Vehicle Speed Aware Computing Task Offloading and Resource Allocation Based on Multi-Agent Reinforcement Learning in a Vehicular Edge Computing Network

For in-vehicle application, the vehicles with different speeds have different delay requirements. However, vehicle speeds have not been extensively explored, which may cause mismatching between vehicle speed and its allocated computation and wireless resource. In this paper, we propose a vehicle speed aware task offloading and resource allocation strategy, to decrease the energy cost of executing tasks without exceeding the delay constraint. First, we establish the vehicle speed aware delay constraint model based on different speeds and task types. Then, the delay and energy cost of task execution in VEC server and local terminal are calculated. Next, we formulate a joint optimization of task offloading and resource allocation to minimize vehicles' energy cost subject to delay constraints. MADDPG method is employed to obtain offloading and resource allocation strategy. Simulation results show that our algorithm can achieve superior performance on energy cost and task completion delay.Comment: 8 pages, 6 figures, Accepted by IEEE International Conference on Edge Computing 202

A Simulation Framework for Traffic Safety with Connected Vehicles and V2X Technologies

With the advancement in automobile technologies, existing research shows that connected vehicle (CV) technologies can provide better traffic safety through Surrogate Safety Measure (SSM). CV technologies involves two network systems: traffic network and wireless communication network. We found that the research in the wireless communication network for CV did not interact properly with the research in SSM in transportation network, and vice versa. Though various SSM has been proposed in previous studies, a few of them have been tested in simulation software in limited extent. On the other hand, A large body of researchers proposed various communication architecture for CV technologies to improve communication performance. However, none of them tested the advanced SSM in their proposed architecture. Hence, there exists a research gap between these two communities, possibly due to difference in research domain. In this study, we developed a V2X simulation framework using SUMO, OMNeT++ and Veins for the development and testing of various SSM algorithms in run time simulation. Our developed framework has three level of communication ( CV to RSU To TS) system and is applicable for large traffic network that can have mixed traffic system (CV and non-CV), multiple road side unit (RSUs), and traffic server (TS). Moreover, the framework can be used to test SSM algorithms for other traffic networks without doing much modification. Our developed framework will be publicly available for its further development and optimization