Towards Uniform Urban Map Coverage in Vehicular Crowd-Sensing: a Decentralized Incentivization Solution

Abstract

Vehicular Crowd-Sensing (VCS) is a well-known data collection approach leveraging sensors of connected vehicles to efficiently gather contextual information in urban environments. High-mileage vehicles such as taxis are often regarded as effective VCS platforms, due to their pervasiveness in modern cities, even though the road network coverage achievable by these vehicles is still an open issue. Indeed, their drivers generally follow the most-efficient route to destination, leading to major roads being frequently visited, while others are often neglected.

To address this issue, many centralized incentivization solutions have been proposed to recruit/reward drivers accepting minor detours towards roads with higher sensing demand. However, these works mostly focus on assigning specific sensing tasks to drivers, rather than achieving an overall better-balanced urban sensing coverage, which is nonetheless required for many use cases, such as air quality monitoring.

To fill this gap, we present ROUTR, an incentivization budget-aware routing solution designed to achieve more uniform coverage in VCS without requiring central coordination, thus significantly reducing back-end infrastructure costs. We empirically evaluated the proposal using taxi traces collected in the City of San Francisco. Results highlighted that, even with small incentivization budgets, our proposal leads to significantly more uniform urban road network coverage.

Additional material

A step-by-step simulation of the application of the ROUTR algorithm on a small road-network graph is available here.