Shai Fine, Yishay Mansour
Machine Learning
To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy consumption, (3) uses only single-hop communication, thus permitting very simple node failure detection and message reliability assurance mechanisms (e. g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance by simulation, using real sensor data streams. Our results demonstrate that our approach is accurate and imposes reasonable communication and power consumption demands. © 2012 Springer-Verlag London Limited.
Shai Fine, Yishay Mansour
Machine Learning
Benjamin N. Grosof
AAAI-SS 1993
Arnold.L. Rosenberg
Journal of the ACM
Fearghal O'Donncha, Albert Akhriev, et al.
Big Data 2021