IoT-Based Sensing Systems for Urban Air Quality Forecasting
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: 30 September 2024 | Viewed by 123
Special Issue Editor
Interests: service of society using machine learning; remote sensing; smart cities; IOT; remote control vehicles (aerial, water and ground); data driven scientific discovery; data driven insights and decision support
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on how scientific, societal, and public health advancements can be facilitated using diverse IoT systems in dense urban environments (outdoors and indoors), on autonomous robotic systems, and in wearable IoT devices. These IoT devices can be enhanced by machine learning, e.g., for sensor calibration, and provide a range of data products and/or the development of advanced forecasting methods. There is a natural synergy of data from IoT sensors with operational forecasting and data assimilation systems. There are many public health benefits of precise, localized air quality information facilitated by IoT devices and/or forecasts, especially for those with health vulnerabilities. We seek contributions that shed new light on how IoT technologies can elevate urban air quality monitoring and forecasting, enhancing the scientific understanding of microenvironments, the human exposome, public health, and environmental policies. There is significant value in exploring methodologies for assessing IoT data quality and uncertainty, studying cost-effective sensor calibration techniques, characterizing the optimum spatial and temporal scales required to capture the natural variability of micro-environments, and promoting transparency and reproducibility through open source approaches, open data, open data standards, and open-design sensor systems.
Prof. Dr. David J. Lary
Guest Editor
Manuscript Submission Information
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Keywords
- IoT
- machine learning
- sensor calibration
- data products
- forecasting methods
- autonomous robotic systems
- wearable devices
- dense urban environments
- operational forecasting
- data assimilation
- data quality
- data uncertainty
- calibration
- open design
- open source
- open data
- open data standards
- spatial scales
- temporal scales
- public health
- urban air quality
- air quality forecasting
- health vulnerabilities
- environmental policies
- microenvironments
- human exposome
- transparency
- reproducibility
- natural variability
