CTOD: collaborative tree-based outlier detection in wireless sensor networks

Authors:
Dylan McDonald;Sanjay Madria;Fikret Ercal;Ryan Birmingham;Thomas Lake
Affiliations:
Missouri University of Science and Technology, Rolla, MO, USA;Missouri University of Science and Technology, Rolla, MO, USA;Missouri University of Science and Technology, Rolla, MO, USA;Missouri University of Science and Technology, Rolla, MO, USA;Western Michigan University, Kalamazoo, MI, USA
Venue:
Proceedings of the 10th ACM international symposium on Mobility management and wireless access
Year:
2012

Citing 10
Cited 0

LOF: identifying density-based local outliers

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An adaptive energy-efficient MAC protocol for wireless sensor networks

Proceedings of the 1st international conference on Embedded networked sensor systems
In-Network Outlier Detection in Wireless Sensor Networks

ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Online outlier detection in sensor data using non-parametric models

VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Outlier detection in sensor networks

Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing
Castalia: revealing pitfalls in designing distributed algorithms in WSN

Proceedings of the 5th international conference on Embedded networked sensor systems
Unsupervised Outlier Detection in Sensor Networks Using Aggregation Tree

ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data

PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Outlier Detection Techniques for Wireless Sensor Networks: A Survey

IEEE Communications Surveys & Tutorials
Nearest neighbor pattern classification

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

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 communi- cation 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.