Sensing Quality-Aware Task Allocation for Multidimensional Vehicular Urban Sensing

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.