Department of Computer Science and Engineering, SRM University, Amaravati 522502, India.
College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
Sensors (Basel). 2023 Jan 6;23(2):667. doi: 10.3390/s23020667.
With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called warm ptimized on-dominated sorting enetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay-energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.
随着延迟敏感和能量关键型车载应用的出现，将感知数据从车辆转发到云端在实践上变得不可行。因此，提出了一种新的计算模型，称为车联网雾计算（Vehicular Fog Computing，VFC）。它将计算工作负载从车载设备（Passenger devices，PDs）卸载到交通基础设施（如路边单元（Roadside units，RSUs）和基站（Base stations，BSs）），称为静态雾节点。它还可以利用附近车辆的未充分利用的计算资源，这些车辆可以充当车联网雾节点（Vehicular fog nodes，VFNs），并提供延迟敏感和节能的计算服务。然而，VFC 的容量规划和设计，属于设施定位问题（Facility Location Problems，FLPs）的一类，是一个具有挑战性的问题。这种复杂性源于车辆交通的时空动态、来自 PD 应用的资源需求变化，以及 VFN 的移动性。本文提出了一种多目标优化模型，用于研究 VFC 网络中的设施定位。该模型的解决方案生成了与服务延迟和能量消耗之间优化权衡（Pareto 前沿）相关的最优 VFC 拓扑。因此，为了解决这个模型，我们提出了一种混合的进化多目标（Evolutionary Multi-Objective，EMO）算法，称为基于优化的非支配排序遗传算法（Warm ptimized on-dominated Sorting Genetic Algorithm，SONG）。它结合了两种流行的 EMO 算法的收敛性和搜索效率：非支配排序遗传算法（Non-dominated Sorting Genetic Algorithm，NSGA-II）和速度约束粒子群优化算法（Speed-constrained Particle Swarm Optimization，SMPSO）。首先，我们使用 SONG 算法解决一个示例问题，以说明延迟-能量解决方案前沿，并绘制相应的布局拓扑。然后，我们在真实的车辆轨迹上针对三个质量指标（超体积（Hyper-Volume，HV）、倒置世代距离（Inverted Generational Distance，IGD）和 CPU 延迟差距）评估 SONG 算法的进化性能。实证结果表明，SONG 算法在 NSGA-II 和 SMPSO 算法的基础上提高了求解质量，因此可以作为服务提供商规划和设计 VFC 网络的潜在工具。
