Liquid Level Sensing Using Commodity WiFi in a Smart Home Environment
Liquid Level Sensing Using Commodity WiFi in a Smart Home Environment
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DOI：
10.1145/3380996
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发表时间：
2020-03
影响因子：
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通讯作者：
Yili Ren;Sheng Tan;Linghan Zhang;Zi Wang;Zhi Wang;J. Yang
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文献类型：
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作者：
Yili Ren;Sheng Tan;Linghan Zhang;Zi Wang;Zhi Wang;J. Yang
物联网（IoT）的普及为我们在智能家居环境中实现各种新兴服务提供了前所未有的机会。在这些服务中，感知容器中的液位对于构建许多智能家居和移动医疗保健应用程序至关重要，这些应用程序可以提高生活质量。LiquidSense是一种低成本、高精度的液位传感系统，广泛适用于不同的日常液体和容器，并且可以很容易地与现有的智能家居网络集成。LiquidSense使用现有的家庭WiFi网络和附着在容器上的低成本传感器来感知容器的共振，从而进行液位检测。特别是，我们的系统在容器表面安装了一个低成本的传感器，并发出精心设计的啁啾信号，使容器产生共振，从而对家庭WiFi信号产生微妙的变化。LiquidSense通过分析WiFi信号的细微相位变化，提取出谐振频率作为液位检测的特征。我们的系统分别使用曲线拟合和支持向量机构建连续和离散预测模型。我们用三种不同材料和六种液体的容器在家庭环境中评估LiquidSense。结果表明，LiquidSense对连续预测的总体准确率为97%，对离散预测的总体f值为0.968。结果还表明，我们的系统在家庭环境中具有很大的覆盖范围，并且在非视距（NLOS）场景下工作良好。
The popularity of Internet-of-Things (IoT) has provided us with unprecedented opportunities to enable a variety of emerging services in a smart home environment. Among those services, sensing the liquid level in a container is critical to building many smart home and mobile healthcare applications that improve the quality of life. This paper presents LiquidSense, a liquid level sensing system that is low-cost, high accuracy, widely applicable to different daily liquids and containers, and can be easily integrated with existing smart home networks. LiquidSense uses existing home WiFi network and a low-cost transducer that attached to the container to sense the resonance of the container for liquid level detection. In particular, our system mounts a low-cost transducer on the surface of the container and emits a well-designed chirp signal to make the container resonant, which introduces subtle changes to the home WiFi signals. By analyzing the subtle phase changes of the WiFi signals, LiquidSense extracts the resonance frequency as a feature for liquid level detection. Our system constructs prediction models for both continuous and discrete predictions using curve fitting and SVM respectively. We evaluate LiquidSense in home environments with containers of three different materials and six types of liquids. Results show that LiquidSense achieves an overall accuracy of 97% for continuous prediction and an overall F-score of 0.968 for discrete predication. Results also show that our system has a large coverage in a home environment and works well under non-line-of-sight (NLOS) scenarios.