# Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology
## 5 Discussion  
### Architecture of an Energy-Efficient IoT Air Quality Monitoring System  
The architecture of an energy-efficient IoT-based air quality monitoring system is based on the principle of minimizing power consumption at all levels of its operation--rangingfrom sensor hardware to data processing and communication. The proposed system is modular and includes several key subsystems: the sensor layer, data transmission, processing and storage, the user interface, and power management (Figure 2). The novelty of the proposed architecture lies in the modular integration of energy-efficient components, such as Smart Air sensors, a Zigbee gateway, and a Mini UPS, using the integrated efficiency index (IEI) to optimize the selection of sensors in terms of functionality, energy efficiency, and cost. This approach ensures the scalability and practicality of the system for smart city applications, offering a quantitative tool for decision-making in the design of IoT systems. At the physical level, the system employs low-energy sensors that are capable of measuring key atmospheric parameters, such as carbon dioxide concentration (CO2), particulate matter (PM2.5, PM10), temperature, humidity, and volatile organic compounds (TVOCs). To ensure high autonomy, devices such as the Ajax LifeQuality or SaveEcoSensor are used, which are optimized for battery operation. Additionally, adaptive sampling strategies are implemented: the frequency of measurements increases in response to a deterioration in air quality indicators, while remaining minimal under stable conditions. For data transmission, the system integrates energy-efficient wireless protocols. In particular, LoRaWAN is utilized for long-distance, low-power communication in outdoor environments, while Zigbee is employed in local sensor networks due to its mesh topology and low consumption. Communication between sensors, gateways, and servers is facilitated by the MQTT protocol, which minimizes packet size and transmission frequency, further reducing energy use. In weak signal conditions, IoT monitoring systems become vulnerable to delays, data loss, and automation failures. This is especially critical in scenarios where a rapid response to changes in air quality is required (e.g., ventilation systems when CO2 levels rise), so the following is recommended:  
* Before implementation, signal coverage should be analyzed (using tools such as Zigbee2 MQTT Map or the Wi-Fi analyzer);  
Figure 2: A typical energy-efficient IoT air quality monitoring system. After implementation, the user should continuously monitor connection stability. The collected data are processed both locally and in the cloud. Local platforms such as Home Assistant (Version: 2025.7.1. Developer: Nabu Casa, Inc. City: Tualatin. State: Oregon. Country: USA) or OpenHAB (Version: 4.3.5. Manufacturer: openHAB Foundation e.V. Country: Germany) handle real-time data visualization and the execution of automated actions, such as controlling ventilation or air purification systems. Cloud services (e.g., AWS IoT (Manufacturer: Amazon Web Services (a division of AWS. City: AWS IoT headquarters - Seattle, WA, USA) or Firebase (Version: 11.10.0 Manufacturer: Google. City: Mountain View. State: CA. Country: USA)) are involved selectively--for example, to perform advanced analytics, store long-term data, or synchronize devices across different locations. The system also employs local data caching to reduce unnecessary data transmission and server access. From the user's perspective, the system is accessible via mobile and web interfaces that provide real-time air quality updates, personalized alerts, and the ability to integrate with smart home ecosystems. The system supports automatic scenario execution, for example, by activating ventilation protocols when the CO2 level exceeds a set threshold, thereby enhancing comfort while minimizing energy use. To ensure system autonomy and reliability, special attention is paid to power supply optimization.