%0 Conference Paper %1 6524427 %A Singh, P. %A Agrawal, S. %B 2013 International Conference on Communication Systems and Network Technologies %D 2013 %K deep_learning ml4pos tdoa %P 400-404 %R 10.1109/CSNT.2013.90 %T TDOA Based Node Localization in WSN Using Neural Networks %U http://ieeexplore.ieee.org/document/6524427/#full-text-section %X In wireless sensor network, the exact positions of the sensor nodes is necessary for location-aware services. Traditional approaches are not producing satisfactory results. In this paper we propose the use of Time Difference of Arrival (TDOA) information with Neural network for accurate node localization. We use two artificial neural network models-Back Propagation Network (BPN) and Radial Basis Function (RBF) Network model for Wireless Sensor Network's node localization problem. Time Difference of Arrival (TDOA) data is used to calculate the distance information from anchor nodes to sensor nodes. This distance information was used to train the neural networks' models. Simulation results show the superiority of Radial Basis Function Network over Back Propagation Network in terms of root mean square error when training data density is high.

@inproceedings{6524427, abstract = {In wireless sensor network, the exact positions of the sensor nodes is necessary for location-aware services. Traditional approaches are not producing satisfactory results. In this paper we propose the use of Time Difference of Arrival (TDOA) information with Neural network for accurate node localization. We use two artificial neural network models-Back Propagation Network (BPN) and Radial Basis Function (RBF) Network model for Wireless Sensor Network's node localization problem. Time Difference of Arrival (TDOA) data is used to calculate the distance information from anchor nodes to sensor nodes. This distance information was used to train the neural networks' models. Simulation results show the superiority of Radial Basis Function Network over Back Propagation Network in terms of root mean square error when training data density is high.}, added-at = {2018-02-14T20:51:34.000+0100}, author = {Singh, P. and Agrawal, S.}, biburl = {https://www.bibsonomy.org/bibtex/2958496731f80e223d9e4978a9f058d77/dallmann}, booktitle = {2013 International Conference on Communication Systems and Network Technologies}, description = {TDOA Based Node Localization in WSN Using Neural Networks - IEEE Conference Publication}, doi = {10.1109/CSNT.2013.90}, interhash = {11d599cbd5604eaa2157002adbeea00d}, intrahash = {958496731f80e223d9e4978a9f058d77}, keywords = {deep_learning ml4pos tdoa}, month = {April}, pages = {400-404}, timestamp = {2018-04-03T17:19:23.000+0200}, title = {TDOA Based Node Localization in WSN Using Neural Networks}, url = {http://ieeexplore.ieee.org/document/6524427/#full-text-section}, year = 2013 }