Reduced power consumption by resource scheduling in mobile cloud using optimized neural network

Abstract

Mobile cloud computing has developed into a modern platform that combines mobile computing with cloud computing in order to process mobile data. In addition to the benefits of mobile cloud storage, energy usage, resource shortage, service efficiency, protection and computational costs are also a big concern. In this article, the neural network-based optimization approaches using artificial neural network and convolutionary neural network models were applied by various variances and loudness in order to minimize overall energy consumption and improve efficiency. Experimental data indicate that the power consumption was lowered by 53.68% by means of an enhancement of the neural network and enhanced by a convolutionary neural network, which further decreased the consumption of energy by 30.3% with a small root average square error relative to other algorithms.