A survey on energy efficient neural network based clustering models in wireless sensor networks

The performance of wireless sensor networks strongly depends on their network lifetime. As a result, Dynamic Power Management approaches with the purpose of reduction of energy consumption in sensor node, after deployment and designing of the network, have drawn attentions of many research studies. Recently, there have been a strong interest to use the intelligent tools especially neural networks in energy efficient approach of Wireless sensor networks, due to their simple parallel distributed computation, distributed storage, data robustness, auto-classification off sensor nodes and sensor reading. Dimensionality reduction and prediction of classification of sensor data obtained simply from the outputs of the neural-networks algorithms can lead to lower communication costs and energy conservation. All these characteristics are well considered in the neural network based algorithms such as ART, ART1, FUZZY ART, IVEBF and EBCS. These algorithms and their performance in improving the lifetime of the WSN are discussed in this paper.