Enhancing Urban Sensing Utility with Sensor-enabled Vehicles and Easily Accessible Data

2025-12-07

AI Summary

To address uneven spatiotemporal coverage, high redundancy, and budget constraints in urban mobile sensing, this paper proposes an adaptive vehicular sensing optimization framework. The core method introduces Improved OptiFleet—a vehicle selection algorithm grounded in information entropy—integrating spatiotemporal weighting with heterogeneous open data sources (including ground-truth air quality measurements) to maximize sensing utility and minimize redundancy under dynamic urban conditions. Evaluated on a real-world fleet of 320 sensor-equipped vehicles in Guangzhou, the framework achieves up to a 5% improvement in sensing utility under identical budget constraints, while enabling smaller fleets to attain superior spatiotemporal coverage. These results demonstrate significant enhancements in both the dynamic monitoring capability of smart cities and the operational efficiency of sensing resources.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Urban sensing is essential for the development of smart cities, enabling monitoring, computing, and decision-making for urban management.Thanks to the advent of vehicle technologies, modern vehicles are transforming from solely mobility tools to valuable sensors for urban data collection, and hold the potential of improving traffic congestion, transport sustainability, and infrastructure inspection.Vehicle-based sensing is increasingly recognized as a promising technology due to its flexibility, cost-effectiveness, and extensive spatiotemporal coverage. However, optimizing sensing strategies to balance spatial and temporal coverage, minimize redundancy, and address budget constraints remains a key challenge.This study proposes an adaptive framework for enhancing the sensing utility of sensor-equipped vehicles.By integrating heterogeneous open-source data, the framework leverages spatiotemporal weighting to optimize vehicle selection and sensing coverage across various urban contexts.An entropy-based vehicle selection strategy, exttt{Improved OptiFleet}, is developed to maximize sensing utility while minimizing redundancy.The framework is validated using real-world air quality data from 320 sensor-equipped vehicles operating in Guangzhou, China, over two months.Key findings show that the proposed method outperforms baseline strategies, providing up to 5% higher sensing utility with reduced fleet sizes, and also highlights the critical role of dynamic urban data in optimizing mobile sensing strategies.

Problem

• Optimizing vehicle-based urban sensing strategies for spatial-temporal coverage
• Minimizing redundancy and addressing budget constraints in mobile sensing
• Enhancing sensing utility with adaptive frameworks and open-source data integration

Innovation

• Adaptive framework integrating heterogeneous open-source data
• Entropy-based vehicle selection strategy minimizing redundancy
• Spatiotemporal weighting optimizing sensing coverage and utility