DrOPS: Model-driven optimization for Public Sensing systems
DrOPS: Model-driven optimization for Public Sensing systems
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DOI：
10.1109/percom.2013.6526731
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发表时间：
2013-03
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影响因子：
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通讯作者：
D. Philipp;Jaroslaw Stachowiak;Patrick Alt;Frank Dürr;K. Rothermel
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作者：
D. Philipp;Jaroslaw Stachowiak;Patrick Alt;Frank Dürr;K. Rothermel
现代智能手机的激增催生了公共传感，这是一种利用移动参与者的智能手机进行数据采集的新范式。在本文中，我们介绍了一个提高公共传感系统中数据采集效率的系统Drops。Drops利用了一种模型驱动的方法，通过从模型推断读数来减少移动智能手机所需的读数数量。此外，该模型还可用于推断没有传感器的位置的读数。该模型是以在线方式从观察到的现象直接构建的。将这些模型与客户指定的质量界限一起使用，我们可以显著减少数据采集的工作量，同时仍能向客户报告所需质量的数据。为此，我们开发了一套在线学习和控制算法来创建和验证观察到的现象的模型，并提出了一个使用我们的算法的传感任务执行系统。我们的评估表明，我们在几个小时甚至几分钟内就能得到模型。使用模型驱动的方法优化数据采集，我们可以节省高达80%的通信能源，并在100%的时间内为符合1°C误差范围的未覆盖位置提供推断温度读数。
The proliferation of modern smartphones has given rise to Public Sensing, a new paradigm for data acquisition systems utilizing smartphones of mobile participants. In this paper, we present DrOPS, a system for improving the efficiency of data acquisition in Public Sensing systems. DrOPS utilizes a model-driven approach, where the number of required readings from mobile smartphones is reduced by inferring readings from the model. Furthermore, the model can be used to infer readings for positions where no sensor is available. The model is directly constructed from the observed phenomenon in an online fashion. Using such models together with a client-specified quality bound, we can significantly reduce the effort for data acquisition while still reporting data of required quality to the client. To this effect, we develop a set of online learning and control algorithms to create and validate the model of the observed phenomenon and present a sensing task execution system utilizing our algorithms in this paper. Our evaluations show that we obtain models in a matter of just hours or even minutes. Using the model-driven approach for optimizing the data acquisition, we can save up to 80% of energy for communication and provide inferred temperature readings for uncovered positions matching an error-bound of 1°C up to 100 % of the time.