Non-Parametric RSS Prediction Based Energy Saving Scheme for Moving Smartphones

With the emergence of WiFi technology and network-based applications, the computing, communication and sensing capabilities of smartphones are increasing rapidly, and the smartphone has emerged as a particularly appealing platform for pervasive network applications. However, WiFi entails considerable energy consumption on these battery-powered devices. Finding ways to reduce power consumption on smartphones becomes a critical issue. In this paper, we propose an adaptive limit-rate selection algorithm based on anon-parametric signal strength prediction scheme and analyze its potential for energy savings. By periodically monitoring the received signal strength (RSS) in diverse network environments, the proposed scheme applies weighted scatter plot smoothing and kernel moving average algorithms to adaptively adjust file downloading and video streaming rates. Experimental results demonstrate that the proposed scheme can save 5.7% energy at least and 13.9% energy at most compared to non-adaptive and non-prediction schemes when the smartphone holders use the applications on the move.