Abstract
Outlier detection is a well-studied problem in various fields. the unique challenges of wireless sensor networks such as limited bandwidth, memory, energy, and unreliable communication make this problem especially difficult. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we present a new robust communication framework for the unsupervised in network detection of outliers in a wireless sensor network. First, communication is minimized through an ad-hoc collaborative communication scheme which controls sensor behavior to increase overall visibility of individual streaming data sets. Second, an outlier detection algorithm is tailored to fit within this communication model. at the same time, minimal assumptions are made about the nature of the data set as to minimize the loss of generality in the architecture. We also build on our previous foundation to introduce the concept of trust to model anomalous behavior caused by security compromises and hardware failures. We not only prove the convergence of our method but also evaluate the performance which shows the usefulness of our model in comparison to other recent work. Copyright 2012 ACM.
Recommended Citation
D. McDonald et al., "CTOD: Collaborative Tree-Based Outlier Detection in Wireless Sensor Networks," MobiWac'12 - Proceedings of the 10th ACM International Symposium on Mobility Management and Wireless Access, pp. 1 - 10, Association for Computing Machinery (ACM), Dec 2012.
The definitive version is available at https://doi.org/10.1145/2386995.2386997
Department(s)
Computer Science
Keywords and Phrases
Communication; Distributed; Outlier; Unsupervised; Wireless sensor networks
International Standard Book Number (ISBN)
978-145031623-1
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
05 Dec 2012
