Energy Reduction in Wireless Sensor Networks through Measurement Estimation with Second Order Recurrent Neural Networks

Wireless sensor networks are real time databases to real world phenomena. As wireless sensor networks (WSNs) generally rely on batteries for power, the nodes of the network have a limited operational lifetime. Efficient power consumption is of utmost importance in operation and maintenance of the network. This paper summarizes work in progress in efficient energy consumption during sensor data collecting through time series modeling. A previous approach for energy efficient model in WSNs using a linear time series model for measurement prediction is reviewed and a new model utilizing a non-linear machine learning approach is proposed.