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The increasing usage of smartphones in everyday tasks has been motivated manystudies on energy consumption characterization aiming to improve smartphonedevices' effectiveness and increase user usage time. In this scenario, it isessential to study mechanisms capable of characterizing user usage patterns, sosmartphones' components can be adapted to promote the best user experience withlower energy consumption. The goal of this study is to build an energyconsumption model based on user usage patterns aiming to provide the bestaccurate model to be used by application developers and automated optimization.To develop the energy consumption models, we established a method to identifythe components with the most influence in the smartphone's energy consumptionand identify the states of each influential device. Besides that, weestablished a method to prove the robustness of the models constructed usinginaccurate hardware and a strategy to assess the accuracy of the model built.After training and testing each strategy to model the energy consumption basedon the user's usage and perform the Nemenyi test, we demonstrated that it ispossible to get a Mean Absolute Error of 158.57mW when the smartphone's averagepower is 1970.1mW. Some studies show that the leading smartphone's workload isthe user. Based on this fact, we developed an automatic model buildingmethodology that is capable of analyzing the user's usage data and build smartmodels that can estimate the smartphone's energy consumption based on theuser's usage pattern. With the automatic model building methodology, we canadopt strategies to minimize the usage of components that drain the battery.

Building Energy Consumption Models Based On Smartphone User's Usage Patterns