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.

中文翻译：

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车辆边缘计算中的成本感知任务卸载：Stackelberg 博弈方法


随着车载通信系统和移动边缘车联网的普及，智能交通应用在车联网中兴起，这些应用对延迟敏感、计算密集型，需要足够的计算和通信资源。为了满足这些应用的要求，计算卸载作为一种新的范式出现，利用车辆上的闲置资源来协作完成任务。然而，实现车辆之间成功的任务卸载存在一些障碍。一方面，当任务卸载到服务车辆上时，会产生通信开销和能源消耗等额外成本，不太可能期望服务车辆会在没有任何奖励的情况下贡献其资源。另一方面，由于周围车辆较多，用户车辆和服务车辆都在努力在成本和利润之间取得平衡，通过完美的服务/用户车辆匹配，并解决对各方都有利的最优卸载方案。针对这些问题，本文的工作重点是设计有效的激励机制，以激励资源闲置的车辆积极参与卸载过程。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，为寻找最佳卸载决策提供富有洞察力的指导。同时，对车辆之间的竞争与合作关系进行了深入研究，并将其建模为两阶段Stackelberg博弈。 特别是，这项工作强调了车辆的社会属性及其对卸载决策过程的影响，多个关键属性，如UV在本地承担任务的意愿、UV的声誉和SV对分配的任务比例的满意度，仔细地融入到优化问题的设计中。提出了一种具有适用复杂度的分布式算法来解决该问题并找到最佳的任务卸载策略。对现实场景进行了广泛的模拟，结果表明，所提出的机制在不同网络和渠道环境下的车辆效用、成本、完成延迟方面取得了显着的性能优势，这证明了这项工作的有效性和效率。