Zeng Yuanyuan, Xiang Kai
Electronic Information School, Wuhan University, Wuhan 430072, China.
Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China.
Sensors (Basel). 2017 Nov 3;17(11):2531. doi: 10.3390/s17112531.
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 -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.
空气污染是现代世界的主要问题之一。智能手机应用程序的普及和强大功能使人们能够参与城市传感，以更好地了解周围的空气问题。数据采样是影响传感性能的最重要问题之一。在本文中，我们提出了一种通过参与式传感的城市空气质量自适应采样方案（AS-air）。首先，我们建议根据参与者贡献的历史数据，基于Apriori算法找出空气质量的模式规则。在此基础上，我们预测在线空气质量，并利用它来加速学习过程，以基于强化学习选择和调整采样参数。评估结果表明，AS-air提供了一种节能的采样策略，它能适应变化的外部空气环境，具有良好的采样效率。
