  Vehicle-based mobile sensing, also known as drive-by sensing, efficiently
surveys urban environments at low costs by leveraging the mobility of urban
vehicles. While recent studies have focused on drive-by sensing for fleets of a
single type, our work explores the sensing power and cost-effectiveness of a
mixed fleet that consists of vehicles with distinct and complementary mobility
patterns. We formulate the drive-by sensing coverage (DSC) problem, proposing a
method to quantify sensing utility and an optimization procedure that
determines fleet composition, sensor allocation, and vehicle routing for a
given budget. Our air quality sensing case study in Longquanyi District
(Chengdu, China) demonstrates that using a mixed fleet enhances sensing
utilities and achieves close approximations to the target sensing distribution
at a lower cost. Generalizing these insights to two additional real-world
networks, our regression analysis uncovers key factors influencing the sensing
power of mixed fleets. This research provides quantitative and managerial
insights into drive-by sensing, showcasing a positive externality of urban
transport activities.
