Energy efficient data aggregation in wireless sensor networks using neural networks Online publication date: Sun, 21-May-2017
by Fereshteh Khorasani; Hamid Reza Naji
International Journal of Sensor Networks (IJSNET), Vol. 24, No. 1, 2017
Abstract: Energy efficiency is very important issue in wireless sensor networks (WSNs). In WSN, sensors are distributed in different places, where they can be exposed to contact with the environment. Data aggregation, eliminating of data redundancy and improve the accuracy of the collected data are essential points for these networks. This research has been suggested some algorithms such as MEDA, LMTBPN, RBDA and RGDA. The first algorithm is based on the moment estimation method and the other three algorithms aggregate the data based on backward propagation, radial basis and general regression. These algorithms use a three-layer neural network. Input layer neurons are located in members of each cluster while the hidden layer neurons are located in cluster heads and output layer neurons are located in base station. Simulation results show that the performance of data aggregation is improved and also energy consumption of the network is reduced.
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