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.

中文翻译：

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用于增强室内空气质量监测的传感数据融合

智能建筑中空气污染物数据的多传感器融合仍然是解决居民感知福祉和舒适度的重要输入。集成传感系统是智能建筑的一部分，其中使用传感器全天候监控实时室内空气质量数据，并在物联网 (IoT) 环境中运行。在这项工作中，我们提出了一种空气质量管理系统，通过实时使用传感器数据，将室内空气质量指数 (IAQI) 和湿气指数合并为增强型室内空气质量指数 (EIAQI)。在这里，室内空气污染物水平由黄蜂传感器网络测量，同时使用扩展分数阶卡尔曼滤波器 (EFKF) 将 IAQI 和湿气数据融合在一起。根据获得的EIAQI，及时提供整体空气质量警报，以进行准确预测，并增强了针对测量噪声和非线性的性能。估计方案​​是通过使用分数阶建模和控制 (FOMCON) 工具箱实现的。分析了一个案例研究，以证明所提出方法的有效性和有效性。