Junjue Wang

It has been a long endeavour to augment human cognition with machine intelligence. Recently, a new genre of applications, named Wearable Cognitive Assistance, has advanced the boundaries of augmented cognition. These applications continuously process data from body-worn sensors and provide just-in-time guidance to help a user complete a specific task. While previous research has demonstrated the technical feasibility of wearable cognitive assistants, this dissertation addresses the problem of scalability. We identify two critical challenges to the widespread deployment of these applications to be 1) the need to operate cloudlets and wireless network at low utilization to achieve acceptable end-to-end latency 2) the level of specialized skills and the long development time needed to create new applications. To address these challenges, we first design and evaluate adaptation-centric optimizations that reduce resource consumption and improve resource management in contentious systems while maintaining acceptable end-to-end latency. We then propose and implement a new prototyping methodology and a suite of development tools to lower the barrier of application development.