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Optimizing Communications in Vehicular Ad hoc Networks Using Evolutionary Computation and Simulation

机译:使用进化计算和仿真优化车载自组织网络中的通信

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Broadcasting efficiently in a Vehicular Ad hoc Network (VANET) is a hard task to achieve. An efficient communication algorithm must take into account several aspects such as the neighboring density, the size and shape of the network, the use of the channel, the priority level of the message. Some studies [6,12,13] have proposed new solutions of broadcasting on such a network, but it is quite hard to evaluate their performance in various contexts. In order to determine the best repeating situation for each node in the network according to its environment, we developed a tool combining a network simulator (NS2) and an evolutionary algorithm. In this paper, we study four types of context and we tackle the best behavior for each node to determine the right input parameters. These studies are necessary to develop efficient broadcast algorithms in VANET.
机译:在车载自组织网络(VANET)中进行有效广播是一项艰巨的任务。高效的通信算法必须考虑多个方面,例如邻近密度,网络的大小和形状,信道的使用,消息的优先级。一些研究[6,12,13]提出了在这样的网络上广播的新解决方案,但是很难评估它们在各种情况下的性能。为了根据环境确定网络中每个节点的最佳重复情况,我们开发了一种结合了网络模拟器(NS2)和进化算法的工具。在本文中,我们研究了四种类型的上下文,我们针对每个节点处理最佳行为以确定正确的输入参数。这些研究对于在VANET中开发有效的广播算法是必需的。

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