A survey of urban drive-by sensing: An optimization perspective
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Abstract (arXiv)
Pervasive and mobile sensing is an integral part of smart transport and smart city applications. Vehicle-based mobile sensing, or drive-by sensing (DS), is gaining popularity in both academic research and field practice. The DS paradigm has an inherent transport component, as the spatial-temporal distribution of the sensors are closely related to the mobility patterns of their hosts, which may include third-party (e.g. taxis, buses) or for-hire (e.g. unmanned aerial vehicles and dedicated vehicles) vehicles. It is therefore essential to understand, assess and optimize the sensing power of vehicle fleets under a wide range of urban sensing scenarios. To this end, this paper offers an optimization-oriented summary of recent literature by presenting a four-step discussion, namely (1) quantifying the sensing quality (objective); (2) assessing the sensing power of various fleets (strategic); (3) sensor deployment (strategic/tactical); and (4) vehicle maneuvers (tactical/operational). By compiling research findings and practical insights in this way, this review article not only highlights the optimization aspect of drive-by sensing, but also serves as a practical guide for configuring and deploying vehicle-based urban sensing systems.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jan 2023.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2025AgentSense: LLMs Empower Generalizable and Explainable Web-Based Participatory Urban Sensingcited by 5
- 2024SPO-VCS: An End-to-End Smart Predict-then-Optimize Framework with Alternating Differentiation Method for Relocation Problems in Large-Scale Vehicle Crowd Sensingcited by 0
- 2026Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensingcited by 0
- 2026Reinforcement Learning for Delivery Drone-Based Participatory Sensing in Dynamic Environmentscited by 0
- 2026Reinforcement Learning for Delivery Drone-Based Participatory Sensing in Dynamic Environments21 Jul 2026
- 2026Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensing25 Mar 2026
- 2025AgentSense: LLMs Empower Generalizable and Explainable Web-Based Participatory Urban Sensing22 Oct 2025
- 2024SPO-VCS: An End-to-End Smart Predict-then-Optimize Framework with Alternating Differentiation Method for Relocation Problems in Large-Scale Vehicle Crowd Sensing27 Nov 2024