## 2 Background and Related Work
### Related Work

Table 1: Summary of related work on smart environment monitoring systems. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Paper** & **Year** & **Monitored Air Component** & **Hardware** & **Real Time** & **Kind of Local/Remote Computation** & **Level of Intelligence/Automation** & **Application Scenarios** \\ \hline
[24] & 2017 & Ozone (O\({}_{3}\)) & Arduino BT IoT Prototyping Board & Yes & Local & Generate warnings when levels exceed a predetermined threshold value & Specific indoor environment \\ \hline
[25] & 2019 & \(\begin{array}{c}\text{O}_{3}\text{, NO}_{2}\text{, temperature, and}\\ \text{relative humidity}\end{array}\) & Sensors with testbeds & Semi real & Local & Comparing and evaluating calibration methods & Indoor and outdoor environments \\ \hline
[26] & 2019 & \(\begin{array}{c}\text{CO}_{2}\text{, CO}\text{, and CH}_{4}\text{ gases}\end{array}\) & \begin{tabular}[]{c} Arduino Uno, ESP266, gas sensors, and Android devices \\ \end{tabular} & Yes & Remote & Prediction and alert notification & Outdoor air monitoring in smart cities \\ \hline Our & 2024 & \(\begin{array}{c}\text{O}_{3}\text{, temperature, humidity}\end{array}\) & \begin{tabular}[]{c} DHT-11 and KH-NFOD-100 sensors, \\ Raspberry/P, UVC Lamp \\ \end{tabular} & \begin{tabular}[]{c} Amazon AWS and IoT technology for both cloud and edge computing \\ \end{tabular} & \
\begin{tabular}[]{c} Autonomous UV-C lamp (de)activation, anomaly \\ detection \\ \end{tabular} &
\begin{tabular}[]{c} Public indoor \\ environments \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Recent advancements in the monitoring of indoor air quality (IAQ) have capitalized on the IoT to significantly improve the collection, analysis, and interaction with real-time data. Numerous studies underscore the integration of IoT technology with various sensors, facilitating continuous monitoring and instantaneous updates concerning indoor air quality parameters. For example, the Smart-Air device incorporates sensors for pollutants such as aerosols, volatile organic compounds (VOCs), carbon monoxide (CO), and carbon dioxide (CO\({}_{2}\)) alongside LTE technology for the transmission of real-time data and cloud-based analysis, thereby demonstrating practical applications in environments such as Hanyang University [13]. Likewise, modular IoT platforms, which include sensor nodes and gateways, accentuate real-time data collection and adaptability, as evidenced by implementations at Qatar University [14]. Other systems employ custom hardware and the Constrained Application Protocol (CoAP) for device communication, thereby accommodating a variety of indoor environments [15]. Research frequently emphasizes the integration of sensors and the design of systems, utilizing an array of sensors such as BME680 (BOSCH, Gerlingen, Germany) and CCS811 (ScoiSense, Eindhoven, The Netherlands) to monitor parameters including CO\({}_{2}\), VOCs, particulate matter (PM2.5), and temperature [16; 19]. These systems employ a range of communication interfaces, including RS485, LoRa, and WiFi, thereby showcasing diverse methodologies for data transmission and system design [20]. The automation and real-time functionalities of these systems are also of considerable significance. Systems equipped with alert mechanisms notify users via SMS when air quality declines [19], while cloud platforms are capable of dispatching notifications when pollutant concentrations surpass predetermined thresholds [22]. Specialized systems designed for monitoring specific pollutants, such as ozone in proximity to photocopy machines, exemplify targeted automation in designated environments [24]. Data storage and analysis represent crucial elements within these monitoring systems. The prevalent utilization of cloud computing platforms and local databases for data storage and analysis is noteworthy.