The emergence of WiFi technology and network based applications in the mobile devices is rapidly increasing in terms of computing, communication and sensing capabilities. These trends have made the mobile phone a particularly appealing platform for pervasive network applications. However due to the significant energy consumption of Wifi module and limited battery capacity, growing at a much slower rate, managing and saving power consumption for this type of work is a critical issue. In this paper, we propose an Adaptive Limit-rate Selection algorithm based on Non-parametric Signal Strength Prediction and analysis its potential of improving energy saving. We periodically monitor received signal strength (RSS) in the diverse real-life network environments. Later we apply locally weighted scatter plot smoothing and kernel moving average algorithms to adaptive file download and video streaming, and compare their effectiveness in terms of energy savings. Experiment results demonstrate that our algorithm could save energy consumption compared to non-adaptive method and traditional non-prediction method.

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