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首页> 外文期刊>International Journal of Applied Engineering Research >MAS based Resource Provisioning in Vehicular Cloud Network
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MAS based Resource Provisioning in Vehicular Cloud Network

机译:车载云网络中基于MAS的资源供应

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摘要

Modern day's vehicles are equipped with various features, like-storage space, sensors and computing capabilities. They have the flexibility to communicate each other as well as with Road-side unit with wireless transceiver. Therefore by the combination of such resources meaningfully will create a momentous influence on the domain of communication network. The underutilized vehicular resources can be rented to customers as a conventional cloud infrastructure. In this position paper, we propose a multi-agent system based information exchange in vehicular cloud network. The problem we address is that to select a useful solution from a number of information gathered from other vehicles as well as from RSU is intrinsically difficult. Our (proposed) solution is a three-layer architecture which integrates cooperative agents (responsive vehicles), autonomous agent (RSU) and learning agents, the idea being to select a smart solution by combining information from all three agents using multi-objective fitness function. NSGA II algorithm has been used as a multi-objective method.
机译:现代汽车具有各种功能,例如存储空间,传感器和计算功能。它们具有彼此之间以及与带有无线收发器的路边单元进行通信的灵活性。因此,通过这样的资源的组合将有意义地对通信网络的领域产生重大影响。未充分利用的车辆资源可以作为传统的云基础设施出租给客户。在这份立场文件中,我们提出了一种基于多智能体系统的车辆云网络中的信息交换。我们要解决的问题是,从其他车辆以及RSU收集的大量信息中选择一种有用的解决方案本质上是困难的。我们的(建议的)解决方案是一个三层体系结构,它将协作代理(响应性车辆),自治代理(RSU)和学习代理集成在一起,其思想是通过使用多目标适应度函数结合来自所有三个代理的信息来选择一个智能解决方案。 。 NSGA II算法已被用作多目标方法。

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