论文标题
使用DAG-Blockchains在车辆中收费的双重拍卖
A Double Auction for Charging Scheduling among Vehicles Using DAG-Blockchains
论文作者
论文摘要
电动汽车(EV)在我们的日常生活中变得越来越受欢迎,这取代了传统的燃料汽车,以减少碳排放并保护环境。电动汽车需要收取费用,但是充电站(CS)的充电桩数量有限,并且充电通常比加油更耗时。根据这种情况,我们提出了一个基于定向的无环图(DAG) - 窗口链和双重拍卖机制的安全有效的调度系统。在智能区域,它试图根据提交的收费请求和状态信息将电动汽车分配给可用的CSS。首先,我们设计了一个轻巧的充电调度框架,该框架集成了DAG-Blockchain和Modern Gryptography Technology,以确保在执行调度和完成交易过程中的安全性和可扩展性。在此过程中,由于CS中充电资源的有限,该过程被制定了有限的多项性双重拍卖问题,该资源激励该领域的EVS和CSS根据其偏好和地位参与市场。由于这种限制,与现有的双重拍卖模型相比,我们的问题更加复杂,更难实现真实性和系统效率。为了适应它,我们提出了两种算法,即充电(TMC)的真实机制和充电的有效机制(EMC),以确定EVS和CSS和定价策略之间的分配。然后,理论分析和数值模拟都均显示了我们提出的算法的正确性和有效性。
Electric Vehicles (EVs) are becoming more and more popular in our daily life, which replaces traditional fuel vehicles to reduce carbon emissions and protect the environment. EVs need to be charged, but the number of charging piles in a Charging Station (CS) is limited and charging is usually more time-consuming than fueling. According to this scenario, we propose a secure and efficient charging scheduling system based on a Directed Acyclic Graph (DAG)-blockchain and double auction mechanism. In a smart area, it attempts to assign EVs to the available CSs in the light of their submitted charging requests and status information. First, we design a lightweight charging scheduling framework that integrates DAG-blockchain and modern cryptography technology to ensure security and scalability during performing scheduling and completing tradings. In this process, a constrained multi-item double auction problem is formulated because of the limited charging resources in a CS, which motivates EVs and CSs in this area to participate in the market based on their preferences and statuses. Due to this constraint, our problem is more complicated and harder to achieve truthfulness as well as system efficiency compared to the existing double auction model. To adapt to it, we propose two algorithms, namely Truthful Mechanism for Charging (TMC) and Efficient Mechanism for Charging (EMC), to determine an assignment between EVs and CSs and pricing strategies. Then, both theoretical analysis and numerical simulations show the correctness and effectiveness of our proposed algorithms.