The Design and Deployment of a Self-Powered, LoRaWAN-Based IoT Environment Sensor Ensemble for Integrated Air Quality Sensing and Simulation Article Swipe
Lakitha O. H. Wijeratne
,
Daniel Kiv
,
John Waczak
,
Prabuddha M. H. Dewage
,
Gokul Balagopal
,
Mazhar 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. Lary
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202411.1145.v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202411.1145.v1
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202411.1145.v1
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404475280
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404475280Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.20944/preprints202411.1145.v1Digital 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
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-11-15Full publication date if available
- Authors
-
Lakitha O. H. Wijeratne, Daniel Kiv, John Waczak, Prabuddha M. H. Dewage, Gokul Balagopal, Mazhar 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/10.20944/preprints202411.1145.v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.20944/preprints202411.1145.v1Direct OA link when available
- Concepts
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Software deployment, Internet of Things, Computer science, Wireless sensor network, Embedded system, Real-time computing, Computer network, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sensors | 255 |
| abstract_inverted_index.supply. | 153 |
| abstract_inverted_index.synergy | 196 |
| abstract_inverted_index.various | 26 |
| abstract_inverted_index.adhering | 245 |
| abstract_inverted_index.allowing | 66, 98 |
| abstract_inverted_index.approach | 117 |
| abstract_inverted_index.behavior | 137 |
| abstract_inverted_index.deployed | 24 |
| abstract_inverted_index.describe | 7 |
| abstract_inverted_index.enhanced | 49 |
| abstract_inverted_index.enhances | 158 |
| abstract_inverted_index.learning | 52 |
| abstract_inverted_index.low-cost | 46 |
| abstract_inverted_index.methods, | 206 |
| abstract_inverted_index.multiple | 119 |
| abstract_inverted_index.optimize | 127 |
| abstract_inverted_index.provides | 171 |
| abstract_inverted_index.quality, | 235 |
| abstract_inverted_index.quality. | 222 |
| abstract_inverted_index.resource | 161 |
| abstract_inverted_index.sensors, | 48 |
| abstract_inverted_index.strategy | 156 |
| abstract_inverted_index.suitable | 111 |
| abstract_inverted_index.systems. | 86 |
| abstract_inverted_index.(Internet | 15 |
| abstract_inverted_index.according | 138 |
| abstract_inverted_index.addition, | 224 |
| abstract_inverted_index.adjusting | 135 |
| abstract_inverted_index.benefits, | 182 |
| abstract_inverted_index.collected | 71 |
| abstract_inverted_index.currently | 22 |
| abstract_inverted_index.downtime. | 132 |
| abstract_inverted_index.exploring | 239 |
| abstract_inverted_index.exposure, | 214 |
| abstract_inverted_index.improving | 163 |
| abstract_inverted_index.iteration | 252 |
| abstract_inverted_index.leverages | 44, 118 |
| abstract_inverted_index.localized | 172 |
| abstract_inverted_index.locations | 27 |
| abstract_inverted_index.metroplex | 32 |
| abstract_inverted_index.operation | 145 |
| abstract_inverted_index.populated | 189 |
| abstract_inverted_index.promoting | 232 |
| abstract_inverted_index.real-time | 173 |
| abstract_inverted_index.solution, | 97 |
| abstract_inverted_index.conserving | 147 |
| abstract_inverted_index.continuous | 144 |
| abstract_inverted_index.efficiency | 129 |
| abstract_inverted_index.emphasizes | 227 |
| abstract_inverted_index.especially | 183 |
| abstract_inverted_index.forecasts. | 264 |
| abstract_inverted_index.geospatial | 68 |
| abstract_inverted_index.innovative | 155 |
| abstract_inverted_index.integrated | 76 |
| abstract_inverted_index.locations. | 114 |
| abstract_inverted_index.long-range | 60 |
| abstract_inverted_index.processing | 259 |
| abstract_inverted_index.scientific | 209 |
| abstract_inverted_index.short-term | 261 |
| abstract_inverted_index.standards. | 249 |
| abstract_inverted_index.techniques | 243 |
| abstract_inverted_index.throughout | 28 |
| abstract_inverted_index.vulnerable | 185 |
| abstract_inverted_index.Dallas-Fort | 30 |
| abstract_inverted_index.Particulate | 38 |
| abstract_inverted_index.calibration | 242 |
| abstract_inverted_index.conditions, | 141 |
| abstract_inverted_index.dynamically | 134 |
| abstract_inverted_index.forecasting | 81, 85, 205 |
| abstract_inverted_index.management, | 162 |
| abstract_inverted_index.operational | 84, 120 |
| abstract_inverted_index.performance | 159 |
| abstract_inverted_index.populations | 186 |
| abstract_inverted_index.principles, | 231 |
| abstract_inverted_index.reliability | 165 |
| abstract_inverted_index.significant | 179 |
| abstract_inverted_index.small-scale | 95 |
| abstract_inverted_index.GPS-enabled, | 65 |
| abstract_inverted_index.architecture | 10 |
| abstract_inverted_index.availability | 125 |
| abstract_inverted_index.calibration, | 55, 203 |
| abstract_inverted_index.connectivity | 58 |
| abstract_inverted_index.contributing | 207 |
| abstract_inverted_index.demonstrates | 194 |
| abstract_inverted_index.self-powered | 13 |
| abstract_inverted_index.environments. | 191 |
| abstract_inverted_index.independently | 102 |
| abstract_inverted_index.significantly | 157 |
| abstract_inverted_index.solar-powered | 96 |
| abstract_inverted_index.transmission. | 62 |
| abstract_inverted_index.transparency, | 233 |
| abstract_inverted_index.understanding | 210 |
| abstract_inverted_index.cost-effective | 109, 240 |
| abstract_inverted_index.concentrations. | 41 |
| abstract_inverted_index.reproducibility | 237 |
| abstract_inverted_index.sustainability. | 167 |
| abstract_inverted_index.learning-enhanced | 202 |
| abstract_inverted_index.self-sufficiency, | 90 |
| abstract_inverted_index.microenvironments, | 212 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 18 |
| citation_normalized_percentile.value | 0.23762213 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |