利用物联网和机器学习实现产业结构资源利用率最小化

A. Rajalingam, G. Charulatha, Kamalakannan Machap, R. Kumudham, M. Prabhu
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引用次数: 0

摘要

与物联网(IoT)相关的系统必须具有较长的电池寿命、较大的覆盖范围和较低的实施成本。商业建筑的供暖、通风和空调(HVAC)解决方案的架构是使用LoRa创建的,并对室内环境中的短距离无线信号进行了评估。这项研究比较了电池寿命、覆盖面积和存储容量等指标。传感器节点的电池使用情况也用LoRa传输功率进行了测试。LoRa的室内覆盖范围比短距离通信大60.4%。高达198%的能源使用可以通过智能控制器的能力来确定何时该区域是空的,暖通空调是关闭的。尽管使用了7.23%的额外功率,LoRa除了提供比RFM 69HW检测器(然后与RFM 69HW收发器进行比较)更重要的58.98%之外,没有显示出容器故障。选择LoRa来实现商业建筑中的智能控制器,因为它需要较少的基站,因此由于结构内部的暴露分类扩展而具有较低的成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Employing IoT and Machine Learning to Minimize Industrial Structure Resource Utilization
Systems associated with the Internet of Things (IoT) must have long battery life, a large coverage area, and low implementation costs. The architecture of Heating, Ventilation, and Air Conditioning (HVAC) solutions in commercial buildings was created using LoRa and evaluated to short-range wireless signals in an indoor setting. This study has compared things like battery life, coverage area, and storage capacity. The sensor node's battery usage was also tested with the LoRa transmission power. LoRa was shown to have a 60.4% greater indoor coverage range than short-range communication. Up to 198% of the energy usage may be saved by the intelligent controller's ability to determine while the area is vacant and the HVAC is turned off. Despite using 7.23% additional power, LoRa exhibited no container failures besides providing a global over58.98 percent more significant over the RFM 69HW detector, which is then compared with the RFM 69HW transceiver. LoRa is chosen for the implementation of smart controller in commercial buildings since it requires fewer base stations and hence has a lower cost because of the expanded exposure assortment inside structures.
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