Task Scheduling With UAV-Assisted Vehicular Cloud for Road Detection in Highway Scenario
Jiangfan Li, Xiaofeng Cao, Deke Guo, Junjie Xie, Honghui Chen
Abstract
Vehicular cloud computing (VCC) has been utilized to enhance traffic management and road safety. By connecting with base stations (BSes), VCC can provide the information of real-time dynamics for smart vehicles (SVs). However, the area outside the coverage of BSes will be the blind areas, where SVs cannot obtain the real-time safety guarantee, especially on the highway. In this article, we utilize unmanned aerial vehicles (UAVs) to assist the communication between SVs and BSes to solve the above problem. In particular, we study the interdependency task scheduling for the highway driving environment detection, where SVs, BSes, and UAVs collect the environmental data, schedule tasks, and feedback results cooperatively. There are two main problems in this scenario: 1) the scheduling within the coverage of BSes and 2) the rescheduling between the coverage of BSes. We model both the processes as constrained numerical optimization problems aiming to minimize the request-response time. To this end, we propose a systematical scheduling scheme named Teso, which consists of two stages: 1) designed approximation algorithm for scheduling and 2) offloading algorithm for rescheduling. Extensive experiments show that Teso can significantly reduce the response time overall and improve the system stability.