融合移动、传感器和社交数据,充分实现上下文感知计算

Aaron Beach, Mike Gartrell, Xinyu Xing, Richard O. Han, Q. Lv, Shivakant Mishra, K. Seada
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引用次数: 146

摘要

在本文中,我们将移动社交网络确定为移动计算研究的一个重要新方向,并展示了包括传感器网络在内的移动社交网络的扩展定义如何能够实现令人兴奋的新上下文感知应用,例如上下文感知视频屏幕、音乐点唱机和移动健康应用。我们提供的SocialFusion系统能够系统地整合各种移动、社交和传感输入流,并实现适当的情境感知输出动作。我们解释了SocialFusion必须克服的一些主要挑战。我们描述了在实现SocialFusion愿景过程中获得的一些初步结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fusing mobile, sensor, and social data to fully enable context-aware computing
In this paper, we identify mobile social networks as an important new direction of research in mobile computing, and show how an expanded definition of mobile social networks that includes sensor networks can enable exciting new context-aware applications, such as context-aware video screens, music jukeboxes, and mobile health applications. We offer SocialFusion as a system capable of systematically integrating such diverse mobile, social, and sensing input streams and effectuating the appropriate context-aware output action. We explain some of the major challenges that SocialFusion must overcome. We describe some preliminary results that we have obtained in implementing the SocialFusion vision.
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