Optimizing energy consumption in modern mobile handheld devices plays a very important role as lowering energy consumption impacts battery life and system reliability. With next-generation smartphones and tablets, the number of sensors and communication tools will increase and more and more communication interfaces and protocols such as Wi-Fi, Bluetooth, GPRS, UMTS, and LTE will be incorporated. Consequently, the fraction of energy consumed by these components will be larger. Nevertheless, the use of the large amount of data from the different sensors can be beneficial to detect the changing user context, to understand habits, and to detect running application needs. All these information, when used properly, may lead to an efficient energy consumption control. This paper proposes a tool to analyze user/application interaction to understand how the different hardware components are used at run-time and optimize them. The idea here is to use machine learning methods to identify and classify user behaviors and habit information. Using this tool, a software has been developed to control at run-time system component activities that have high impacts on the energy consumption. The tool allows also to predict future applications usages. By this way, screen brightness, CPU frequency, Wi-Fi connectivity, and playback sound level can be optimized while meeting the applications and the user requirements. Our experimental results show that the proposed solution can lower the energy consumption by up to 30 % versus the out-of-the-box power governor, while maintaining a negligible system overhead.
['Ismat Chaib Draa', 'Smail Niar', 'Jamel Tayeb', 'Emmanuelle Grislin', 'Mikael Desertot']
Sensing user context and habits for run-time energy optimization