Spatiotemporal air quality inference of low-cost sensor data: Evidence from multiple sensor testbeds
Recent advances in sensor and IoT technologies allow for denser and mobile air quality
measurements. These measurements are still spatiotemporally sparse at city-level, but can …
measurements. These measurements are still spatiotemporally sparse at city-level, but can …
Intelligent coverage and cost-effective monitoring: Bus-based mobile sensing for city air quality
M Huang, X Li, M Yang, X Kuai - Computers, Environment and Urban …, 2024 - Elsevier
Bus-based mobile sensing has emerged as a cost-effective approach for collecting high
spatio-temporal air quality data by leveraging the mobility of buses. However, when …
spatio-temporal air quality data by leveraging the mobility of buses. However, when …
Mapping air quality in IoT cities: Cloud calibration and air quality inference of sensor data
Monitoring air quality in cities is challenging as a high resolution in both space and time is
required to accurately assess population exposure. This paper presents an innovative IoT …
required to accurately assess population exposure. This paper presents an innovative IoT …
Completeness assessment and improvement in mobile crowd-sensing environments
S Mehanna, Z Kedad, M Chachoua - SN Computer Science, 2022 - Springer
Mobile sensors are increasingly used to monitor air quality to accurately quantify human
exposure to air pollution. These sensors are subject to various issues (misuse, malfunctions …
exposure to air pollution. These sensors are subject to various issues (misuse, malfunctions …
Street-level air quality inference based on geographically context-aware random forest using opportunistic mobile sensor network
The spatial heterogeneity and temporal variability of air pollution in urban environments
make air quality inference for fine-grained air pollution monitoring extremely challenging …
make air quality inference for fine-grained air pollution monitoring extremely challenging …
Leveraging machine learning for multi-source data enrichment and analytics in air quality monitoring and crowd sensing
M Abboud - 2023 - theses.hal.science
Data enrichment using machine anddeep learning techniques in the context of the Internet
of Things (IoT) has become increasinglycrucial in today's technological landscape. Dueto …
of Things (IoT) has become increasinglycrucial in today's technological landscape. Dueto …
Spatiotemporal air quality inference of low-cost sensor data; application on a cycling monitoring network
Air quality monitoring in heterogeneous cities is challenging as a high resolution in both
space and time is required to accurately assess population exposure. As regulatory …
space and time is required to accurately assess population exposure. As regulatory …
[PDF][PDF] Spatiotemporal Air Quality Inference of Low-Cost Sensor Data; Application on a Cycling Monitoring Network
ME Nikolaou, W Philips, N Deligiannis, VP La Manna - 2021 - academia.edu
Air quality monitoring in heterogeneous cities is challenging as a high resolution in both
space and time is required to accurately assess population exposure. As regulatory …
space and time is required to accurately assess population exposure. As regulatory …