Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zeng, Yuanyuan, Xiang, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
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
_version_ 1783283300007149568
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