Minimizing Distribution Cost of Distributed Neural Networks in Wireless Sensor Networks

This paper presents a novel study on how to distribute neural networks in a wireless sensor networks (WSNs) such that the energy consumption is minimized while improving the accuracy and training efficiency. Artificial neural network (ANN) learning has been shown robust to noisy and uncertain sensory data for function approximation and pattern classification applications. With the advances of miniature hardware technologies for powerful sensor nodes, embedded neural networks will emerge as important decision-making brains for WSNs and vast surveillance applications to enable adaptive data quality and self-managing capabilities. To distribute neural networks in WSNs in an energy-efficient manner, we propose parallel transmission and adaptive neural selection algorithms(ANSA) in multilayer backpropagation(MLBP) learning process of neural networks, which is a popular supervised learning technique used for training feedforward artificial neural networks. We further analyze the energy consumption components in the online training process and evaluate the reduced energy consumption using our proposed algorithms.