Energy Efficient and Fair Management of Sensing Applications on Heterogeneous Resource Mobile Devices

Abstract

The widespread use of sensor-equipped smartphones and wearables has enabled the rapid growth of personal sensing applications that monitor user behavior. Examples include continuously sampling the microphone for the detection of stressed speech or processing the accelerometer sensor stream for transportation mode detection. Deploying multiple of these applications on the mobile device is a rich source of behavioral insights but poses a significant strain on the battery life. The aim of the dissertation work is to make full use of the heterogeneous resources available in off-the-shelf smartphones and wearables (viz. CPU, low-power co-processors and wireless) to efficiently and fairly orchestrate the execution of multiple interacting sensing applications. This requires building an abstraction scheduling layer that transparently distributes sensor processing tasks and optimizes the utilization of shared computing resources to meet individual app requirements.