Dependency-Aware Task Scheduling for Vehicular Networks Enhanced by the Integration of Sensing, Communication and Computing

Vehicular networks have evolved to a new stage where they integrate sensing, communication, and computing capabilities, giving rise to a multitude of vehicular applications that cater to contemporary demands. These applications are characterized by a high degree of integration, coupled functionality between sensing, communication, and computing (SCC), and the need for timely scheduling. Most studies on the integration of sensing, communication, and computing (ISCC) for vehicular networks focus on directly matching SCC resources to task demands. However, in the era of ISCC, the interdependence among tasks is critical and therefore cannot be ignored during the task scheduling process. For instance, the computing task can only start after the sensing task is finished. In addition, the SCC resources and task demands fluctuate significantly as time goes by due to the high mobility of vehicular networks. In this paper, we propose a dependency-aware task scheduling strategy for ISCC-based vehicular networks, which takes both task interdependence and high mobility into consideration. With the proposed strategy, the demands of vehicle application tasks on SCC resources are determined after the relationship between the tasks is examined. In addition, the mobility of vehicles is taken into consideration in order to properly match the demands of the sources on different vehicles. Finally, a meta deep reinforcement learning-based task scheduling (MTS) algorithm is used to make the appropriate task scheduling decision. Extensive simulation results indicate that the proposed strategy can effectively reduce dependent task processing delay in dynamic vehicular networks. In addition, the MTS approach ensures that the proposed strategy can quickly adapt to new vehicular network environments.