Paco: A System-Level Abstraction for On-Loading Contextual Data to Mobile Devices

摘要

Spatiotemporal context is crucial in modern mobile applications that utilize increasing amounts of context to better predict events and user behaviors, requiring rich records of users’ or devices’ spatiotemporal histories. Maintaining these rich histories requires frequent sampling and indexed storage of spatiotemporal data that pushes the limits of resource-constrained mobile devices. Today's apps offload processing and storing contextual information, but this increases response time, often relies on the user's data connection, and runs the very real risk of revealing sensitive information. In this paper, we motivate the feasibility of on-loading large amounts of context and introduce Programming Abstraction for Contextual On-loading (nPacon), an architecture for on-loading data that optimizes for location and time while allowing flexibility in storing additional context. ThenPaconAPI's innovations enable on-loading very dense traces of information, even given devices’ resource constraints. Using real-world traces and our implementation for Android, we demonstrate thatnPaconcan support expressive application queries entirely on-device. Our quantitative evaluation assessesnPacon's energy consumption, execution time, and spatiotemporal query accuracy. Further,nPaconfacilitates unified contextual reasoning across multiple applications and also supports user-controlled release of contextual data to other devices or the cloud; we demonstrate these assets through a proof-of-concept case study.