Mobile devices are powered by batteries whose capacities are limited by size, so efficient management of energy becomes a very important issue in such devices. Many solutions have been proposed seeking to extend mobile device's battery life, but there are only a few who takes the user as a decisive and determining factor. Only the user determines how to consume battery's energy. Therefore the proposed application aims to solve this issue by learning about user's context. By analyzing this information, automated actions are performed in order to optimize energy consumption.

Nowadays, the main problem we found in most smartphones is the poor battery life when using applications that make an intensive use of connectivity services. This becomes a serious problem in specific applications such as telemedicine; where services such as WiFi and 3G connection are the largest consumers of energy, but are essential for the transfer of data required by these applications. This paper examines different energy consumption studies on mobile devices via WiFi and 3G. After this analysis, we propose and test a method for choosing the network with lower energy consumption based on the characteristics of the data transfer context that the application requires.