EnergyBox is a parametrised estimation tool that uses packet traces as input to simulate the energy consumption of communication in mobile devices. This tool models the transmission behaviour of a smart phone by analysing a recorded packet trace from the device. The purpose of the thesis is to reimplement the original EnergyBox energy consumption modelling tool. The project aims to develop support for a graphical user interface (GUI) and a code base that is easier to modify and maintain. The motivation for the reimplementation of the tool is to simplify its usage and to structure the code so that new features can be added. The existing features such as the calculation of total power consumed by the packet trace and the modelling of a device's energy states are reimplemented and new features are developed. Among the new features, a GUI is added to simplify the usage of the application features such as the detection of the recording device's IP address and the ability to alter the configuration parameters used as input to the energy model. The application is written with a GUI and modularity in mind. This is achieved using Java's proprietary new GUI framework - JavaFX, which supports built-in chart and graph GUI elements, that can be easily integrated and supported. The energy modelling engines follow the semantics of the original implementation and the evaluation shows that the new implementation's results are identical to the original tool in 94.94% of the tested cases.

The advancement of mobile clients and applications makes it possible for people to always stayconnected, sending and receiving data constantly. The nature of the 3G technology widelyused, however, causes a high battery drain in cellular phones and because of that a lot of toolsfor measuring mobile phones energy consumption has been developed. In this report we lookinto the trace-driven tool EnergyBox and find out how we can use it to estimate the energyconsumption of 3G transmissions for an application we’ve developed ourselves. We beginwith identifying the types of traffic our application generates and identify which parts of itmake up our applications background traffic. Different combinations of the identified traffictypes are looked into in order to decide which ones that need to be present in the packet tracesfor an estimation of our applications energy footprint for 3G transmission. Further, answersare sought to how long the time span should be for which the packet traces are collected andhow many of them are needed in order to draw a conclusion about our application’s energyfootprint. We conclude that all traffic types responsible for our application’s backgroundtraffic need to be present in the analyzed packet traces, and data suggests that collectingmore than 10 one minute packet traces does not improve accuracy significantly (less than1%). Without user interaction, our application generates traffic, which transmitted over 3G,drains as much as an average of 930mW, meaning that a Samsung Galaxy S4 battery with acapacity of 9.88Wh would last for a maximum of 10 hours and 30 minutes (excluding otherenergy consuming sources inside the handset).

With the steady increase in the use of mobile technologies, studying the energy consumption of mobile applications becomes more interesting. In this thesis, the energy consumption of such applications connected to Long Term Evolution (LTE) cellular networks is studied. Using physical measurements on a mobile device, this thesis aims at characterizing the energy consumption due to communication of a mobile device connected to an LTE network in order to extend EnergyBox. EnergyBox is a tool that estimates the communication energy of mobile devices using packet traces as input.   We perform systematic experiments which exercise the LTE network interface of a mobile device while measuring the consumed power. Using the resulting data and a literature review of the operation of the LTE interface an energy model for LTE is developed. The model is then implemented in EnergyBox. The evaluation of the model is performed by comparing the accuracy of the energy model to physical measurements using five different packet traces from different mobile applications. The results show that the model integrated in EnergyBox provides an average accuracy of between 90% and 100%.