A Survey on Application of Neural Networks in Energy Conservation of Wireless Sensor Networks

There are many restrictions for Wireless Sensor Networks that enforce us to use low power batteries as their source of energy. Moreover since we often use these networks in rough and inaccessible environments, normally there is low possibility to change or recharge dead nodes. Today, Dynamic Power Management approaches with purpose of reduction of energy consumption in sensor nodes, after deployment and designing of the network draw attentions of many research studies. Therefore, there was a strong interest to use intelligent and capable tools such as Neural Networks in recent years, due to their simple parallel distributed computation, distributed storage, data robustness, auto-classification of sensor readings, dimensionality reduction and sensor data prediction obtained simply from the outputs of the neural-networks algorithms which lead to lower communication costs and energy conservation. This paper aims to present the most important applications of neural networks in reduction of energy consumption of WSNs, up to now.