ArticleSensors (Basel, Switzerland)2017
Adaptive Sampling for Urban Air Quality through Participatory Sensing.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- An Adaptive Sampling Framework for Life Cycle Degradation Monitoring.Sensors (Basel, Switzerland) · 2023Article
- Academically Produced Air Pollution Sensors for Personal Exposure Assessment: The Canarin Project.Sensors (Basel, Switzerland) · 2021Article
- A Deep CNN-LSTM Model for Particulate Matter (PMSensors (Basel, Switzerland) · 2018Article
- Performance Evaluation of Energy-Autonomous Sensors Using Power-Harvesting Beacons for Environmental Monitoring in Internet of Things (IoT).Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Air pollution is one of the major problems of the modern world. The popularization and powerful functions of smartphone applications enable people to participate in urban sensing to better know about the air problems surrounding them. Data sampling is one of the most important problems that affect the sensing performance. In this paper, we propose an Adaptive Sampling Scheme for Urban Air Quality (AS-air) through participatory sensing. Firstly, we propose to find the pattern rules of air quality according to the historical data contributed by participants based on Apriori algorithm. Based on it, we predict the on-line air quality and use it to accelerate the learning process to choose and adapt the sampling parameter based on
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Registered trials
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