车载边缘计算中的成本感知任务卸载:堆栈博弈方法

IF 5.8 2区 计算机科学 Q1 TELECOMMUNICATIONS
Shujuan Wang, Dongxue He, Mulin Yang, Lin Duo
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引用次数: 0

摘要

随着车载通信系统和移动边缘车联网的普及,车联网(IoVs)中出现了智能交通应用,这些应用对延迟敏感、计算密集,需要足够的计算和通信资源。为满足这些应用的要求,计算卸载作为一种新模式应运而生,可利用车辆上的闲置资源协同完成任务。然而,要在车辆间成功实现任务卸载,还存在一些障碍。首先,当任务卸载到服务车辆上时,会产生额外的成本,如通信开销和能源消耗。另一方面,由于周围有许多车辆,用户车辆和服务车辆都在努力通过匹配完美的服务/用户车辆来实现成本和利润之间的平衡,并达成对各方都有利的最佳卸载方案。为了解决这些问题,这项工作的重点是设计有效的激励机制,以刺激拥有闲置资源的车辆积极参与卸载过程。本文提出了一种基于模糊逻辑的动态定价策略,以准确评估车辆处理任务的成本,为找到最优卸载决策提供有见地的指导。同时,对车辆之间的竞争与合作关系进行了深入研究,并将其建模为一个两阶段的 Stackelberg 博弈。本研究特别强调了车辆的社会属性及其对卸载决策过程的影响,并在优化问题的设计中精心整合了多个关键属性,如 UV 在本地承担任务的意愿、UV 的声誉以及 SV 对分配任务比例的满意度。提出了一种复杂度适用的分布式算法来解决该问题,并找到最优任务卸载策略。在实际场景中进行了大量仿真,结果表明,在不同的网络和信道环境下,所提出的机制在车辆效用、成本、完成延迟等方面都取得了显著的性能优势,证明了这项工作的有效性和高效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cost-aware task offloading in vehicular edge computing: A Stackelberg game approach

With the popularity of vehicular communication systems and mobile edge vehicle networking, intelligent transportation applications arise in Internet of Vehicles (IoVs), which are latency-sensitive, computation-intensive, and requiring sufficient computing and communication resources. To satisfy the requirements of these applications, computation offloading emerges as a new paradigm to utilize idle resources on vehicles to cooperatively complete tasks. However, there exist several obstacles for realizing successful task offloading among vehicles. For one thing, extra cost such as communication overhead and energy consumption occurs when a task is offloaded on a service vehicle, it is unlikely to expect the service vehicle will contribute its resources without any reward. For another, since there are many vehicles around, both user vehicles and service vehicles are trying to strike a balance between cost and profit, through matching the perfect service/user vehicles and settled with optimal offloading plan that is beneficial to all parties. To solve these issues, this work focuses on the design of effective incentive mechanisms to stimulate vehicles with idle resources to actively participate in the offloading process. A fuzzy logic-based dynamic pricing strategy is proposed to accurately evaluate the cost of a vehicle for processing the task, which provides insightful guidance for finding the optimal offloading decision. Meanwhile, the competitive and cooperation relations among vehicles are thoroughly investigated and modeled as a two-stage Stackelberg game. Particularly, this work emphasizes the social attributes of vehicles and their effect on the offloading decision making process, multiple key properties such as the willingness of UV to undertake the task locally, the reputation of UV and the satisfaction of SV for the allocated task proportion, are carefully integrated in the design of the optimization problem. A distributed algorithm with applicable complexity is proposed to solve the problem and to find the optimal task offloading strategy. Extensive simulations are conducted on real-world scenarios and results show that the proposed mechanism achieves significant performance advantages in terms of vehicles' utilities, cost, completion delay under varied network and channel environment, which justifies the effectiveness and efficiency of this work.

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来源期刊
Vehicular Communications
Vehicular Communications Engineering-Electrical and Electronic Engineering
CiteScore
12.70
自引率
10.40%
发文量
88
审稿时长
62 days
期刊介绍: Vehicular communications is a growing area of communications between vehicles and including roadside communication infrastructure. Advances in wireless communications are making possible sharing of information through real time communications between vehicles and infrastructure. This has led to applications to increase safety of vehicles and communication between passengers and the Internet. Standardization efforts on vehicular communication are also underway to make vehicular transportation safer, greener and easier. The aim of the journal is to publish high quality peer–reviewed papers in the area of vehicular communications. The scope encompasses all types of communications involving vehicles, including vehicle–to–vehicle and vehicle–to–infrastructure. The scope includes (but not limited to) the following topics related to vehicular communications: Vehicle to vehicle and vehicle to infrastructure communications Channel modelling, modulating and coding Congestion Control and scalability issues Protocol design, testing and verification Routing in vehicular networks Security issues and countermeasures Deployment and field testing Reducing energy consumption and enhancing safety of vehicles Wireless in–car networks Data collection and dissemination methods Mobility and handover issues Safety and driver assistance applications UAV Underwater communications Autonomous cooperative driving Social networks Internet of vehicles Standardization of protocols.
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