Abstract
With the vast developments of microelectronics, a multitude of sensors can now be packed into a smart phone. This has enabled many context-aware applications that utilize the rich set of sensors to sense the environmental contexts, system status, and user activities. For a smart phone, it is common that different applications may request the same context, while a context may be inferred by different combinations of sensors. Current phone systems do not coordinate the use of sensors by the context-aware applications, leading to redundant sensors activated and unnecessary energy consumption. In this thesis, we propose an Energy/Accuracy-Optimized Framework (EAOF), which sits between the applications and the low-level sensors to provide a coordinated and optimized use of sensors to satisfy the context-sensing requirements of the applications. The use of sensors may be optimized based on two criteria: energy-optimized, which minimizes the total energy consumption while maintaining a target accuracy, and accuracy-optimized, which maximizes the overall accuracy under a given energy budget. Our experimental results show that the power consumption can be reduced by 30.45% in the energy-optimized mode and 19.91% in the accuracy-optimized mode, compared with the original, uncoordinated sensor management on Android.