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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/17/11/2531 |
| _version_ | 1811187057232445440 |
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| author | Yuanyuan Zeng Kai Xiang |
| author_facet | Yuanyuan Zeng Kai Xiang |
| author_sort | Yuanyuan Zeng |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T13:56:50Z |
| format | Article |
| id | doaj.art-2166b95db6604549980a35f964476c70 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:56:50Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2166b95db6604549980a35f964476c702022-12-22T04:20:18ZengMDPI AGSensors1424-82202017-11-011711253110.3390/s17112531s17112531Adaptive Sampling for Urban Air Quality through Participatory SensingYuanyuan Zeng0Kai Xiang1Electronic Information School, Wuhan University, Wuhan 430072, ChinaSchool of Information Management and Statistics, Hubei University of Economics, Wuhan 430205, ChinaAir 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.https://www.mdpi.com/1424-8220/17/11/2531urban sensingair quality sensingdata samplingadaptiveness |
| spellingShingle | Yuanyuan Zeng Kai Xiang Adaptive Sampling for Urban Air Quality through Participatory Sensing Sensors urban sensing air quality sensing data sampling adaptiveness |
| 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 | urban sensing air quality sensing data sampling adaptiveness |
| url | https://www.mdpi.com/1424-8220/17/11/2531 |
| work_keys_str_mv | AT yuanyuanzeng adaptivesamplingforurbanairqualitythroughparticipatorysensing AT kaixiang adaptivesamplingforurbanairqualitythroughparticipatorysensing |