Vehicular sensing has become attracting an increasing research interest for cost-effective monitoring in urban areas. Even though multiple types of sensing data are required to form a multidimensional sensing map in urban sensing applications, most of the previous works have only considered the sensing quality of single sensor type. In this article, we formulate an optimization problem of task allocation to improve the overall sensing quality in multidimensional vehicular urban sensing. To mitigate the high complexity of the formulated problem, we prove the submodularity of the objective function and present a low-complexity heuristic algorithm called sensing quality-aware task allocation (SQTA) leveraging the property of submodular optimization. Extensive experiments have been conducted by using two real-world data sets, which demonstrate that SQTA can improve the average sensing quality of multiple sensor types and also guarantee sufficient levels of the sensing quality of all sensor types.

中文翻译：

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多维车辆城市感知的感知质量感知任务分配


车辆传感已经吸引了越来越多的研究兴趣，以实现城市地区经济高效的监控。尽管在城市传感应用中需要多种类型的传感数据来形成多维传感地图，但之前的大多数工作仅考虑单一传感器类型的传感质量。在本文中，我们提出了任务分配的优化问题，以提高多维车辆城市感知的整体感知质量。为了减轻所提出的问题的高复杂性，我们证明了目标函数的子模性，并提出了一种低复杂性启发式算法，称为感知质量感知任务分配（SQTA），利用子模优化的特性。使用两个真实数据集进行了大量实验，表明SQTA可以提高多种传感器类型的平均传感质量，并保证所有传感器类型的传感质量达到足够的水平。