Applied multivariate statistical analysis
Applied multivariate statistical analysis
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wireless integrated network sensors
Communications of the ACM
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Outlier Detection Algorithms in Data Mining Systems
Programming and Computing Software
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Habitat monitoring with sensor networks
Communications of the ACM - Wireless sensor networks
Security in wireless sensor networks
Communications of the ACM - Wireless sensor networks
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Detecting Malicious Beacon Nodes for Secure Location Discovery in Wireless Sensor Networks
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
SeRLoc: Robust localization for wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Ultra-low power data storage for sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
LAD: localization anomaly detection for wireless sensor networks
Journal of Parallel and Distributed Computing - 19th International parallel and distributed processing symposium
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
The design and evaluation of a mobile sensor/actuator network for autonomous animal control
Proceedings of the 6th international conference on Information processing in sensor networks
Robust management of outliers in sensor network aggregate queries
MobiDE '07 Proceedings of the 6th ACM international workshop on Data engineering for wireless and mobile access
Clustering by competitive agglomeration
Pattern Recognition
DCOSS'07 Proceedings of the 3rd IEEE international conference on Distributed computing in sensor systems
Anomaly detection in wireless sensor networks
IEEE Wireless Communications
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Clustering ellipses for anomaly detection
Pattern Recognition
IEEE Transactions on Information Forensics and Security
Hyperspherical cluster based distributed anomaly detection in wireless sensor networks
Journal of Parallel and Distributed Computing
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Anomalies in wireless sensor networks can occur due to malicious attacks, faulty sensors, changes in the observed external phenomena, or errors in communication. Defining and detecting these interesting events in energy-constrained situations is an important task in managing these types of networks. A key challenge is how to detect anomalies with few false alarms while preserving the limited energy in the network. In this article, we define different types of anomalies that occur in wireless sensor networks and provide formal models for them. We illustrate the model using statistical parameters on a dataset gathered from a real wireless sensor network deployment at the Intel Berkeley Research Laboratory. Our experiments with a novel distributed anomaly detection algorithm show that it can detect elliptical anomalies with exactly the same accuracy as that of a centralized scheme, while achieving a significant reduction in energy consumption in the network. Finally, we demonstrate that our model compares favorably to four other well-known schemes on four datasets.