The Dynamical Prediction of V2V Link Duration in Urban VANETs
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摘要: 链路时延是决定车联网(vehicular ad hoc networks, VANETs)许多网络性能的重要标准.现存的VANETs基于节点移动性解决链路时延的问题，但是都没有预测的功能，不适合实际VANETs中动态预测车-车(vehicle to vehicle, V2V)链路时延.提出动态预测任意2车链路时延的数学模型DPLD，考虑2车相对速度分布、相对距离变化、交通密度和城市场景中交通灯因素对2车之间链路时延的影响，因为这些因素在链路连接过程中是变化的.通过考虑相对速度的分布，模型能够实时地调整原则自适应车速变化.通过自动调整2车之间相对距离计算方法，DPLD模型能够自适应2车间相对距离的变化.因此该模型能够有效地预测预期要发生的2车之间的链路时延.这个模型实现取决于相对速度分布参数的估计方法、指数移动平均法对车速异常处理以及交通灯对链路时延影响的概率建模并且详细给出2车遇到不同交通灯的具体链路时延预测方法.仿真结果表明:DPLD模型预测的城市环境的2车之间链路时延准确性很高.Abstract: Link duration prediction is an important standard which determines many performance of network in VANETs. Existing analytical methods about link duration based on mobility of nodes in VANETs have no function to predict link duration between any two nodes in the future, so it is not practical for these methods to predict link duration between two vehicles. We propose a dynamical prediction model which considers the distribution of relative velocity, inter-vehicle distance, traffic density change and traffic light to estimate the expected link duration between any pair of connected vehicles, because these factors change continuously in the process of link connection. By taking into account the relative velocity distribution, the model is able to adjust the principle in real time to adapt variation of vehicle speed. By automatically adjusting computing method of the relative distance between two vehicles, DPLD(dynamically predict link duration) model can automatically adapt to the change of relative distance between two vehicles. Therefore, DPLD model can effectively predict the link duration between the two vehicles. Such model is implemented on each vehicle along with parameters estimation methods of relative velocity distribution, exponential moving average method processes speed exception and considering the impact of the traffic light on link duration. Simulation results show that this model predict link duration for urban scenario has the high accuracy.
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