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首页> 外文期刊>ACM transactions on sensor networks >BuildSense: Accurate, Cost-aware, Fault-tolerant Monitoring with Minimal Sensor Infrastructure
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BuildSense: Accurate, Cost-aware, Fault-tolerant Monitoring with Minimal Sensor Infrastructure

机译：BuildSense：具有最小传感器基础结构的精确，成本感知，容错监视

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摘要

Buildings can achieve energy-efficiency by using solar passive design, energy-efficient structures and materials, or by optimizing their operational energy use. In each of these areas, efficiency can be improved if the physical properties of the building along with its dynamic behavior can be captured using low-cost embedded sensor devices. This opens up a new challenge of installing and maintaining the sensor devices for different types of buildings. In this article, we propose BuildSense, a sensing framework for fine-grained, long-term monitoring of buildings using a mix of physical and virtual sensors. It not only reduces the deployment and management cost of sensors but can also guarantee accurate and fault-tolerant data coverage for long-term use. We evaluate BuildSense using sensor measurements from two rammed-earth houses that were custom-designed for a challenging hot-arid climate so almost no artificial heating or cooling is required. We demonstrate that BuildSense can significantly reduce the cost of permanent physical sensors while still achieving fit-for-purpose accuracy, fault-tolerance, and stability. Overall, we were able to reduce the cost of a building sensor network by 60% to 80% by replacing physical sensors with virtual ones while still maintaining accuracy of <= 1.0 degrees C and fault-tolerance of two or more predictors per virtual sensor.
机译：建筑物可以通过使用太阳能被动设计，节能结构和材料，或者通过优化其运行能耗来实现节能。如果可以使用低成本的嵌入式传感器设备捕获建筑物的物理特性及其动态行为，则可以在每个这些区域中提高效率。这就提出了针对不同类型建筑物安装和维护传感器设备的新挑战。在本文中，我们提出了BuildSense，这是一个使用物理和虚拟传感器混合对建筑物进行细粒度，长期监控的传感框架。它不仅减少了传感器的部署和管理成本，还可以保证长期使用的准确且容错的数据覆盖范围。我们使用来自两个夯土房屋的传感器测量来评估BuildSense，这两个夯土房屋是为具有挑战性的热干旱气候量身定制的，因此几乎不需要人工加热或冷却。我们证明了BuildSense可以显着降低永久性物理传感器的成本，同时仍可达到适用的精度，容错性和稳定性。总体而言，通过将物理传感器替换为虚拟传感器，我们能够将建筑物传感器网络的成本降低60％至80％，同时仍保持<= 1.0摄氏度的精度以及每个虚拟传感器两个或多个预测变量的容错能力。

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