Improving security applications using indoor location systems on wireless sensor networks
Proceedings of the International Conference on Advances in Computing, Communication and Control
Distributed Range-Free Localization Algorithm Based on Self-Organizing Maps
WASA '09 Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications
Distributed range-free localization algorithm based on self-organizing maps
EURASIP Journal on Wireless Communications and Networking - Special issue on wireless network algorithms, systems, and applications
Distributed network control for mobile multi-modal wireless sensor networks
Journal of Parallel and Distributed Computing
International Journal of Communication Systems
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We consider the problem of estimating the geographic locations of nodes in a wireless sensor network where most sensors are without an effective self-positioning functionality. We propose LSVM -- a novel solution with the following merits. First, LSVM localizes the network based on mere connectivity information (i.e., hop counts only), and, therefore, is simple and does not require specialized ranging hardware or assisting mobile devices as in most existing techniques. Second, LSVM is based on Support Vector Machine (SVM) learning. Although SVM is a classification method, we show its applicability to the localization problem and prove that the localization error can be upper-bounded by any small threshold given an appropriate training data size. Third, LSVM addresses the border and coverage-hole problems effectively. Last but not least, LSVM offers fast localization in a distributed manner with efficient use of processing and communication resources. We also propose a modified version of mass-spring optimization to further improve the location estimation in LSVM. The promising performance of LSVM is exhibited by our simulation study.