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首页> 外文期刊>International journal of communication systems >An improved distance-based ant colony optimization routing for vehicular ad hoc networks
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An improved distance-based ant colony optimization routing for vehicular ad hoc networks

机译:用于车辆临时网络的改进的距离基蚁群优化路由

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摘要

Vehicular ad hoc network (VANET) has earned tremendous attraction in the recent period due to its usage in a wireless intelligent transportation system. VANET is a unique form of mobile ad hoc network (MANET). Routing issues such as high mobility of nodes, frequent path breaks, the blind broadcasting of messages, and bandwidth constraints in VANET increase communication cost, frequent path failure, and overhead and decrease efficiency in routing, and shortest path in routing provides solutions to overcome all these problems. Finding the shortest path between source and destination in the VANET road scenario is a challenging task. Long path increases network overhead, communication cost, and frequent path failure and decreases routing efficiency. To increase efficiency in routing a novel, improved distance-based ant colony optimization routing (IDBACOR) is proposed. The proposed IDBACOR determines intervehicular distance, and it is triggered by modified ant colony optimization (modified ACO). The modified ACO method is a metaheuristic approach, motivated by the natural behavior of ants. The simulation result indicates that the overall performance of our proposed scheme is better than ant colony optimization (ACO), opposition-based ant colony optimization (OACO), and greedy routing with ant colony optimization (GRACO) in terms of throughput, average communication cost, average propagation delay, average routing overhead, and average packet delivery ratio.
机译:由于其在无线智能运输系统中的使用,因此车辆临时网络(VANET)在最近的时间内获得了巨大的吸引力。 VANET是一种独特的移动临时网络(MANET)。路由问题如节点的高迁移率,频繁路径中断,消息的盲目广播和VANET中的带宽约束增加了通信成本,频繁路径故障和开销,并降低路由中的效率,路由中最短路径提供了克服所有问题这些问题。寻找Vanet Road场景中的源和目的地之间的最短路径是一个具有挑战性的任务。长路径增加网络开销,通信成本和频繁路径故障,降低路由效率。为了提高路由新颖的效率,提出了改进的基于距离的蚁群优化路由(IDBacor)。所提出的idbacor确定了间距离,它是通过改进的蚁群优化(修改ACO)触发的。修改的ACO方法是一种成群质方法,受到蚂蚁的自然行为的动机。仿真结果表明,我们提出的方案的整体性能优于蚁群优化(ACO),基于反对派的蚁群优化(OACO),以及在吞吐量方面与蚁群优化(Graco)的贪婪路由,平均通信成本,平均传播延迟,平均路由开销和平均分组传递比率。

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