Power extraction of human behavior in mobile environment

The popularity of smartphones is growing every day. The hardware components of the mobile devices are renewed almost every six months. however the smartphones still cannot stay awake for more than one day. This is one of the biggest problem today both in the mobile industry and among users as well. The engineers try to reduce the energy usage not just through hardware optimization but also software solutions are made. These solutions are mostly unsuitable for everyday usage, but what happens if we optimize the energy consumption based on the most frequent user habits? The users usually use their devices to be continuously notified about the latest messages, activities, phone calls etc. This behavior can be sometimes unnecessary, for example during the night it is not important to synchronize all e-mails of the users. From that point of view, the users and their devices can form a socio-cognitive channel, which can help to create behavior-based technologies. In this paper we introduce a model which represents the most common smartphone usage scenarios using Markov chains. Based on this model we make suggestions how the processes can be modified to improve the energy consumption of the mobile devices.