Abstract:
The availability of advanced sensors on smart-phones allows feeding mobile applications with rich contextual information. Continuous sensing mechanisms in smartphones cost high energy consumption to support accurate contextual recognition. Hence, there is a trade-off between the classification accuracy and the energy consumption that needs to be identified and optimized. In this paper, we formulate the energy-accuracy trade-off as an entropy-based optimization problem, in order to propose an efficient algorithm based on user activity and phone sensor parameters. Experiments demonstrate the gains of the proposed algorithm with 43percent reduction in energy consumption for a case study based on real data traces. © 2013 IEEE.