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Adaptive Sampling for Urban Air Quality through Participatory Sensing
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 t...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712849/ https://www.ncbi.nlm.nih.gov/pubmed/29099766 http://dx.doi.org/10.3390/s17112531 |
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author | Zeng, Yuanyuan Xiang, Kai |
author_facet | Zeng, Yuanyuan Xiang, Kai |
author_sort | Zeng, Yuanyuan |
collection | PubMed |
description | 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 Q-learning. The evaluation results show that AS-air provides an energy-efficient sampling strategy, which is adaptive toward the varied outside air environment with good sampling efficiency. |
format | Online Article Text |
id | pubmed-5712849 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-57128492017-12-07 Adaptive Sampling for Urban Air Quality through Participatory Sensing Zeng, Yuanyuan Xiang, Kai Sensors (Basel) Article 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 Q-learning. The evaluation results show that AS-air provides an energy-efficient sampling strategy, which is adaptive toward the varied outside air environment with good sampling efficiency. MDPI 2017-11-03 /pmc/articles/PMC5712849/ /pubmed/29099766 http://dx.doi.org/10.3390/s17112531 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zeng, Yuanyuan Xiang, Kai Adaptive Sampling for Urban Air Quality through Participatory Sensing |
title | Adaptive Sampling for Urban Air Quality through Participatory Sensing |
title_full | Adaptive Sampling for Urban Air Quality through Participatory Sensing |
title_fullStr | Adaptive Sampling for Urban Air Quality through Participatory Sensing |
title_full_unstemmed | Adaptive Sampling for Urban Air Quality through Participatory Sensing |
title_short | Adaptive Sampling for Urban Air Quality through Participatory Sensing |
title_sort | adaptive sampling for urban air quality through participatory sensing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712849/ https://www.ncbi.nlm.nih.gov/pubmed/29099766 http://dx.doi.org/10.3390/s17112531 |
work_keys_str_mv | AT zengyuanyuan adaptivesamplingforurbanairqualitythroughparticipatorysensing AT xiangkai adaptivesamplingforurbanairqualitythroughparticipatorysensing |