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Sensors 2017, 17(11), 2531;

Adaptive Sampling for Urban Air Quality through Participatory Sensing

1,2,* and 3,*
Electronic Information School, Wuhan University, Wuhan 430072, China
Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China
School of Information Management and Statistics, Hubei University of Economics, Wuhan 430205, China
Authors to whom correspondence should be addressed.
Received: 27 September 2017 / Revised: 26 October 2017 / Accepted: 31 October 2017 / Published: 3 November 2017
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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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. View Full-Text
Keywords: urban sensing; air quality sensing; data sampling; adaptiveness urban sensing; air quality sensing; data sampling; adaptiveness

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Zeng, Y.; Xiang, K. Adaptive Sampling for Urban Air Quality through Participatory Sensing. Sensors 2017, 17, 2531.

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