Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology

Abstract

Air quality monitoring systems based on Internet of Things (IoT) technology are critical for addressing environmental and public health challenges, but their energy efficiency poses a significant challenge to their autonomous and scalable deployment. This study investigates strategies to enhance the energy efficiency of IoT-based air quality monitoring systems. A comprehensive analysis of sensor types, data transmission protocols, and system architectures was conducted, focusing on their energy consumption. An energy-efficient system was designed using the Smart Air sensor, Zigbee gateway, and Mini UPS, with its performance evaluated through daily energy consumption, backup operation time, and annual energy use. An integrated efficiency index (IEI) was introduced to compare sensor models based on functionality, energy efficiency, and cost. The proposed system achieves a daily energy consumption of 72 W·h, supports up to 10 h of autonomous operation during outages, and consumes 26.28 kW·h annually. The IEI analysis identified the Ajax LifeQuality as the most energy-efficient sensor, while Smart Air offers a cost-effective alternative with broader functionality. The proposed architecture and IEI provide a scalable and sustainable framework for IoT air quality monitoring, with potential applications in smart cities and residential settings. Future research should explore renewable energy integration and predictive energy management.

1. Introduction

Efficient use of energy resources is one of the most important challenges of modern society. In the context of Internet of Things (IoT) technologies, which are now actively implemented for air quality monitoring, the issue of energy efficiency is gaining particular relevance. The implementation of energy-efficient IoT systems allows for the long-term autonomous operation of devices, thereby reducing electricity costs and minimizing environmental impact. Reducing electricity consumption, which is generated mostly from fossil fuels, helps to reduce greenhouse gas emissions and slow down climate change, and optimizes the use of natural resources [1,2]. Energy-efficient solutions allow for significant reductions in operating costs, which is important for large-scale monitoring systems. In addition, reducing energy consumption extends the service life of electronic components, reducing the cost of their replacement. From a technical point of view, energy-efficient solutions expand the functionality of monitoring systems by increasing the number of sensors without also increasing the overall energy consumption. Reducing the heating level of components increases their reliability and contributes to long-term autonomous operation, which is especially important for mobile and remote systems [3]. In addition, energy-efficient IoT systems have a significant social impact. They provide up-to-date and accurate data on air quality, which allow policymakers to make informed decisions about the health and safety of the population. These systems are also an important element of the infrastructure of smart cities, contributing to an increase in the quality of life and sustainable development. Therefore, the research and implementation of energy-efficient IoT solutions for air quality monitoring is an important step towards ensuring environmental safety and economic efficiency and improving the quality of life.

This study aims to propose an energy-efficient IoT architecture and introduce an IEI for sensor evaluation.

1.1. The Role of Internet of Things Technology in Air Quality Monitoring Systems

The Internet of Things is actively integrated into various industries, and air quality monitoring is no exception. With the ability to connect physical objects and collect data in real time, IoT opens up new possibilities for air quality monitoring. Unlike traditional systems, which are limited to readings from fixed monitoring stations, the IoT allows sensors to be placed at many points within a city or region, creating a detailed picture of air pollution.

One of the key advantages of IoT systems is the collection of data in real time. Sensors can capture various pollutants such as pollen, nitrogen dioxide, or fine particles at high frequency, which allows the system to quickly detect short-term peaks in pollution. This approach also contributes to the creation of more accurate air quality forecasting models, which allows for early warnings of potential environmental threats to the population. In addition, IoT systems are able to provide personalized information about air quality, taking into account the user’s location and their individual needs. Data collected by IoT networks can be made available to a wide range of users through mobile applications and web portals, which contributes to informed decision-making about health and activity.

The use of IoT in air quality monitoring covers a variety of scenarios. It can involve controlling pollution levels in urban areas, monitoring industrial emissions, or assessing air quality in offices, schools, and residential buildings. However, the implementation of such systems is accompanied by challenges. One of the main problems is the need to standardize sensors to ensure interoperability between the different systems. In addition, the issues of protecting data from unauthorized access and developing energy-efficient solutions for data transmission remain important.

Despite these challenges, the potential for IoT to monitor air quality is significant. Further research and innovation will enable the creation of even more accurate, reliable, and affordable systems that will help improve people’s quality of life and contribute to environmental conservation.

The proposed integrated efficiency index (IEI) introduces a novel quantitative framework to evaluate sensors by balancing functionality, energy efficiency, and cost, addressing the lack of standardized metrics for IoT system design and supporting scalable, sustainable deployments in smart cities.

1.2. Energy Efficiency in the Context of IoT Systems

IoT systems consist of a large number of devices that are often located in remote locations and operate autonomously. As a result, energy efficiency is a key factor determining their performance and durability. Reducing power consumption can significantly extend battery life, which is especially important for devices that do not have constant access to the power grid. This not only reduces the cost of battery replacement but also helps reduce the overall operating costs by saving electricity. In addition, energy-efficient IoT systems contribute to reducing greenhouse gas emissions, thereby having a positive impact on the environment. Reducing power consumption also reduces component heating, increasing their reliability, and enables the creation of more scalable and complex IoT networks.

The energy efficiency of IoT systems is influenced by a number of factors (Figure 1). Hardware plays an important role: the choice of processors, memory, and radio modules, as well as the optimization of component operating modes, can significantly reduce energy consumption. The use of energy-efficient sensors is also an important aspect. System software must ensure efficient data processing, the optimization of operating systems, and intelligent power management. The choice of appropriate communication protocols, such as LoRaWAN (Cycleo, Grenoble, France) or Bluetooth LE (Bluetooth Special Interest Group, Kirkland, WA, USA), contributes to the efficient use of energy, while optimizing the data transmission frequency further helps to reduce energy costs. The network infrastructure also plays an important role: the correct network topology and the use of repeaters ensure an increase in transmission range and a reduction in the load on individual devices.

A number of strategies are used to improve the energy efficiency of IoT systems. Software optimization involves reducing the frequency of sensor polling, using sleep modes, and simplifying the data processing algorithms. The choice of energy-efficient components, such as low-power microcontrollers and sensors with minimal power consumption, also contributes to reducing energy costs. The optimization of communication protocols involves the use of adaptive data transfer rates and reducing packet sizes. It is worth noting the possibility of using alternative energy sources, such as solar panels and thermal generators, thereby providing a significant increase in the autonomy of IoT systems.

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. Alsamrai 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 PM₁₀) offer high accuracy but consume significant power due to their laser-based operation. Taştan [15] highlighted that piezoelectric sensors have lower power consumption, making them suitable for low-cost smart home applications, although they compromise on precision. Wall et al. [16] explored electrochemical gas sensors for detecting NO₂, CO₂, and SO₂, which offer high sensitivity but are prone to environmental interference. Kukre et al. [17] and Darwin et al. [18] integrated machine learning with IoT sensors to enhance data accuracy while emphasizing low-power designs. Similarly, Truong et al. [19] and Malakhova 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. Collado 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. Othman et al. [22] explored NB-IoT (3rd Generation Partnership Project(3GPP)), which provides robust coverage and low power consumption (e.g., 50 mW during transmission) for cellular-based deployment. Hura and Monastyrskii [23] and Palamar et al. [24] highlighted Zigbee’s (Develco Products, Aarhus, Denmark) suitability for mesh networks, with power consumption as low as 30 mW, although its range is limited to 100 m. Conversely, Vasanth et al. (2021) [25] and Saxena 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. Behjati et al. [27] and Pandey and Arya (2022) [28] explored LoRa and D2D communications for rural and 5G-IoT applications, respectively, suggesting hybrid protocols to balance energy and bandwidth. Rakib 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. Malleswari and Mohana [30] reviewed fixed monitoring stations, which provide high accuracy but are energy-intensive and limited in coverage. Messan et al. [31] proposed mobile stations for flexible deployment, although battery life remains a challenge; these yield a typical autonomy of 8–12 h. Singh [32] and Kaur and Sharma [33] explored the integration of smart sensors into urban infrastructure; these reduce maintenance costs but require stable power sources. Shashank et al. [34] and Malakhova et al. [20] advocated for modular architectures with edge computing to minimize cloud-based data processing, thereby reducing energy consumption by 25%. Cano-Suñén et al. [35] demonstrated a scalable IoT ecosystem with over 200 sensors in its buildings, achieving a 15% energy saving through localized data aggregation. Faniyi and Luo [36] and Pexyean et al. [37] applied modular designs to greenhouse and campus monitoring, respectively, suggesting the designs’ transferability to air quality systems. Mohammed et al. [38] and Anozie et al. [39] utilized federated learning and real-time optimization to enhance system efficiency, while Khorasgani et al. [40] and Cojocaru and Isopescu [41] integrated IoT with building management systems to improve indoor air quality. Mahmud et al. [42] and Kalenyuk et al. [43] emphasized scalable architectures for agricultural and smart city applications, respectively, highlighting the need for low-power, cost-effective designs. Saeed 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. Collado et al. [4] implemented solar panels for air quality sensors in tropical environments, achieving up to 90% autonomy in sunny conditions. Nguyen et al. [45] developed an RF energy-harvesting rectenna, providing 7.6 µW for low-power IoT devices. Palamar et al. [24] explored RF energy harvesting systems for remote sensors, supplementing battery power by 10–15%. Truong et al. [19] and Faniyi and Luo [36] investigated solar and thermal energy harvesting, with solar panels yielding 50–100 mW under optimal conditions. Massaoudi 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 Singh and by Othman et al. [22,32]. Third, the practical implementation of alternative energy sources in large-scale deployments remains limited, as discussed by Collado et al. [4] and Palamar et al. [24]. Finally, there is a lack of quantitative metrics, such as efficiency indices, to holistically compare sensor performance, as implied by Taştan [15] and Kukre 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. The main advantage of such systems is their high mobility; however, limited autonomy and the influence of external factors on the accuracy of measurements remain important challenges.

Fixed monitoring stations are installed at specific points in a city or region for the long-term monitoring of air quality. They provide high accuracy of measurements and can be integrated with other monitoring systems. At the same time, the limited number of measurement points and the high cost of installation and maintenance are the disadvantages of such systems.

Another type is “smart” sensors that are integrated into urban infrastructure, such as in streetlights, bus stops, or buildings. They provide a dense monitoring network with relatively low maintenance costs. However, the limited set of measured parameters and the dependence on urban infrastructure can be challenging for their widespread use.

Components of IoT-based air quality monitoring systems include sensors that measure the level of air pollution by various harmful substances (PM_2.5, PM₁₀, NO₂, SO₂, etc.), data transmission networks (Wi-Fi, LoRaWAN, and NB-IoT), and cloud platforms for data storage and processing, as well as user interfaces that provide access to information in a convenient format through web portals or mobile applications.

The main functionalities of such systems are real-time air quality monitoring, automatic notifications when levels exceed permissible pollution standards, and data visualization in the form of maps and graphs, as well as integration with other city management systems, such as transport or energy. Despite their numerous advantages, these systems also face challenges, including the need to calibrate sensors to ensure measurement accuracy, develop energy-efficient solutions, and protect data from unauthorized access. Overcoming these challenges will contribute to the further development of effective air quality monitoring systems.

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 (~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.

4. Results

4.1. Comparison of Different Types of Sensors and Their Power Consumption in Air Quality Monitoring Systems

Choosing the right sensor for an air quality monitoring system is an important factor for determining the accuracy of measurement, battery life, and total cost of a system. The power consumption of sensors is one of the key parameters that must be taken into account when designing such systems, as it affects their autonomy and efficiency (

Table 1. Comparison of the power consumption of different types of sensors.

Sensor Type	Principle of Operation	Power Consumption	Advantages	Disadvantages
Optical Dust Sensor	Laser scattering	High	High accuracy	High power consumption
Piezoelectric dust sensor	Changing the filter mass	Low	Low power consumption	Low accuracy
Electrochemical Gas Sensor	Chemical reaction	Average	High sensitivity	Sensitivity to interference
Semiconductor Gas Sensor	Change in conductivity	Low	Low cost	Low selectivity
Temperature and humidity sensor	Resistance change	Very low	Low cost, high reliability
TSI (Optical Dust, Industrial)	Laser scattering	High (2–5 W)	High accuracy, industrial reliability	High power consumption, high cost
Aeroqual (Electrochemical/ Optical)	Chemical reaction/laser scattering	Average (0.5–2 W)	High accuracy, modularity	High cost, requires calibration

Note: Data for TSI and Aeroqual are based on the typical specifications of industrial sensors (e.g., the TSI DustTrak for PM_2.5/PM₁₀ or Aeroqual Series 500 for gases and dust). Power consumption is estimated for those portable models suitable for IoT systems.

).

Different types of sensors are used to monitor air quality. Dust sensors (PM_2.5 and PM₁₀) measure the concentration of fine particles in the air. Among them, optical sensors are highly accurate, due to the use of laser light to count the particles, but they are characterized by relatively high power consumption. Piezoelectric sensors have lower power consumption but provide less accuracy because they measure the changes in the mass of the filter on which the particles settle.

Gas sensors are used to determine the concentration of gases such as CO₂, NO₂, and SO₂. Electrochemical sensors exhibit high sensitivity due to chemical reactions, but they can also be sensitive to interference. Semiconductor sensors change their conductivity when exposed to gases, making them an affordable, low-cost option, although their selectivity is lower compared to other types.

Temperature and humidity sensors also play an important role in air quality monitoring systems. Resistive sensors, which change their resistance depending on temperature or humidity, are an energy-efficient and low-cost option.

The power consumption of sensors is influenced by several factors. The principle behind their operation determines their basic level of power consumption: for example, optical sensors require more energy due to their use of laser radiation. The frequency of measurement is also a critical parameter—the more often measurements are taken, the more energy is consumed. The presence of additional functions, such as a backlight or display, increases power consumption. Operating modes, such as standby or sleep mode, can significantly reduce energy consumption, which is essential for ensuring the long-term autonomy of IoT systems.

Careful selection of sensors and the optimization of their operating modes allow researchers to create efficient and energy-saving air quality monitoring systems.

Developing strategies to reduce the energy consumption of sensors is an important aspect of improving the efficiency of IoT air quality monitoring systems. One of the key strategies used is choosing the optimal type of sensor. It is necessary to give preference to those sensors with the lowest power consumption that, at the same time, provide the required level of measurement accuracy needed for a specific task.

Optimizing the frequency of measurements is also an effective way to reduce energy consumption. In cases where monitoring conditions allow users to reduce the frequency of data collection, this significantly reduces energy consumption. An important aspect is the use of low-energy data transfer protocols, such as LoRaWAN or Bluetooth LE, which ensure efficient information transmission with minimal energy consumption.

The use of energy-efficient microcontrollers also contributes to a significant reduction in energy consumption. Modern microcontrollers with low power consumption allow you to maintain a high level of functionality in IoT devices with lower energy consumption.

The application of sleep modes is another important strategy. Putting the sensor to sleep during periods of inactivity can significantly reduce its energy consumption and extend the battery life of the device. The combination of these strategies ensures the creation of energy-efficient solutions for air quality monitoring systems that are more sustainable and durable.

There are several popular models of sensors for monitoring indoor air quality available on the Ukrainian market. An overview of some of them, with a focus on their energy efficiency, is presented in

Table 2. Comparative table of the most popular air quality sensor models in Ukraine.

Model	Measured Parameters	Data Transfer Method	Power Supply	Features	Price (UAH/USD *)
104.ua Air	CO2, temperature, humidity	Wi-Fi	Constant power (USB)	Stylish design with LED display; audible alerts for exceeding CO2 standards; the ability to integrate into the Smart Home system.	1450/~35
SaveEcoSensor	PM2.5, PM10, temperature, humidity	Wi-Fi	Constant power (USB)	The data are available through the SaveEcoBot platform; the ability to view the measurement history; integration with other environmental services.	3300/~80
Ajax LifeQuality	CO2, temperature, humidity	Jeweller (proprietary protocol)	Battery (3x AAA, up to 3 years)	Integration with Ajax security systems; long battery life; mobile notifications about changes in air parameters.	8299/~200
AirHome	CO2, PM2.5, temperature, humidity	Wi-Fi	Constant power (USB)	Possibility of remote monitoring through a mobile application and setting thresholds for notifications.	2350/~57
Smart Air	CO2, PM2.5, PM10, TVOC, temperature, humidity	Wi-Fi, Zigbee, BLE	Permanent power supply (USB) or battery	Integration with smart home systems; real-time data display; automation of climate systems.	650/~16

* Prices converted at an approximate exchange rate of USD 1 ≈ UAH 41.5 (2025).

.

1. 104.ua Air (104.ua, Kyiv, Ukraine) is a smart device equipped with a color screen that informs the system about air quality using color indicators and sound signals.

2. AirHome (Manufacturer/company: “Free Arduino”, City: Ivano-Frankivsk, Country: Ukraine) is a compact device that measures PM_2.5 and PM₁₀ particle levels, as well as temperature and humidity.

3. The SaveEcoSensor 3.0 (Manufacturer/company: SaveDnipro. City: Dnipro. Country: Ukraine) is an air quality monitoring station that measures PM_2.5 and PM₁₀ dust content and is equipped with temperature, humidity, and pressure sensors, as well as a heating module to minimize the impact of weather conditions. This device, when integrated with the SaveEcoBot platform, allows the user to monitor air quality in real time and receive data through a mobile application or website.

4. The Ajax LifeQuality (Manufacturer/Company: Ajax Systems City: Kyiv. Country: Ukraine) features high-precision temperature, humidity, and CO₂ sensors, along with the automation of scenarios for climate control. The LifeQuality sensor works using its own Ajax Jeweller protocol, which provides stable communication with the hub at a distance of up to 1700 m and has minimal power consumption. This allows for reduced electricity consumption compared with Wi-Fi or other wireless standards.

5. Smart Air (Manufacturer/company: GAOTek, New York, NY, USA and Toronto, ON, Canada) is an air quality monitoring device that provides detailed data on indoor air conditions and can integrate with other smart home systems. These devices help monitor indoor air quality, providing a comfortable and safe living and working environment.

4.2. Analysis of Data Transmission Protocols from the Point of View of Energy Efficiency in Air Quality Monitoring Systems

Choosing the right data transfer protocol is a critical factor for ensuring the energy efficiency of IoT air quality monitoring systems. Each protocol has its own characteristics that affect power consumption, transmission range, bandwidth, and other variables (

Table 3. Comparison table of the available wireless technologies for the IoT.

Technology	Range of Action	Transmission Speed	Energy Consumption	Network Type	Capacity
Wi-Fi	30–50 m (indoors)	Up to 100–200 Mbit/s	High	Point-to-point	High
Bluetooth	~10 m	Up to 1 Mbit/s	Average	One-to-one	Low
Bluetooth LE	10–50 m	Up to 2 Mbit/s	Very low	Star or mesh (BLE 5.0)	Average
Zigbee	10–100 m (mesh)	Up to 250 Kbit/s	Low	Mesh	Low
LoRaWAN	Up to 15 km (open space)	Up to 50 Kbit/s	Very low	Star	Very low
NB-IoT	Up to 10 km	~50–100 Kbit/s	Average	Star (via operator)	Low
Thread	10–100 m (mesh)	Up to 250 Kbit/s	Low	Mesh	Average
LPWAN (General Category)	Up to 15 km	Up to 100 Kbit/s	Very low	Star	Very low

).

Among the main data transfer protocols used in such systems, Wi-Fi provides high transfer speeds but is characterized by significant power consumption and a limited range. It is most suitable for stationary devices with a constant power supply.

Bluetooth is more energy-efficient but has a short transmission range. This protocol is often used to transfer data between devices in close proximity. Zigbee, in turn, provides low power consumption and the ability to create networks with many sensors, making it effective for applications involving low-power monitoring systems.

LoRaWAN is characterized by low power consumption and a long transmission range, making it an ideal choice for mobile sensors and large-scale networks. Another important protocol is NB-IoT, developed on the basis of cellular networks; this provides low power consumption and a significant range, so it is suitable for the mass deployment of IoT devices [27].

When choosing a data transfer protocol from the point of view of energy efficiency, several criteria should be taken into account. Power consumption in transmit and receive modes is one of the most important parameters, as lower power consumption allows the devices to run on battery power for longer. Transmission range is particularly important for remote sensors that require stable communication over long distances.

Bandwidth determines the rate of data transmission and can affect the frequency of measurements, which is important for systems that require operational monitoring. Networking capabilities, such as support for data routing and the creation of sensor networks, are also of great importance. Finally, the cost of the modules and the licenses needed to use the protocol is an additional criterion that must be considered when designing energy-efficient IoT solutions.

Strategies to reduce energy consumption during data transmission are important to ensure the effectiveness of IoT air quality monitoring systems. One of the key strategies is to optimize data frequency. In cases where the requirements of measurement accuracy allow for reducing the transmission frequency, this strategy contributes to a significant reduction in energy consumption and an increase in the autonomy of devices.

Using device sleep patterns is another effective way of reducing energy consumption. Putting a device to sleep between data sessions can significantly reduce its power consumption during periods of inactivity.

Data compression also helps to reduce power consumption; reducing the size of the data packets reduces transmission time and also reduces the load on the transmitter. This is especially important for systems with a large number of sensors.

The use of adaptive transmission algorithms allows the user to dynamically change the transmitter power depending on the conditions of radio signal propagation. This approach ensures the efficient use of energy and the maintenance of stable communication, even under difficult conditions. The combination of these strategies contributes to the creation of energy-efficient and sustainable IoT systems for air quality monitoring.

4.3. Analysis of Factors Limiting the Energy Efficiency of IoT-Based Air Quality Monitoring Systems

Despite their importance, IoT-based air quality monitoring systems often face energy efficiency limitations. These limitations are due to a number of factors (hardware components, software, network infrastructure, operating conditions, and system optimization) that are worth considering in detail.

Hardware Components

Sensors are an important element of monitoring systems, but they can consume a significant amount of energy. The more often measurements are taken, the more energy is consumed. The type of sensor also affects consumption: electrochemical sensors usually require more energy than optical sensors. Additional features, such as backlights or displays, increase power consumption.

Microcontrollers are key to data processing. A higher clock speed in the processor increases its performance, but at the same time, it increases its power consumption. The choice of microcontroller architecture can also significantly affect energy efficiency.

Wireless modules have a significant impact on the power consumption of the system. The choice of data transfer protocol (Wi-Fi, Bluetooth, and LoRaWAN) affects the efficiency of the system; the higher power of the transmitter increases the transmission range but also leads to an increase in power consumption.

Accessories such as high-resolution displays and speakers can also increase power consumption, especially when using beeps or backlights.

Software

Software optimization is of great importance to ensure the energy efficiency of IoT systems. Lightweight operating systems that are specifically optimized for embedded systems allow the reduction of power consumption. Choosing efficient data transfer protocols also helps to reduce energy costs.

Data processing algorithms need to be optimized because complex computing processes require more resources. The frequency of software updates can also affect power consumption, as frequent updates require additional resources.

Network infrastructure

The power consumption of gateways depends on their functionality and the number of connected devices. The servers to which the data are transmitted also affect the overall energy efficiency of the system. Poor signal quality can cause the transmitter to increase power usage, resulting in additional energy costs.

Operating conditions

Temperature conditions can affect the performance of components. High or low temperatures may cause additional power consumption. Humidity is also an important factor, as high humidity can cause contact corrosion and increase resistance, which negatively affects energy consumption. Radio interference from other wireless devices can cause transmitters to operate at a higher power level.

System optimization

Insufficient system optimization and a lack of energy-saving modes can lead to significant energy costs. Regular system maintenance, including sensor cleaning and component diagnostics, can help prevent an increase in energy consumption and ensure the stable operation of IoT air quality monitoring systems.

5. Discussion

5.1. 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—ranging from 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 (PM_2.5, 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:

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Before implementation, signal coverage should be analyzed (using tools such as Zigbee2MQTT Map or the Wi-Fi analyzer);

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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. Critical components are supported by Mini UPS devices, which allow operation during power outages. Additionally, the system can be powered by solar panels, especially in remote outdoor settings. Energy-saving algorithms are used to manage sensor and gateway behaviors dynamically, placing the devices into low-power or sleep modes when appropriate.

The proposed system’s low annual energy consumption (26.28 kW·h) contributes to reaching large-scale energy efficiency objectives by reducing the operational footprint compared to traditional systems (e.g., 100–150 W·h/day for LoRaWAN-based systems). Assuming the use of fossil fuel-based electricity (0.5 kg CO₂/kW·h), this could yield an estimated CO₂ reduction of ~13 kg/year per deployment, supporting smart city sustainability goals. Future research will quantify additional metrics, such as impacts on ozone-depleting substances, through life-cycle assessments.

In summary, the proposed architecture enables the development of scalable and sustainable IoT systems for air quality monitoring. Through a combination of energy-aware hardware choices, communication protocols, and data handling strategies, the suggested architecture ensures low power consumption while maintaining high accuracy and responsiveness.

The successful implementation of a distributed IoT air quality monitoring network requires not only the selection of the right sensors but also the following characteristics (

Table 4. The risks, challenges, and solutions found in distributed air quality monitoring networks.

Risk Category	Risk/Challenge Description	Decision/Recommendation
Loss of connection with the sensor	Connection interruptions due to a weak signal, interference, or power failure	Placement of Zigbee/BLE repeaters Power supply via Mini UPS
Signal instability	Influence of Wi-Fi or radio interference, RSSI reduction < −80 dBm	Selecting free Zigbee channels (25–26) Physical distance between the gateway and Wi-Fi
Loss or delay of data	Packets do not reach the controller, or are lost due to collisions or timeouts	Local data caching on ESP or a microcontroller Use of the MQTT QoS protocol
Inaccurate data due to environmental conditions	Changes in humidity/temperature affect accuracy (TVOC, PM2.5)	Installation of T/RH sensors nearby for compensation Application of error-correction algorithms
Drift or degradation of sensors	Decreased sensor accuracy over time, impact of contamination	Regular calibration (1–2 times a year) Selection of self-calibrating sensors
Complete loss of sensor/node	Failure of the sensor or microcontroller (voltage surges, overheating)	Reservation of nodes (duplication of critical areas) Monitoring the status of each device in the software package
Incorrect operation of automation	Due to a delay in or omission of CO2 readings, the ventilation system does not turn on	Setting timers/backup triggers. Checking the last sensor update before action
Misinterpretations	Inaccurate visualization on the dashboard, due to gaps in data	Filtering anomalies in the software package Displaying the ‘unavailable’ mark if the data are old

):

-

Redundancy at all levels (power, communication, and storage);

-

An adaptive network structure (mesh or clustering);

-

Proactive diagnostics and calibration; and

-

Flexible data processing that takes errors into account.

The basis of our proposed energy-efficient indoor air quality monitoring system is the Smart Air sensor (Figure 3), which is an intelligent device used for the remote monitoring of the air environment. It provides accurate data on air pollution levels, temperature, and humidity, as well as the concentration of fine particles of PM_2.5, PM₁₀, NO₂, and CO₂. An example using a typical Smart Air sensor that is equipped with a low-energy microcontroller, PM_2.5, PM₁₀, NO₂, CO₂ sensors, a temperature and humidity sensor, and a wireless data transmission module can be found in Ref. [56].

A Zigbee gateway enables communication between different devices such as temperature sensors, lamps, and switches. This gateway collects information from the sensors, processes it, and transmits it to a server or cloud over a Wi-Fi network. Its role is critical for ensuring uninterrupted communication between the system components [57].

The Mini UPS acts as a backup power supply. It ensures the smooth operation of sensors and the gateway in the event of a power outage, which is essential to ensure stable air quality data collection, even in conditions of unstable power supply.

The collected data are then transmitted to a server or to the cloud, where the information is stored, analyzed, and visualized. The server also performs the function of managing the entire system. Its user interface allows the user to view air quality data, configure the system, and receive notifications when pollution standards are exceeded.

The use of Smart Air sensors offers a number of advantages. Thanks to their energy efficiency, long-term battery life without recharging is ensured, which is critical for mobile devices. Reduced power consumption reduces battery replacement costs and also allows the user to increase the number of sensors in the network, creating larger monitoring systems.

The energy efficiency of Smart Air sensors is affected by a number of factors. Different types of sensors have different power consumption demands: for example, gas sensors usually consume more energy compared to others. The frequency of measurements is also an important parameter: the more often measurements are taken, the more energy is consumed. The backlight of the display, if present, can significantly increase power consumption. Choosing a wireless protocol such as Wi-Fi, Bluetooth, or LoRaWAN also affects data transfer efficiency. Operating modes, such as sleep or standby, can significantly reduce energy consumption.

The energy efficiency of Smart Air sensors is ensured by a number of technical solutions. Software optimization allows the user to minimize power consumption by efficiently managing measurement and data transmission processes. The use of energy-efficient components, such as low-energy microcontrollers and wireless modules, further reduces energy costs. Intelligent power management allows the user to dynamically adjust the voltage and frequency of the processor, depending on the load. Putting the sensors to sleep during periods of inactivity also contributes to a significant reduction in energy consumption. The optimization of data transmission through the use of energy-efficient protocols and the minimization of the amount of data transmitted ensures the efficient use of energy resources.

A typical low-power Zigbee gateway is equipped with an energy-efficient processor, a radio module with support for energy-saving modes, and a small display screen for displaying information. Due to these characteristics, the Zigbee gateway provides efficient data transmission and communication support between the components of an IoT system.

Mini UPS (uninterruptible power supply) setups or small uninterruptible power supplies are becoming increasingly popular due to their portability and their ability to keep devices running smoothly in the event of a power outage. The choice of such a device largely depends on its energy efficiency, which has an important impact on the functioning of the system (

Table 5. Comparison of the different types of Mini UPS in terms of their energy efficiency.

UPS Type	Advantages in Terms of Energy Efficiency	Disadvantages
Linear-interactive	High efficiency in normal operation	May have low efficiency, with frequent battery switching
Online	Stable output voltage, but high power consumption online	High power consumption
Offline	The most energy-efficient system, but it does not provide voltage stabilization	May cause damage to devices during sudden voltage drops

).

Among the main advantages of a Mini UPS is battery life. The less power the device consumes, the longer it is able to provide power to connected components during a power outage. Energy efficiency also contributes to increased battery life, as lower levels of stress reduce the battery degradation process. Reducing energy consumption has a positive effect on the cost of operation, thereby reducing energy costs. In addition, energy-efficient devices contribute to the preservation of the environment by reducing carbon emissions.

The energy efficiency of the Mini UPS is affected by a number of factors, one of which is the type of battery. Lithium-ion batteries, which are often used in Mini UPS setups, have high energy density and efficiency. Modern charging circuits are able to optimize the charging process, thereby minimizing energy loss. The efficiency of the inverter also plays an important role, as it converts direct current from the battery to alternating current to power the devices. The higher the efficiency of the inverter, the lower the energy loss.

The mode of operation of the Mini UPS is also an important factor: different modes (online, offline, or line-interactive) have different efficiency levels. For example, the online mode provides the best power stability but usually has higher power consumption. Additional features, such as a display or USB ports, can increase power consumption, so they must be considered when choosing a device.

The energy efficiency of the Mini UPS is critical to ensure the stable operation of IoT air quality monitoring systems and minimize energy consumption.

During the launching and testing of an air quality monitoring system developed based on Internet of Things technology, data were collected on the efficiency of the components, the accuracy of sensor measurements, the stability of communication, and the performance of the automated scenarios. An analysis of the system’s communication stability and data transmission shows the following values:

-

The frequency of data updates from the Smart Air Zigbee (Manufacturer/company: GAOTek, New York, USA and Toronto, Canada) sensor via the gateway was 30–60 s.

-

Packet loss in the Zigbee network was < 1% at a distance of up to 10 m from the gateway, indicating stable communication.

-

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:

• CO₂: average error ±6%;
• PM_2.5: average error ±8%;
• VOC: average error ±10%;
• Temperature and humidity: deviation ± 1 °C/± 3%.

5.2. 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.

5.3. Energy Efficiency Estimation of the Proposed IoT Air Quality Monitoring System

To assess the energy performance of the proposed air quality monitoring system, a configuration based on the Smart Air sensor, a Zigbee gateway, and a Mini UPS was analyzed. The estimation focused on daily energy consumption, uninterruptible power supply (UPS) efficiency, and projected annual energy use under continuous operation.

The assumed parameters for the components were as follows:

• Smart Air sensor: 0.5 W average power consumption;
• Zigbee gateway: 1.5 W;
• Mini UPS (accounting for charging losses and standby operation): 1.0 W;
• Operational time: 24 h/day; and
• Battery capacity of Mini UPS: 20 W·h.

Daily Energy Consumption

The total daily energy consumption of the system was calculated as follows:

• Smart Air: 0.5 W × 24 h = 12 W·h
• Gateway: 1.5 W × 24 h = 36 W·h
• Mini UPS: 1.0 W × 24 h = 24 W·h

Total daily energy consumption: 72 W·h

Backup Operation Time

In the event of a power outage, the Mini UPS is responsible for maintaining the operation of critical components. The combined consumption of the Smart Air sensor and the Zigbee gateway is 2 W. With a battery capacity of 20 W·h, the autonomous operation time provided by the UPS is:

$${\displaystyle \frac{20 \mathrm{W}·\mathrm{h}}{2 \mathrm{W}}}=10 \mathrm{h}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{s}.$$

This level of autonomy is sufficient for most typical short-term outages in urban environments.

UPS Efficiency

The energy efficiency of the Mini UPS can be expressed as the ratio of useful output energy to the total energy consumed for charging and operation:

$$\mathrm{E}\mathrm{f}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}=\left({\displaystyle \frac{20 \mathrm{W}·\mathrm{h}}{24 \mathrm{W}·\mathrm{h}}}\right)\times 100\mathrm{\%}=83.3\mathrm{\%}.$$

Annual Energy Consumption

Assuming continuous operation throughout the year, the projected annual consumption of the system is:

72 W·h/day × 365 days = 26.28 kW·h/year.

This level of consumption is considered low for IoT-based environmental monitoring systems, especially those capable of real-time multi-parameter measurements.

To contextualize the proposed system’s energy efficiency, we compared its performance with existing IoT-based air quality monitoring systems. For instance, Ref. [4] reported a daily energy consumption of 100–150 W·h for a LoRaWAN-based system, while the authors of [21] achieved approximately 90 W·h/day with optimized data transmission protocols. Our system, consuming 72 W·h/day (26.28 kW·h/year), demonstrates improved energy efficiency, which is particularly suitable for battery-powered or resource-constrained deployment. This comparison highlights the proposed architecture’s potential for sustainable, scalable air quality monitoring in smart city applications.

The evaluation confirms that the proposed system architecture ensures both energy efficiency and operational reliability. With a low daily energy demand, extended backup duration, and UPS efficiency above 80%, this configuration is suitable for long-term deployment in residential, institutional, or smart city environments with limited access to a continuous power supply.

5.4. Comparative Assessment of Sensor Energy Efficiency and Cost-Effectiveness Using the Integrated Efficiency Index (IEI)

A Smart Air sensor is a device for monitoring indoor air quality that measures the main parameters of the atmosphere and transmits them to a smart home system or mobile application.

LifeQuality sensors are equipped with sensors offering medical measurement accuracy. Non-dispersive infrared sensors provide accurate CO₂ information that is not affected by air pollutants such as aerosols and steam. Industrial-grade digital sensors measure the relative humidity and temperature. Ajax automation devices and scenarios support all these indicators within a given range (

Table 6. Comparison of the energy efficiency and price of Smart Air and Ajax LifeQuality sensors.

Characteristic	Smart Air	Ajax Lifequality
Price	650 UAH (~16 USD)	8299 UAH (~200 USD)
Power	Permanent USB connection (5V, 1A)	3x AAA batteries (up to 3 years of battery life)
Daily Intake	72 W·h	~1 W·h
Annual Consumption	26.28 kW	~0.365 kW
Communication Protocol	Wi-Fi, Zigbee, BLE	Jeweller (energy efficient)
Measured Parameters	CO2, PM2.5, PM10, TVOC, temperature, humidity	CO2, temperature, humidity
Smart Home Integration	Home Assistant, Google Home, Alexa	Ajax Security System, mobile app

).

Thus, the Ajax LifeQuality sensor is much more energy efficient as it runs on batteries for up to 3 years, consuming 70 times less energy than the Smart Air, while the Smart Air sensor is more functional—it measures more air parameters (PM_2.5, PM₁₀, TVOC) and offers more integration options, but has higher power consumption. However, the Smart Air sensor is much cheaper compared to the Ajax LifeQuality, which significantly compensates for its annual energy consumption (about 100 UAH/year ≈ USD 2.4/year).

To ensure the comprehensive comparison of sensor models, we introduced an integrated efficiency index (IEI), which accounts for three key factors: the number of parameters measured (functionality, F), energy efficiency (E, which is inversely proportional to annual power consumption), and cost (C) in USD. The IEI balances functionality and energy efficiency (desirable attributes to be maximized) against cost (a limiting factor to be minimized), aligning with the need for scalable, sustainable IoT systems in smart cities [31,34].

The IEI is calculated as follows:

$$IEI={\displaystyle \frac{F\times E}{C}}$$

where:

• F—number of measured parameters (e.g., CO₂, PM_2.5, temperature);
• E—energy efficiency, defined as 1/annual consumption (kW · h); and
• C—sensor price in USD.

In the IEI formula, the parameters F (functionality), E (energy efficiency), and C (price) are given equal weight, as the study seeks to balance three key aspects of sensor selection: their ability to measure various air parameters, their energy efficiency, to ensure autonomy, and their economic feasibility for scaling systems. Their equal weight reflects the assumption that none of these factors is prioritized in the overall context, and the optimal sensor should demonstrate a compromise between all three factors.

The equal weighting of functionality (F), energy efficiency (E), and cost (C) in the IEI was chosen to balance the trade-offs that are critical for scalable IoT systems in smart city applications, ensuring that no single factor dominates and providing a practical decision-making tool for diverse stakeholders. While sensor accuracy and data reliability are critical, they were not included in the current IEI to maintain simplicity and to focus on scalability. Future iterations of the IEI could incorporate accuracy as an additional weighted factor to enhance its robustness.

For an objective comparison of the most popular sensors, we proposed an integral efficiency index (IEI), which combines functionality, energy efficiency, and price. According to the results of our calculations, the highest efficiency is demonstrated by the Ajax LifeQuality sensor, which, despite its high cost, provides the lowest energy consumption (0.365 kW·h/year) and also has a long battery life (

Table 7. The integrated efficiency index for selected air quality sensors.

Model	Measured Parameters, F	Annual Power Consumption, (kW·h)	Energy Efficiency, E = 1/Cann	Price (USD)	IEI
Smart Air	6	26.28	0.038	16	0.014
Ajax Lifequality	3	0.365	2.74	200	0.041
104.Ua Air	3	~5.0 *	0.20	35	0.017
Airhome	4	~6.0 *	0.167	57	0.012
Saveecosensor	4	~10.0 *	0.10	80	0.005

* Estimated based on typical USB-powered sensors operating continuously at 0.5–1.5 W.

, Figure 3). Equal weighting simplifies the calculation and interpretation of IEI, which is important for its practical application in real-world projects where complex models may be less convenient [29].

At the same time, the Smart Air model is an economically viable alternative, due to its having the lowest price (~USD 16) and the widest range of measured parameters. Its IEI is 0.014, which is an acceptable compromise between functionality and energy consumption.

Thus, the use of the IEI allows not only qualitative but also quantitative justification for choosing the optimal sensor for an energy-efficient IoT air quality monitoring system.

According to the IEI, the Ajax LifeQuality achieves the highest efficiency score due to its extremely low power consumption and long battery life, despite its high cost. Smart Air, although consuming more energy, maintains a competitive IEI due to its affordability and broader parameter range, making it a viable budget-friendly option. Finally, the SaveEcoSensor demonstrates low energy efficiency relative to cost.

This combined metric supports decision-making when selecting sensor models that balance functionality, sustainability, and cost, which is especially important during large-scale deployment in smart city or environmental monitoring projects. The IEI provides a quantitative tool for sensor selection, supporting decision-making in IoT air quality monitoring projects. Future work could validate the IEI through stakeholder surveys to refine parameter weights and apply it to a broader range of sensors, including international models [45].

6. Conclusions

This study substantiates the importance of energy efficiency in the design and implementation of air quality monitoring systems based on Internet of Things (IoT) technology. The analysis demonstrates that optimizing sensor selection, data transmission protocols, and power management strategies plays a decisive role in ensuring the long-term, autonomous, and sustainable operation of such systems.

A comparative assessment of commercially available air quality sensors revealed significant differences in energy consumption, functionality, and cost. To support informed decision-making, an integrated efficiency index (IEI) was introduced, enabling a holistic evaluation of sensor performance with respect to these criteria.

Based on the analysis, an energy-efficient system architecture was proposed and quantitatively evaluated. The selected configuration, which includes the Smart Air sensor, a Zigbee-based gateway, and a Mini UPS, demonstrates the following performance indicators:

• Daily energy consumption: 72 W·h;
• Autonomous operation during outages: up to 10 h;
• UPS efficiency: 83.3%;
• Annual energy use: 26.28 kW·h.

The proposed architecture ensures reliable monitoring with a minimal energy footprint, supports integration with smart home ecosystems, and is suitable for both residential and urban-scale deployment. The system can be considered cost-effective and scalable, offering a balanced trade-off between performance and sustainability.

Future research should focus on the integration of renewable energy sources (e.g., solar power), advanced edge computing techniques, and predictive analytics for adaptive energy management in IoT-based environmental monitoring systems, new technologies such as 5G-IoT protocols for high-speed data transmission with low power consumption, and advanced energy harvesting methods such as advanced solar panels or radio frequency energy harvesting to increase the autonomy of the systems. In addition, high-precision industrial sensors such as TSI and Aeroqual are planned to be validated in various real-world scenarios to assess their suitability for large-scale IoT air quality monitoring systems, which will complement the proposed energy-efficient architecture. Future research will also involve long-term field deployments to validate the system’s performance under real-world environmental variability. Additionally, future work will explore the sensitivity of the IEI to varying weight configurations for functionality, energy efficiency, and cost, potentially achieving this through stakeholder surveys or case studies to tailor the index to specific IoT applications and AI techniques, such as anomaly detection for identifying irregular air quality patterns and adaptive control for optimizing sensor sampling rates, to enhance the system’s predictive energy management capabilities.