Robust regression and outlier detection
Robust regression and outlier detection
SIA: secure information aggregation in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Distributed deviation detection in sensor networks
ACM SIGMOD Record
The impact of spatial correlation on routing with compression in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
The sybil attack in sensor networks: analysis & defenses
Proceedings of the 3rd international symposium on Information processing in sensor networks
Spatio-temporal correlation: theory and applications for wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: In memroy of Olga Casals
Correlation Analysis for Alleviating Effects of Inserted Data in Wireless Sensor Networks
MOBIQUITOUS '05 Proceedings of the The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services
Hierarchical Anomaly Detection in Distributed Large-Scale Sensor Networks
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
On supporting distributed collaboration in sensor networks
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume II
Designing secure sensor networks
IEEE Wireless Communications
Statistical en-route filtering of injected false data in sensor networks
IEEE Journal on Selected Areas in Communications
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Recent technology in wireless communication has enabled the development of low-cost sensor networks. Sensors at different locations can generate streaming data, which can be analyzed in real-time to identify events of interest. Wireless sensor networks (WSNs) usually have limited energy and transmission capacity, which cannot match the transmission of a large number of data collected by sensor nodes. So, it is necessary to perform in-network data aggregation in the WSN which is performed by aggregator node. Since, the nodes in WSN are vulnerable to malicious attackers and physical impairment; the data collected in WSNs may be unreliable. So, in this paper, we propose an efficient model based technique to detect the unreliable data. Data model is designed using the sound statistical multivariate technique called Principal Component Analysis (PCA). But as a drawback, it is not robust to outliers. Hence, if the input data is corrupted, an arbitrarily wrong representation is obtained. To overcome this problem, we propose two approaches namely Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) to design robust PCA which aids in design of a noise-free data model. The performance of proposed approach is evaluated and compared with previous approaches and found that our approach is effective and efficient.