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Research Article Open Access
Wireless sensor network node localization is an important research area that creating more innovations in the field of WSN. The exact position of each node is necessary for efficient routing of packets and location-aware services. Existing systems are not producing satisfactory results. In this paper proffers the use of time difference of arrivals (TDOA) information with neural network to estimate the node location. Two artificial neural network models- Radial Bases Function Network and multilayer perceptron models are used for wireless sensor network node localization problem. Time difference of arrival data is used to calculate the distance between anchor nodes to sensor nodes, this information data is used to train and test the neural network models. Simulation result gives the performance Radial Basis Function Network and Multi layer perceptron Network in terms of root mean square error by varying training data density.