Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Comparison of different implementations of MFCC
Journal of Computer Science and Technology
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Vehicle classification in distributed sensor networks
Journal of Parallel and Distributed Computing
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Military communications systems and technologies
Fast, accurate event classification on resource-lean embedded sensors
EWSN'11 Proceedings of the 8th European conference on Wireless sensor networks
Fast, Accurate Event Classification on Resource-Lean Embedded Sensors
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
An algorithm on fairness verification of mobile sink routing in wireless sensor network
Personal and Ubiquitous Computing
Towards augmenting federated wireless sensor networks in forestry applications
Personal and Ubiquitous Computing
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In this paper, an efficient target classification and fusion scheme for wireless sensor networks (WSNs) is proposed and evaluated. When a classification algorithm for WSN nodes is designed, parametric approaches such as Gaussian mixture model (GMM) should be more preferred to non-parametric ones due to the hard limitation in resources. The GMM algorithm not only shows good performances for target classification in WSNs but it also requires very small resources. Based on the classifier, a decision tree generated by the classification and regression tree algorithm is used to fuse the information from heterogeneous sensors. This node-level classification scheme provides a satisfactory classification rate, 94.10%, with little resources. Finally, a confidence-based fusion algorithm improves the overall accuracy by fusing the information among sensor nodes. Our experimental results show that the proposed group-level fusion algorithm improves the accuracy by an average of 4.17% accuracy with randomly selected nodes.