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

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.

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Institutions

Publication Details

Journal
Energies
Published
2025-07-16
DOI
https://doi.org/10.3390/en18143768
Citations
3
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
2.40
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article

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

Орест Кочан, Elvira Dzhumelia, T. Drzymała, Кrzysztof Przystupa et al.
3 citations
Energies
Air Quality Monitoring and Forecasting
2.40
article

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

Орест Кочан, Elvira Dzhumelia, T. Drzymała, Кrzysztof Przystupa, Nataliya Bernatska
article en
3 citations

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.

EnergiesVol. 18(14)
Akademia Pożarnicza (PL), Hubei University of Technology (CN), Lublin University of Technology (PL), Lviv Polytechnic National University (UA)
Openalex Percentile: Top 12%
Air Quality Monitoring and Forecasting
2.40
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Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology — Орест Кочан, Elvira Dzhumelia, et al. · Energies (2025) | TGRS Research Map | TGRS