Network Prediction for Adaptive Mobile Applications

Abstract

Prediction of wireless network conditions enables the reconfiguration of mobile applications in a varying network environment, which in turn might gain more energy savings and better quality of service. In this paper, we focus on the prediction of network signal strength and its potential of improving energy saving in network-based power adaptations. We evaluate the performance of three prediction algorithms, namely, ARIMA, Linear regression and NFI, based on the data sets collected from diverse real-life network environments. Later, we apply the network prediction algorithms to adaptive file download, and compare their effectiveness in terms of energy savings. The results show that the adaptations using prediction could save up to 14.7% more energy when compared to prediction-less adaptation.