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

TL;DR

This paper presents an adaptive framework that uses open-source data and an entropy-based vehicle selection strategy to optimize urban sensing with sensor-enabled vehicles, improving data utility and reducing costs.

Contribution

It introduces a novel adaptive framework and the Improved OptiFleet strategy for optimizing vehicle-based urban sensing using heterogeneous data sources.

Findings

Up to 5% higher sensing utility compared to baseline methods.

Reduced fleet sizes while maintaining sensing effectiveness.

Validated with real-world data from Guangzhou, China.

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, \texttt{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.