# Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology
## 2 Literature Review
[31] proposed mobile stations for flexible deployment, although battery life remains a challenge; these yield a typical autonomy of 8-12 h. [PERSON] [32] and [PERSON] and [PERSON] [33] explored the integration of smart sensors into urban infrastructure;these reduce maintenance costs but require stable power sources. [PERSON] et al. [34] and [PERSON] et al. [20] advocated for modular architectures with edge computing to minimize cloud-based data processing, thereby reducing energy consumption by 25%. [PERSON] et al. [35] demonstrated a scalable IoT ecosystem with over 200 sensors in its buildings, achieving a 15% energy saving through localized data aggregation. [PERSON] and [PERSON] [36] and [PERSON] et al. [37] applied modular designs to greenhouse and campus monitoring, respectively, suggesting the designs' transferability to air quality systems. [PERSON] et al. [38] and [PERSON] et al. [39] utilized federated learning and real-time optimization to enhance system efficiency, while [PERSON] et al. [40] and [PERSON] and [PERSON] [41] integrated IoT with building management systems to improve indoor air quality. [PERSON] et al. [42] and [PERSON] et al. [43] emphasized scalable architectures for agricultural and smart city applications, respectively, highlighting the need for low-power, cost-effective designs. [PERSON] et al. [44] proposed blockchain-based security mechanisms to ensure data integrity in IoT architectures, indirectly supporting energy-efficient operations by reducing retransmissions. To enhance the autonomy of IoT systems, alternative energy sources are increasingly being explored. [PERSON] et al. [4] implemented solar panels for air quality sensors in tropical environments, achieving up to 90% autonomy in sunny conditions. [PERSON] et al. [45] developed an RF energy-harvesting rectenna, providing 7.6 \(\upmu\)W for low-power IoT devices. [PERSON] et al. [24] explored RF energy harvesting systems for remote sensors, supplementing battery power by 10-15%. [PERSON] et al. [19] and [PERSON] and [PERSON] [36] investigated solar and thermal energy harvesting, with solar panels yielding 50-100 mW under optimal conditions. [PERSON] et al. [46] analyzed energy harvesting for UAV-based systems, noting a 20% improvement in autonomy. These studies highlight the potential of renewable energy but identify challenges such as cost, environmental adaptability, and integration complexity. Despite significant advancements, several gaps persist in the literature. First, there is a lack of standardized frameworks for evaluating energy efficiency across sensor types and protocols [33; 47; 48; 49; 50; 51; 52; 53; 54]. Second, few studies propose integrated architectures that balance scalability, cost, and energy efficiency for both indoor and outdoor applications, as highlighted by [PERSON] and by [PERSON] et al. [22; 32]. Third, the practical implementation of alternative energy sources in large-scale deployments remains limited, as discussed by [PERSON] et al. [4] and [PERSON] et al. [24]. Finally, there is a lack of quantitative metrics, such as efficiency indices, to holistically compare sensor performance, as implied by [PERSON] [15] and [PERSON] et al. [17]. This study addresses these gaps by proposing an energy-efficient IoT system architecture, introducing the IEI for sensor evaluation, and assessing the feasibility of solar-powered and battery-backed systems for sustainable air quality monitoring [55]. Overview of Existing Air Quality Monitoring Systems Based on Internet of Things Technology  
Current IoT-based air quality monitoring system types encompass a variety of approaches, each with its own advantages and disadvantages. Mobile monitoring stations are compact devices that can be mounted on vehicles or carried by users. They provide data collection in different locations and in real time, which contributes to the creation of dense monitoring networks.