A Joint Optimization of Computation and Communication Resource Allocation Algorithm in VEC Networks

With the advancements in fifth-generation (5G) networking and Internet of Things (IoT) technologies, Internet of Vehicles (IoVs) networks have developed in a fast speed in recent years. The number of vehicle terminals (VTs) is increasing rapidly, which contributes to various kinds of vehicular applications, like path planning and information collection. Processing these vehicular applications generally requires a large amount of computation resources. However, due to the limited physical space, the VTs do not have enough resources to handle these vehicular applications. The emergence of mobile edge computing (MEC) provides a promising paradigm for dealing with vehicular applications. By combining MEC with vehicular networks, vehicular edge computing (VEC) is considered a novel solution method to provide rich computational resources and improve the performance of vehicular networks. Nevertheless, how to optimally allocate computation and communication resources such that the energy consumption and processing delay are both reduced is a challenging problem. In this paper, we try our best to solve this problem by proposing an efficient resource allocation scheme. An optimization problem to minimize energy consumption and processing delay in VEC networks is formulated and solved by applying the proposed scheme. The simulation results show that the proposed resource allocation algorithm can reduce processing cost from the perspective of energy consumption and processing delay compared with the benchmark algorithms.