From Human Mobility to Data Mobility: Leveraging Spatiotemporal History in Device-to-Device Information Diffusion

Michel, Jonas; Julien, Christine

doi:10.1109/mdm.2016.39

Cited by 1 publication

(1 citation statement)

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“…Implementing applications that use such rich spatiotemporal context information is possible given today's technical capabilities [1]. Today's mobile devices already largely collect this information, whether by applications that collect spatiotemporal traces explicitly, such as fitness and lifestyle apps 1 , by applications that collect (potentially spotty) spatiotemporal traces implicitly, for example via social network checkins, or, more insidiously, by service providers themselves. Furthermore, it has been shown that when users are presented with the frequency that applications request contextual information and sensor data, they are likely to adjust or revoke the permissions [2].…”

Section: Introductionmentioning

confidence: 99%

<sc>Paco</sc>: A System-Level Abstraction for On-Loading Contextual Data to Mobile Devices

Wendt

Julien

2018

IEEE Trans. on Mobile Comput.

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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 PACO (Programming Abstraction for Contextual On-loading), an architecture for on-loading data that optimizes for location and time while allowing flexibility in storing additional context. The PACO API'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 that PACO can support expressive application queries entirely on-device. Our quantitative evaluation assesses PACO's energy consumption, execution time, and spatiotemporal query accuracy. Further, PACO facilitates 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. ! • N. Wendt and C. Julien are with the Nathaniel Wendt. Nathaniel is a graduate student at the University of Texas at Austin researching applications of mobile context for preserving privacy, enabling better monitoring and learning in mobile platforms and the IoT. Nathaniel primarily focuses on middleware that leverages mobile context to enable simplified and more intelligent application programming in pervasive computing environments.Christine Julien. Dr. Julien is a professor in the Department of Electrical and Computer Engineering at the University of Texas at Austin. Her research focuses on the intersection of software engineering and dynamic, unpredictable networked environments and develops models, abstractions, tools, and middleware whose goals are to ease the software engineering burden associated with building applications for pervasive and mobile computing environments. Dr.

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Section: Introductionmentioning

confidence: 99%