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Crowd-sourced air quality studies: A review of the literature & portable sensors
Abstract Development of low-cost, portable, and low-power devices for monitoring airborne
pollutants is a crucial step towards develo** improved air quality models and better …
pollutants is a crucial step towards develo** improved air quality models and better …
[HTML][HTML] Crowdsourcing public engagement for urban planning in the global south: methods, challenges and suggestions for future research
Crowdsourcing could potentially have great benefits for the development of sustainable
cities in the Global South (GS), where a growing population and rapid urbanization …
cities in the Global South (GS), where a growing population and rapid urbanization …
Maximizing coverage and maintaining connectivity in WSN and decentralized IoT: an efficient metaheuristic-based method for environment-aware node deployment
The node deployment problem is a non-deterministic polynomial time (NP-hard). This study
proposes a new and efficient method to solve this problem without the need for predefined …
proposes a new and efficient method to solve this problem without the need for predefined …
Low cost sensor with IoT LoRaWAN connectivity and machine learning-based calibration for air pollution monitoring
S Ali, T Glass, B Parr, J Potgieter… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Air pollution poses significant risk to environment and health. Air quality monitoring stations
are often confined to a small number of locations due to the high cost of the monitoring …
are often confined to a small number of locations due to the high cost of the monitoring …
Intelligent calibration and virtual sensing for integrated low-cost air quality sensors
This paper presents the development of air quality low-cost sensors (LCS) with improved
accuracy features. The LCS features integrate machine learning based calibration models …
accuracy features. The LCS features integrate machine learning based calibration models …
Regional air quality forecasting using spatiotemporal deep learning
Accelerated urbanization and industrialization have led to poor air quality, which threatens
human health with various lung ailments. Monitoring, modeling, and forecasting air quality …
human health with various lung ailments. Monitoring, modeling, and forecasting air quality …
Urban healthcare big data system based on crowdsourced and cloud-based air quality indicators
The ever accelerating process of globalization enables more than half the population to live
in cities. Thus, the air quality in cities exerts critical influence on the health status of more …
in cities. Thus, the air quality in cities exerts critical influence on the health status of more …
IoT based urban climate monitoring using Raspberry Pi
Internet of Things is the web of physical objects that contain the embedded technology
which is hel** to develop man to machine or machine to machine communication. This …
which is hel** to develop man to machine or machine to machine communication. This …
HazeEst: Machine learning based metropolitan air pollution estimation from fixed and mobile sensors
Metropolitan air pollution is a growing concern in both develo** and developed countries.
Fixed-station monitors, typically operated by governments, offer accurate but sparse data …
Fixed-station monitors, typically operated by governments, offer accurate but sparse data …
Calibration assessment of low-cost carbon dioxide sensors using the extremely randomized trees algorithm
As the monitoring of carbon dioxide is an important proxy to estimate the air quality of indoor
and outdoor environments, it is essential to obtain trustful data from CO2 sensors. However …
and outdoor environments, it is essential to obtain trustful data from CO2 sensors. However …