# 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
* Transmission of MQTT messages via the software package with no delays, and in response to changes in the environmental parameters, occurred on average after 3-5 s. Accuracy of air quality measurements:
* The comparison of Smart Air sensor data with a reference device showed the following deviations:
* CO\({}_{2}\): average error \(\pm\)6%;
* PM\({}_{2.5}\): average error \(\pm\)8%;
* VOC: average error \(\pm\)10%;
* Temperature and humidity: deviation \(\pm\) 1 \({}^{\circ}\)C/\(\pm\) 3%. Ways to Improve the Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology  
Optimizing energy consumption is critical for Internet of Things (IoT)-based air quality monitoring systems, especially for devices that run on battery power or have a limited power supply. Increasing the energy efficiency of such systems is possible through the implementation of complex solutions at the level of hardware and software, network architecture, and the use of alternative energy sources. One of the key approaches is hardware optimization. The use of low-power microcontrollers, energy-efficient sensors, and radio modules can significantly reduce energy consumption. The optimization of power supply schemes, in particular, the introduction of switching converters, contributes to the more efficient use of energy resources. The miniaturization of devices not only reduces weight but also reduces material costs and helps to reduce energy consumption. At the software level, an important aspect is the use of efficient algorithms that minimize computational complexity. The dynamic power management of components, depending on the operating mode of the device, helps to reduce power consumption. Optimizing data transmission by reducing the frequency of their transmission, compressing the data, and choosing the optimal transmission protocol also contributes to energy savings. It is important to implement sleep mode when a device is not performing the requisite actions. Choosing the right data transfer protocol is another important factor. LoRaWAN provides low power consumption and a long transmission range, making it ideal for mobile sensors and large networks. NB-IoT allows efficient operation based on cellular networks with wide coverage, while Zigbee offers low power consumption and is suitable for creating networks with many devices. The use of alternative energy sources makes it possible to ensure the autonomous operation of IoT systems. Solar panels are an effective solution in places with adequate sunlight, wind energy can be used to power sensors in open spaces, and thermoelectric generators are capable of converting thermal energy into electrical energy. Optimizing the sensor network is an important step to improve energy efficiency. The choice of the optimal network topology, for example, star-shaped or mesh configurations, depending on the specific operating conditions, contributes to the efficient use of energy resources. Optimizing data routing and managing refresh intervals also help minimize energy waste. The use of energy-efficient materials plays an important role in reducing energy consumption. Using lightweight materials for device enclosures reduces their weight, while thermal insulation materials help reduce heat loss. Regular monitoring and optimization are essential to maintain the high energy efficiency of systems. Analyzing energy consumption data allows the user to identify bottlenecks and find ways to optimize them. Regular software updates allow the user to take advantage of new algorithms and optimizations that can help reduce energy costs. The application of these measures contributes to the creation of energy-efficient air quality monitoring systems that last longer, are cost-effective, and have a lower impact on the environment.