## 5 Smart Community
### Smart Environment Management
#### 5.1.1 Air Quality Monitoring
Fog computing [65] and cloud computing [44; 45; 55; 57; 63; 65; 66] are commonly used in data analysis. Usually, fog computing is responsible for data preprocessing, while cloud computing is responsible for data analysis. For example, [PERSON] et al. [65]'s system includes a Local MultiSource Heterogeneous Data Fusion Subsystem (LMFS) and Centralized Homologous Data Training Subsystem (CHTS). The LMFS, in a central cloud, collects data from sensors directly. The CHTS, in distributed fog gateway devices, serves as a distributed federated learning system based on Stochastic Gradient Descent model averaging. The CHTS transfers model parameters for local model training, inference, real-time system, feature engineering, and constructing subclassifiers. In addition, cloud computing would also be used as a database [44; 52; 55; 57]. Azure, as an example of a cloud pipeline, can store and analyze the sensor-generated data through its applications like Stream Analytics, Azure IoT services, Power BI, and Azure ML [45]. Other scholars may store data in SQL databases, such as MySQL [41; 44; 62], SQL database [45]. Some of them may store the data in SD cards [38; 47]. Data preprocessing is necessary to maintain the accuracy of sensor-generated data. For example, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [41] developed a calibration equation for the sensors through linear regression. [PERSON] et al. [43] highlighted some data preprocessing techniques, including the measurement of the mean absolute error of the sensor and Limited-Distance-K-means for clustering. Descriptive analysis and machine learning are major data analysis methods. Descriptive analysis (e.g., line charts [41], bar charts [42]) is used to analyze the data commonly [41; 60]. Some scholars may conduct data visualization [43; 57]. The methods of data visualization include inverse distance weighted interpolation [43] and bilinear interpolation [46]. For instance, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [46] use sensor data to calculate air quality index (AQI) and then construct AQI heatmaps through bilinear interpolation [46]. [PERSON] et al. [42] used segmented regression to transfer the voltage of the sensor to the CO concentration. Multiple linear regression can also be used in data analysis [53]. Currently, machine learning is commonly used to analyze sensor-generated data [44; 52; 55; 57; 63; 65; 66; 67]. The machine learning techniques include neural network [49], support vector machine [52], Long Short-Term Memory [56; 67], and Random Forest [44]. There are three challenges in understanding the relationship between environmental parameters and sensor data, including the lack of detailed observation of environmental data in real time, the automated identification of anomalous events from environmental data, and joint multisensor data and multivariate visualization. Therefore, [PERSON] et al. [57] proposed an effective integrated visualization framework for real-time urban microclimate detection and analysis containing a Bayesian Maximum Entropy-based spatiotemporal identification method and a hyperellipsoidal model-based anomaly detection method. Since atmospheric factors, as well as previous dynamical behavior of its own, may affect the variation in PM diffusion, [PERSON] [49] developed a nonlinear autoregressive network with exogenous inputs from a radial basis function neural network to model the time-dependent causal associations between PM concentration and atmospheric factors. [PERSON] et al. [67] used a Long Short-Term Memory deep learning technique to analyze the weather data from sensors to forecast surface temperature. After data analysis, people tend to provide a holistic view of sensor-generated information on applications, especially mobile applications [44; 49; 51; 52; 55; 56; 58]. The LabVIEW program is one of the platforms for constructing the applications [41]. [PERSON] et al.