EdgeVCD: Intelligent Algorithm-Inspired Content Distribution in Vehicular Edge Computing Network
Quyuan Luo, Changle Li, Tom H. Luan, Weisong Shi
Abstract
Vehicular edge computing (VEC), which integrates mobile-edge computing (MEC) into vehicular networks, can provide more capability for executing resource-hungry applications and lower latency for connected vehicles. Distributing the result content to connected vehicles is vital for them to take proper actions based on computing results. However, the increasing number of connected vehicles and the limited communication resources make the content distribution a challenge. Besides, the diversity of connected vehicles and contents makes it more challenging for content distribution. To address this issue, in this article, we propose EdgeVCD, an intelligent algorithm-inspired content distribution scheme. Specifically, we first propose a dual-importance (DI) evaluation approach to reflect the relationship between the Priority of Vehicles (PoV) and the Priority of Contents (PoC). To make use of the limited communication resources, we then formulate an optimization problem to maximize the system utility for content distribution. To solve the complex optimization problem effectively, we first divide the road into small segments. Then, we propose a fuzzy-logic-based method to select the most proper content replica vehicle (CRV) for aiding content distribution and redefine the number of content request vehicles in each segment. Thereafter, the optimization problem is transformed into a nonlinear integer programming problem. Inspired by the artificial immune system, we propose an immune clone-based algorithm to solve it, which has a fast convergence to an optimal solution. Extensive simulations validate the effectiveness of our proposed EdgeVCD in terms of system utility, average utility, and convergence.