# Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology
## 3 Materials and Methods  
_Methods for Estimating the Energy Consumption of IoT-Based Air Quality Monitoring Systems_  
An energy consumption assessment of IoT air quality monitoring systems is an important step in optimizing their operation and increasing efficiency. This process allows the user to identify the most energy-intensive components of their system and identify ways to reduce energy consumption. Various methods are used to assess energy consumption, which can be divided into direct, software-based, and indirect methods.  
Direct methods involve the direct measurement of current and voltage at the power supply point of devices. The use of ammeters and energy meters provides accurate data on the amount of energy consumed over a certain period of time. Such measurements are important for accurately determining the energy characteristics of a system.  
Software methods involve analyzing the energy consumption logs stored on most IoT platforms. This approach provides access to information about the average, maximum, and minimum energy consumption of devices. Software profiling enables the identification of the most energy-intensive operations and their optimization. Modelling, in turn, provides the creation of mathematical models that can predict energy consumption depending on external factors such as temperature, humidity, or device activity level.  
Indirect methods are based on using the technical characteristics of device components, such as processors, memory, or radio modules, to estimate power consumption. Comparisons with similar devices also help to create a picture of the potential power consumption of a new system.  
The accuracy of energy consumption estimation is affected by various factors. Measurement conditions such as temperature, humidity, and network load can significantly affect the results. The choice of measurement methodology and equipment settings is also important. Errors in measuring devices can significantly affect the accuracy of the obtained data.  
Energy consumption assessment is of great importance for optimizing the operation of IoT systems. Identifying the most energy-intensive components enables the development of measures for their optimization and the prediction of battery life. Based on energy consumption data, the user can select the optimal components for the development of energy-efficient devices and create algorithms that minimize energy consumption. In general, effective energy consumption assessment is a key step in creating durable and effective IoT solutions in the field of air quality monitoring.  
For this study, component selection was guided by real-world implementation considerations, including cost, availability, and energy efficiency. For instance, the Smart Air sensor was chosen for its low cost (\(\sim\)USD 16) and suitability for integration with smart home ecosystems, while the Zigbee gateway was selected for its low power consumption (1.5 W) and mesh topology, which is suitable for scalable deployments. The Mini UPS (Develco Products, Aarhus, Denmark) (20 W-h capacity) was included to ensure autonomy during outages, reflecting practical needs in unstable power environments.