A Contract-Stackelberg Offloading Incentive Mechanism for Vehicular Parked-Edge Computing Networks
A Contract-Stackelberg Offloading Incentive Mechanism for Vehicular Parked-Edge Computing Networks
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DOI：
10.1109/vtcspring.2019.8746454
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发表时间：
2019-04
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影响因子：
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通讯作者：
Yuwei Li;Bo Yang;Zhijie Chen;Cailian Chen;X. Guan
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文献类型：
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作者：
Yuwei Li;Bo Yang;Zhijie Chen;Cailian Chen;X. Guan
随着智能车辆和计算密集型车辆应用的发展，在本地计算资源稀缺的情况下保持车辆的高性能成为了挑战。移动边缘计算 (MEC) 是一种通过将计算密集型任务卸载到 MEC 服务器来改善车辆服务的巨大潜力的计算范例。然而，由于MEC服务器的计算资源有限，应该利用具有丰富闲置计算资源的停车场（PL）。我们引入了一种新的计算范式，命名为车载边缘计算（VPEC）。我们制定了一个三阶段的合约-Stackelberg 卸载激励机制来描述这个问题。根据闲置计算资源将PL分为不同类型，停车场代理（PLA）为不同类型的PL提供不同的合约。最优问题旨在最大化车辆、操作员和 PLA 的效用。我们使用逆向归纳法来解决这个三阶段问题，并给出每个阶段最优策略的封闭式表达式。仿真结果验证了所提出的激励机制的可行性，并揭示了交通密度变化时各阶段最优策略的变化趋势。
With the development of smart vehicles and computation-intensive vehicular applications, it is a challenge to maintain high performance for vehicles with scarce local computational resources. Mobile Edge Computing (MEC) is a computing paradigm with high potential to improve vehicular services by offloading computation-intensive tasks to the MEC servers. However, as the computational resources of MEC servers are limited, parking lots (PLs) having abundant idle computational resources should be utilized. We introduce a new computing paradigm, named by Vehicular Parked-Edge Computing (VPEC). We formulate a three-stage contract-stackelberg offloading incentive mechanism to describe this problem. The PLs are classified into different types according to their idle computational resources, and parking lot agent (PLA) offers different contracts to different types of PLs. The optimal problem is designed to maximize the utilities of vehicles, operator and PLA. We use backward induction method to solve this three-stage problem, and give the closed-form expressions of the optimal strategies for each stage. Simulation results demonstrate the feasibility of the proposed incentive mechanism and reveal the changing trend of optimal strategies in each stage when traffic density changes.