The Design and Deployment of a Self-Powered, LoRaWAN-Based IoT Environment Sensor Ensemble for Integrated Air Quality Sensing and Simulation Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.3390/air3010009
The goal of this study is to describe a design architecture for a self-powered IoT (Internet of Things) sensor network that is currently being deployed at various locations throughout the Dallas-Fort Worth metroplex to measure and report on Particulate Matter (PM) concentrations. This system leverages diverse low-cost PM sensors, enhanced by machine learning for sensor calibration, with LoRaWAN connectivity for long-range data transmission. Sensors are GPS-enabled, allowing precise geospatial mapping of collected data, which can be integrated with urban air quality forecasting models and operational forecasting systems. To achieve energy self-sufficiency, the system uses a small-scale solar-powered solution, allowing it to operate independently from the grid, making it both cost-effective and suitable for remote locations. This novel approach leverages multiple operational modes based on power availability to optimize energy efficiency and prevent downtime. By dynamically adjusting system behavior according to power conditions, it ensures continuous operation while conserving energy during periods of reduced supply. This innovative strategy significantly enhances performance and resource management, improving system reliability and sustainability. This IoT network provides localized real-time air quality data, which has significant public health benefits, especially for vulnerable populations in densely populated urban environments. The project demonstrates the synergy between IoT sensor data, machine learning-enhanced calibration, and forecasting methods, contributing to scientific understanding of microenvironments, human exposure, and public health impacts of urban air quality. In addition, this study emphasizes open source design principles, promoting transparency, data quality, and reproducibility by exploring cost-effective sensor calibration techniques and adhering to open data standards. The next iteration of the sensors will include edge processing for short-term air quality forecasts. This work underscores the transformative role of low-cost sensor networks in urban air quality monitoring, advancing equitable policy development and empowering communities to address pollution challenges.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/air3010009
- https://www.mdpi.com/2813-4168/3/1/9/pdf?version=1741783752
- OA Status
- diamond
- Cited By
- 2
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408362257
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408362257Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/air3010009Digital Object Identifier
- Title
-
The Design and Deployment of a Self-Powered, LoRaWAN-Based IoT Environment Sensor Ensemble for Integrated Air Quality Sensing and SimulationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-12Full publication date if available
- Authors
-
Lakitha O. H. Wijeratne, Daniel Kiv, John Waczak, Prabuddha M. H. Dewage, Gokul Balagopal, Muddassar Iqbal, Adam Aker, Bharana Fernando, Matthew Lary, Vinu Sooriyaarachchi, Rittik Patra, Nora Desmond, Hannah Zabiepour, D. Xi, Vardhan Agnihotri, Seth Lee, Chris Simmons, David J. LaryList of authors in order
- Landing page
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https://doi.org/10.3390/air3010009Publisher landing page
- PDF URL
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https://www.mdpi.com/2813-4168/3/1/9/pdf?version=1741783752Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2813-4168/3/1/9/pdf?version=1741783752Direct OA link when available
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Software deployment, Internet of Things, Computer science, Systems engineering, Environmental science, Embedded system, Engineering, Software engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
-
56Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.microenvironments, | 212 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 18 |
| citation_normalized_percentile.value | 0.86538787 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |