Kang Xu, Liu Liang, Ma Huadong
Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2017 Jan 4;17(1):88. doi: 10.3390/s17010088.
Monitoring the status of urban environments, which provides fundamental information for a city, yields crucial insights into various fields of urban research. Recently, with the popularity of smartphones and vehicles equipped with onboard sensors, a people-centric scheme, namely "crowdsensing", for city-scale environment monitoring is emerging. This paper proposes a data correlation based crowdsensing approach for fine-grained urban environment monitoring. To demonstrate urban status, we generate sensing images via crowdsensing network, and then enhance the quality of sensing images via data correlation. Specifically, to achieve a higher quality of sensing images, we not only utilize temporal correlation of mobile sensing nodes but also fuse the sensory data with correlated environment data by introducing a collective tensor decomposition approach. Finally, we conduct a series of numerical simulations and a real dataset based case study. The results validate that our approach outperforms the traditional spatial interpolation-based method.
监测城市环境状况可为城市提供基础信息，能让我们对城市研究的各个领域有至关重要的深入了解。近来，随着配备车载传感器的智能手机和车辆的普及，一种以人群为中心的方案，即“众包感知”，正兴起用于城市规模的环境监测。本文提出一种基于数据关联的众包感知方法用于细粒度城市环境监测。为展示城市状况，我们通过众包感知网络生成感知图像，然后通过数据关联提高感知图像的质量。具体而言，为获得更高质量的感知图像，我们不仅利用移动感知节点的时间相关性，还通过引入一种集体张量分解方法将传感数据与相关环境数据进行融合。最后，我们进行了一系列数值模拟以及基于真实数据集的案例研究。结果证实我们的方法优于传统的基于空间插值的方法。
