# Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology
## 2 Literature Review  
The rapid advancement of Internet of Things technologies has transformed air quality monitoring systems, enabling real-time data collection, analysis, and dissemination to address environmental and public health challenges [4, 5, 6, 7, 8, 9]. However, the energy efficiency of these systems remains a critical barrier to their scalability, autonomy, and cost-effectiveness, particularly in large-scale or battery-powered deployments [10, 11, 12, 13]. This section reviews recent studies on IoT-based air quality monitoring systems, focusing on sensor technologies, data transmission protocols, system architectures, and alternative energy sources. By synthesizing the findings from key studies, we identified several research gaps that this study addresses through an energy-efficient IoT system architecture and an IEI for sensor evaluation. Sensors are the cornerstone of IoT-based air quality monitoring systems, and their energy consumption directly impacts system autonomy. [PERSON] et al. [14] conducted a systematic review of indoor and outdoor air pollution monitoring systems, noting that optical dust sensors (e.g., for PM\({}_{2.5}\) and PM\({}_{10}\)) offer high accuracy but consume significant power due to their laser-based operation. [PERSON] [15] highlighted that piezoelectric sensors have lower power consumption, making them suitable for low-cost smart home applications, although they compromise on precision. [PERSON] et al. [16] explored electrochemical gas sensors for detecting NO\({}_{2}\), CO\({}_{2}\), and SO\({}_{2}\), which offer high sensitivity but are prone to environmental interference. [PERSON] et al. [17] and [PERSON] et al. [18] integrated machine learning with IoT sensors to enhance data accuracy while emphasizing low-power designs. Similarly, [PERSON] et al. [19] and [PERSON] et al. [20] demonstrated that adaptive sampling and sleep modes can reduce sensor energy consumption by up to 30%, although the implementation complexity increases. These studies underscore the trade-off between accuracy and energy efficiency, necessitating optimized sensor operation strategies. The choice of data transmission protocol significantly affects the energy efficiency, range, and scalability of IoT systems. [PERSON] et al. [4] developed an open-source IoT system using LoRaWAN, which offers low power consumption (e.g., 10 mW in sleep mode) and long-range communication (up to 15 km), which are ideal for tropical environments. In [21] LoRaWAN was utilized in an AI-powered industrial monitoring system, achieving a 20% reduction in energy use through optimized data transmission. [PERSON] et al. [22] explored NB-IoT (3 rd Generation Partnership Project(3 GPP)), which provides robust coverage and low power consumption (e.g., 50 mW during transmission) for cellular-based deployment. [PERSON] and [PERSON] [23] and [PERSON] et al. [24] highlighted Zigbee's (Developc Products, Aarhus, Denmark) suitability for mesh networks, with power consumption as low as 30 mW, although its range is limited to 100 m. Conversely, [PERSON] et al. (2021) [25] and [PERSON] et al. [26] noted that Wi-Fi and Bluetooth consume significantly more power (e.g., 200-300 mW), making them less suitable for battery-powered systems. [PERSON] et al. [27] and [PERSON] and [PERSON] (2022) [28] explored LoRa and D2D communications for rural and 5G-IoT applications, respectively, suggesting hybrid protocols to balance energy and bandwidth. [PERSON] et al. (2024) [29] introduced reconfigurable intelligent surfaces to enhance signal efficiency, reducing transmission power by 15%. These findings indicate that LoRaWAN and Zigbee are optimal for energy-constrained systems, but hybrid approaches may further improve system efficiency. The architecture of IoT-based air quality monitoring systems influences their functionality, scalability, and energy efficiency. [PERSON] and [PERSON] [30] reviewed fixed monitoring stations, which provide high accuracy but are energy-intensive and limited in coverage. [PERSON] et al.