论文标题
感测数据融合以增强室内空气质量监测
Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring
论文作者
论文摘要
智能建筑中空气污染物数据的多传感器融合仍然是解决其居民所感知的福祉和舒适感的重要意见。集成传感系统是智能建筑物的一部分，该建筑物的实时室内空气质量数据全天候使用传感器进行监控，并在The Internet Internet（IoT）环境中进行操作。在这项工作中，我们提出了一个空气质量管理系统，通过实时使用传感器数据，将室内空气质量指数（IAQI）和Humidex合并为增强的室内空气质量指数（EIAQI）。在这里，室内空气污染物水平通过废液传感器网络测量，而IAQI和Humidex数据则使用扩展的分数Kalman滤波器（EFKF）融合在一起。根据获得的EIAQI，及时提供了整体空气质量警报，以进行准确的预测，并提高了针对测量噪声和非线性的性能。估计方案是通过使用分数级建模和控制（FOMCON）工具箱来实现的。分析了案例研究以证明所提出方法的有效性和有效性。
Multisensor fusion of air pollutant data in smart buildings remains an important input to address the well-being and comfort perceived by their inhabitants. An integrated sensing system is part of a smart building where real-time indoor air quality data are monitored round the clock using sensors and operating in the Internet-of-Things (IoT) environment. In this work, we propose an air quality management system merging indoor air quality index (IAQI) and humidex into an enhanced indoor air quality index (EIAQI) by using sensor data on a real-time basis. Here, indoor air pollutant levels are measured by a network of waspmote sensors while IAQI and humidex data are fused together using an extended fractional-order Kalman filter (EFKF). According to the obtained EIAQI, overall air quality alerts are provided in a timely fashion for accurate prediction with enhanced performance against measurement noise and nonlinearity. The estimation scheme is implemented by using the fractional-order modeling and control (FOMCON) toolbox. A case study is analysed to prove the effectiveness and validity of the proposed approach.
