[论文解读] Dense Air Quality Maps Using Regressive Facility Location Based Drive By Sensing

Charul Paliwal, Pravesh Biyani|arXiv (Cornell University)|Jan 20, 2022

Air Quality Monitoring and Forecasting被引用 7

一句话总结

本文提出了一种回归型设施选址（RFL）框架，用于在行驶中传感（drive-by sensing）中选择最优公交车，以生成密集且精确的空气质量（AQ）地图。通过建模空间平滑性和时间自回归相关性，RFL在时空采样方面优于基线方法，与最先进方法相比，插值空气质量地图的平均相对误差最高降低25%。

ABSTRACT

Currently, fixed static sensing is a primary way to monitor environmental data like air quality in cities. However, to obtain a dense spatial coverage, a large number of static monitors are required, thereby making it a costly option. Dense spatiotemporal coverage can be achieved using only a fraction of static sensors by deploying them on the moving vehicles, known as the drive by sensing paradigm. The redundancy present in the air quality data can be exploited by processing the sparsely sampled data to impute the remaining unobserved data points using the matrix completion techniques. However, the accuracy of imputation is dependent on the extent to which the moving sensors capture the inherent structure of the air quality matrix. Therefore, the challenge is to pick those set of paths (using vehicles) that perform representative sampling in space and time. Most works in the literature for vehicle subset selection focus on maximizing the spatiotemporal coverage by maximizing the number of samples for different locations and time stamps which is not an effective representative sampling strategy. We present regressive facility location-based drive by sensing, an efficient vehicle selection framework that incorporates the smoothness in neighboring locations and autoregressive time correlation while selecting the optimal set of vehicles for effective spatiotemporal sampling. We show that the proposed drive by sensing problem is submodular, thereby lending itself to a greedy algorithm but with performance guarantees. We evaluate our framework on selecting a subset from the fleet of public transport in Delhi, India. We illustrate that the proposed method samples the representative spatiotemporal data against the baseline methods, reducing the extrapolation error on the simulated air quality data. Our method, therefore, has the potential to provide cost effective dense air quality maps.

研究动机与目标

• 解决城市地区密集静态空气质量监测的高成本问题。
• 通过为行驶中传感选择代表性公交车，提高时空采样效率。
• 利用空气质量数据中的空间平滑性和时间自回归相关性，实现更优的数据插值。
• 通过矩阵补全技术，在密集AQ地图重建中减少外推误差。
• 为部署数百个静态传感器提供可扩展、成本效益更高的替代方案。

提出的方法

• RFL框架使用子模性目标函数对空气质量数据进行建模，该函数结合了空间平滑性和时间自回归相关性。
• 采用因果自回归结构，优先选择多样化的时间采样，避免连续时间戳的冗余采样。
• 将车辆选择问题形式化为回归型设施选址问题，支持贪心选择并具备理论性能保证。
• 通过优先选择非相邻、具有代表性的位置来优化空间覆盖，减少冗余。
• 通过基于与当前时间戳相关性递减的权重，增强对后续时间戳的覆盖。
• 对采样数据应用矩阵补全，以重建密集的时空空气质量地图。

实验结果

研究问题

• RQ1 如何选择最少数量的移动车辆，以实现空气质量监测的代表性时空覆盖？
• RQ2 建模时间自回归相关性在多大程度上提升了插值空气质量地图的准确性？
• RQ3 能否联合利用空间平滑性和时间相关性，以减少采样冗余并提升插值质量？
• RQ4 RFL框架在覆盖范围和重建误差方面与基线方法相比表现如何？
• RQ5 在车辆路径选择中，空间多样性与时间相关性之间应如何实现最优平衡？

主要发现

• 当ρ=0.98时，RFL在密集AQ地图重建中实现了最低的平均相对误差（MRE），在所有公交车采样水平下均优于VBMC(CS)和VBSF(CS)。
• 在低公交车采样（20辆）情况下，VBMC(CS)优于VBSF(CS)，表明在稀疏数据下，低秩性和时间模式学习的效果较弱。
• 与FLS基线相比，RFL(ρ=0.98)将MRE降低了25%，证明通过更优采样显著提升了插值准确性。
• 覆盖度图显示，RFL在时空采样上比MCL和FLS更具多样性和代表性，尤其在时间覆盖方面表现更优。
• 该框架在保持优异FLS增益和RFL增益的同时，实现了较高的百分比停靠覆盖（PSC）和百分比覆盖（PC），表明其在空间和时间采样之间实现了良好平衡。
• RFL(ρ=0.98)在各项指标上均保持强劲表现，优于RFL(ρ=0.99)，因其对时间相关性衰减的处理更佳。