Many modern smart building applications are supported by wireless sensors to sense physical parameters, given the flexibility they offer and the reduced cost of deployment. However, most wireless sensors are powered by batteries today, and large deployments are inhibited by the requirement of periodic battery replacement. Energy harvesting sensors provide an attractive alternative, but they need to provide adequate quality of service to applications given the uncertainty of energy availability. We propose ACES, which uses reinforcement learning to maximize sensing quality of energy harvesting sensors for periodic and event-driven indoor sensing with available energy. Our custom-built sensor platform uses a supercapacitor to store energy and Bluetooth Low Energy to relay sensors data. Using simulations and real deployments, we use the data collected to continually adapt the sensing of each node to changing environmental patterns and transfer learning to reduce the training time in real deployments. In our 60-node deployment lasting 2 weeks, nodes stop operations only 0.1% of the time, and collection of data is comparable with current battery-powered nodes. We show that ACES reduces the node duty-cycle period by an average of 33% compared to three prior reinforcement learning techniques while continuously learning environmental changes over time.

中文翻译：

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王牌

鉴于无线传感器提供的灵活性和降低的部署成本，许多现代智能建筑应用程序都由无线传感器支持以感测物理参数。然而，当今大多数无线传感器都由电池供电，并且定期更换电池的要求阻碍了大规模部署。能量收集传感器提供了一种有吸引力的替代方案，但鉴于能源可用性的不确定性，它们需要为应用程序提供足够的服务质量。我们提出 ACES，它使用强化学习来最大限度地提高能量收集传感器的感知质量，以利用可用能量进行周期性和事件驱动的室内感知。我们定制的传感器平台使用超级电容器来存储能量，并使用蓝牙低功耗来中继传感器数据。使用模拟和实际部署，我们使用收集到的数据来不断调整每个节点的感知以适应不断变化的环境模式和迁移学习，以减少实际部署中的训练时间。在我们持续 2 周的 60 节点部署中，节点停止运行的时间仅为 0.1%，并且数据收集与当前的电池供电节点相当。我们表明，与之前的三种强化学习技术相比，ACES 将节点占空比平均减少了 33%，同时随着时间的推移不断学习环境变化。