System architecture directions for networked sensors
ACM SIGPLAN Notices
Templates for the solution of algebraic eigenvalue problems: a practical guide
Templates for the solution of algebraic eigenvalue problems: a practical guide
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Incremental PCA or On-Line Visual Learning and Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
In-Network Outlier Detection in Wireless Sensor Networks
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Analysis of Anomalies in IBRL Data from a Wireless Sensor Network Deployment
SENSORCOMM '07 Proceedings of the 2007 International Conference on Sensor Technologies and Applications
Wireless sensor network survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Group-based intrusion detection system in wireless sensor networks
Computer Communications
Hyperellipsoidal SVM-Based Outlier Detection Technique for Geosensor Networks
GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
WAINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications Workshops
Face Recognition Using Incremental Principal Components Analysis
ICC '09 Proceedings of the 2009 International Conference on Computing, Engineering and Information
Adaptive incremental principal component analysis in nonstationary online learning environments
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Towards in-network data prediction in wireless sensor networks
Proceedings of the 2010 ACM Symposium on Applied Computing
Clustering ellipses for anomaly detection
Pattern Recognition
IEEE Transactions on Information Forensics and Security
Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns
ACM Transactions on Sensor Networks (TOSN)
CCIPCA-OPCSC: An online method for detecting shared congestion paths
Computer Networks: The International Journal of Computer and Telecommunications Networking
Multivariate stream data reduction in sensor network applications
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
TRUSTCOM '11 Proceedings of the 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications
Routing techniques in wireless sensor networks: a survey
IEEE Wireless Communications
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Wireless sensor networks (WSNs) applications are growing rapidly in various fields such as environmental monitoring, health care management, and industry control. However, WSN's are characterized by constrained resources especially; energy which shortens their lifespan. One of the most important factors that cause a rapid drain of energy is radio communication of multivariate data between nodes and base station. Besides, the dynamic changes of environmental variables pose a need for an adaptive solution that cope with these changes over the time. In this paper, a new adaptive and efficient dimension reduction model (APCADR) is proposed for hierarchical sensor networks based on the candid covariance-free incremental PCA (CCIPCA). The performance of the model is evaluated using three real sensor networks datasets collected at Intel Berkeley Research Lab (IBRL), Great St. Bernard (GSB) area, and Lausanne Urban Canopy Experiments (LUCE). Experimental results show 33.33% and 50% reduction of multivariate data in dynamic and static environments, respectively. Results also show that 97-99% of original data is successfully approximated at cluster heads in both environment types. A comparison with the multivariate linear regression model (MLR) and simple linear regression model (SLR) shows the advantage of the proposed model in terms of efficiency, approximation accuracy, and adaptability with dynamic environmental changes.