Cost-and-Quality Aware Data Collection for Edge-Assisted Vehicular Crowdsensing

In view of the rapid development of edge computing and vehicular network, edge-assisted vehicular crowdsensing system has brought significant benefits to Intelligent Transportation Systems (ITS). To satisfy the requirements of ITS applications, a substantial number of sensed data are generated continuously, which incurs considerable communication and computation delays. In addition, with fast-moving vehicles and the sheer amount of data, data loss becomes an issue during the process of data uploading. To deal with these challenges, this paper aims to propose a cost-and-quality aware data collection scheme for edge-assisted vehicular crowdsensing system, so as to satisfy the accuracy and timeliness requirements of ITS applications without overloading the network. Particularly, we design a functional architecture for edge-assisted vehicular crowdsensing system, where vehicular sensing, data uploading and sensing parameter adjustment interwork to enable ITS applications. Based on the architecture, two effective algorithms are proposed, which are adaptive clustering and online sensing parameter adjustment. Adaptive clustering algorithm can support reliable and timely data uploading by taking into account the cluster stability and the communication reliability during the process of clustering vehicles. Online sensing parameter adjustment algorithm can guide the actions of intelligent vehicles based on the real-time traffic condition, which can achieve a tradeoff between sensing accuracy and communication overhead. In order to evaluate the performance of our scheme, we develop a simulation platform in which all the components in the system are built. By conducting extensive simulation, the superiority of the proposed scheme is validated.