ABSTRACT

Current vehicles are equipped with a number of environmental sensors to improve safety and quality of life for passengers. Many researchers have shown that these sensors can also be exploited for opportunistic crowd-sensing. Useful new services can be developed on top of these data, like urban surveillance of Smart Cities. The spatio-temporal sensing coverage achievable with Vehicular Crowd-Sensing (VCS), however, is an open issue, since vehicles are not uniformly distributed over the road network, undermining the quality of potential services based on VCS data.

In this paper, we present an evolution of the standard A${ }^{\mathbf{\ast }}$ routing algorithm, meant to increase VCS coverage by selecting a route in a random way among all those satisfying a parametric constraint on the total cost of the path. The proposed solution is based on an edge-computing paradigm, not requiring a central coordination but rather leveraging the computational resources available on-board, significantly reducing the back-end infrastructure costs. The proposed solution has been empirically evaluated on two public datasets of 450,000 real taxi trajectories from two cities, San Francisco and Porto, characterized by a very different road network topology. Results show sensible improvements in terms of achievable spatio-temporal sensing coverage of probe vehicles.

摘要

目前的车辆配备了许多环境传感器，以提高乘客的安全性和生活质量。许多研究人员已经表明，这些传感器也可以用于机会性人群感应。可以在这些数据的基础上开发有用的新服务，例如智能城市的城市监控。然而，车辆人群传感 (VCS) 可实现的时空传感覆盖是一个悬而未决的问题，因为车辆在道路网络上分布不均匀，从而破坏了基于 VCS 数据的潜在服务的质量。

在本文中，我们介绍了标准A${ }^{\mathbf{＊}}$路由算法，旨在通过在所有满足路径总成本参数约束的路由中以随机方式选择路由来增加 VCS 覆盖范围。提议的解决方案基于边缘计算范式，不需要中央协调，而是利用板上可用的计算资源，显着降低后端基础设施成本。所提出的解决方案已在来自旧金山和波尔图两个城市的 450,000 条真实出租车轨迹的两个公共数据集上进行了实证评估，其特点是道路网络拓扑结构截然不同。结果表明，在探测车辆可实现的时空传感覆盖方面取得了显着的改进。