Is my sensor sleeping, hibernating, or broken?: A data-driven monitoring system for indoor energy harvesting sensors
Is my sensor sleeping, hibernating, or broken?: A data-driven monitoring system for indoor energy harvesting sensors
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DOI：
10.1145/3408308.3427625
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发表时间：
2020-11
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影响因子：
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通讯作者：
Alan Wang;Jianyu Su;Arsalan Heydarian;Bradford Campbell;P. Beling
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作者：
Alan Wang;Jianyu Su;Arsalan Heydarian;Bradford Campbell;P. Beling
随着物联网（IoT）设备数量的不断增加，能量收集（EH）设备消除了在室内环境中更换电池或为传感器寻找插座的需要。然而，这是有代价的，因为这些能量收集设备引入了传统物联网设备中不存在的新故障模式：长时间无法收集能量导致它们处于休眠状态，它们通常简单的无线协议不可靠，并且它们有限的能量储备禁止许多诊断功能。虽然能量收集传感器承诺易于安装和免维护部署，但其局限性阻碍了强大的长期数据收集。为了持续监测和维护建筑物中的能量收集设备网络，我们提出了eh -管家。EH-管家是一个数据驱动的系统，可监控EH设备的合规性，并根据现有网关位置和建筑物配置文件预测建筑物中的健康信号区域，以便于设备维护。eh -管家通过首先过滤多余的事件触发数据点，并在描述网关和设备之间路径的构建特征上应用表示学习来实现这一点。我们通过在17000平方英尺的研究基础设施中部署125个不同类型的能量收集传感器来评估eh -管家，随机屏蔽四分之一的传感器作为验证的测试集。我们6个月的数据收集期的结果表明，在预测子集的健康状态方面，平均准确率超过80%。我们的结果验证了跨设备类型评估传感器健康状态、推断网关状态的技术，以及帮助识别网关、传输和传感器故障的方法。
As the number of Internet of Things (IoT) devices continues to increase, energy-harvesting (EH) devices eliminate the need to replace batteries or find outlets for sensors in indoor environments. This comes at a cost, however, as these energy-harvesting devices introduce new failure modes not present in traditional IoT devices: extended periods of no harvestable energy cause them to go dormant, their often simple wireless protocols are unreliable, and their limited energy reserves prohibit many diagnostic features. While energy-harvesting sensors promise easy-to-setup and maintenance-free deployments, their limitations hinder robust, long-term data collection. To continuously monitor and maintain a network of energy-harvesting devices in buildings, we propose the EH-HouseKeeper. EH-HouseKeeper is a data-driven system that monitors EH device compliance and predicts healthy signal zones in a building based on the existing gateway location(s) and building profile for easier device maintenance. EH-HouseKeeper does this by first filtering excess event-triggered data points and applying representation learning on building features that describe the path between the gateways and the device. We assessed EH-HouseKeeper by deploying 125 energy-harvesting sensors of varying types in a 17,000 square foot research infrastructure, randomly masking a quarter of the sensors as the test set for validation. The results of our 6-month data-collection period demonstrate an average greater than 80% accuracy in predicting the health status of the subset. Our results validate techniques for assessing sensor health status across device types, for inferring gateway status, and approaches to assist in identifying between gateway, transmission, and sensor faults.