2023/11/26 by Ke Han, Han, Ke, Wen Ji +8 · 1 citation
Computer Science · Engineering · Social Sciences · #FOS: Computer and information sciences #FOS: Mathematics #Mobile Crowdsensing and Crowdsourcing #Networking and Internet Architecture (cs.NI) #Optimization and Control (math.OC) #Transportation Planning and Optimization #Urban and Freight Transport Logistics
paper · pdf · doi:10.48550/arxiv.2311.15237
openalex publication_date 2023/11/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.