SVM-based target tracking in combined with Sensor Scheduling

A new target tracking method is presented to improve target location accuracy in the case of prolonging the network lifetime. The presented method utilizes sensor scheduling to extend the network lifetime and support vector machine for target tracking. Analysis and simulation results show that the algorithm has a high target localization accuracy by comparing with the least-square location method.

[1]  Tarek F. Abdelzaher,et al.  Towards optimal sleep scheduling in sensor networks for rare-event detection , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[4]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[5]  Ying Liang,et al.  Heterogeneous Multi-Sensor Data Fusion with Multi-Class Support Vector Machines: Creating Network Security Situation Awareness , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[6]  Di Tian,et al.  A coverage-preserving node scheduling scheme for large wireless sensor networks , 2002, WSNA '02.

[7]  Xiaorong Zhu,et al.  Hausdorff Clustering and Minimum Energy Routing for Wireless Sensor Networks , 2007, IEEE Transactions on Vehicular Technology.

[8]  Abdelouahid Lyhyaoui,et al.  Support Vector Machines for Multiple Targets Tracking with Sensor Networks , 2008, 2008 New Technologies, Mobility and Security.

[9]  Ulrich H.-G. Kreßel,et al.  Pairwise classification and support vector machines , 1999 .

[10]  Eyuphan Bulut,et al.  DSSP: A Dynamic Sleep Scheduling Protocol for Prolonging the Lifetime of Wireless Sensor Networks , 2007, 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07).

[11]  Duc A. Tran,et al.  Localization In Wireless Sensor Networks Based on Support Vector Machines , 2008, IEEE Transactions on Parallel and Distributed Systems.

[12]  Xiaorong Zhu,et al.  Hausdorff Clustering and Minimum Energy Routing for Wireless Sensor Networks , 2009, IEEE Trans. Veh. Technol..

[13]  Rameswar Debnath,et al.  A decision based one-against-one method for multi-class support vector machine , 2004, Pattern Analysis and Applications.

[14]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.