UAD techniques, distance-based anomaly detection (DB-AD) is a special one that allows to be described by a probability model. Based on this observation, DB-AD is explored deeply with a feature reduction technique, principal component analysis (PCA). Through examining the proportion of the variance explained by the first principal component (PC), a new feature reduction approach is proposed for DB- AD in WSNs, which enables to reduce the dimensionality to one in any situation. Specifically, the first PC is alone used for representing the original data as long as it retains most of the variance; otherwise, the information loss is geometrically reverted to neutralize the error. By obtaining a tradeoff between the detection error and performance overload, this approach is significant for resource-constrained WSNs, as the computational complexity and communication overhead will be reduced to a fraction of the original magnitude. Finally, this approach is evaluated with a real WSN dataset.
Showing items related by title, author, creator and subject.
Adams, M.; Mullane, J.; Vo, Ba-Ngu (2011)Perceptive laser and radar sensors provide information from the surrounding environment and are a critical aspect of many robotics applications. These sensors are generally subject to many sources of uncertainty, namely ...
Li, Jun; Hao, Hong (2015)This paper investigates the feasibility and effectiveness of using a recently developed relative displacement sensor for the structural health monitoring of joint conditions in steel truss bridges. The developed relative ...
Pham, DucSon; Saha, Budhaditya; Phung, Dinh; Venkatesh, Svetha (2012)The data deluge has created a great challenge for data mining applications wherein the rare topics of interest are often buried in the flood of major headlines. We identify and formulate a novel problem: cross-channel ...