Incentive-Driven Fog-Edge Computation Offloading and Resource Allocation for 5G-NR V2X-Based Vehicular Networks

Vehicular fog-edge computing (VFEC) is a new paradigm that has significant advantages in expanding the resource capacity of mobile edge computing (MEC) servers in vehicular networks. This work studies an incentive-based vehicular fog-edge computational offloading, and resource allocation schemes for vehicular networks using 5G-NR V2X communications. Here, smart vehicles in a given area are assumed to have computationally intensive tasks which can offload to the roadside unit (RSU) with a MEC server. To expand the resource capacity of the MEC server, in this work, we propose an incentive-based vehicular fog-edge computational offloading, and resource allocation scheme to maximize the unified utility function of the system with delay and incentive satisfaction of the entities under deadline and incentive constraints. This results in the mixed integer non-linear programming problem that is solved using continuous relaxation and an alternating optimization between convex sub-problems. Simulations are provided to demonstrate the effectiveness of the proposed scheme.