Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application
Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application
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DOI：
10.1145/1460412.1460445
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发表时间：
2008-11
期刊：
影响因子：
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通讯作者：
E. Miluzzo;N. Lane;K. Fodor;Ronald A. Peterson;Hong Lu;Mirco Musolesi;S. Eisenman;Xiao Zheng
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文献类型：
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作者：
E. Miluzzo;N. Lane;K. Fodor;Ronald A. Peterson;Hong Lu;Mirco Musolesi;S. Eisenman;Xiao Zheng
我们介绍了theCenceMe应用程序的设计、实现、评估和用户体验，该应用程序代表了第一个将使用现成的、具有传感器功能的手机的个人存在的推断与通过Facebook和MySpace等社交网络应用程序共享这些信息相结合的系统。讨论了诺基亚N95手机软件开发面临的系统挑战。我们提出了分级分类的设计和权衡，即个人感知存在（例如，走路，谈话，在健身房）来源于分类器，分类器部分在手机上执行，部分在后端服务器上执行，以实现可扩展的推理。我们报告了性能测量，这些测量描述了软件的计算需求和CenceMe电话客户端的能耗。我们通过一项用户研究来验证该系统，其中包括本科生、研究生和教师在内的22人在一个校园小镇连续使用了三周的时间。从这个用户研究中，我们了解了系统在生产环境中的表现，以及人们对个人传感系统的使用情况。
We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.