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An improved distance‐based ant colony optimization routing for vehicular ad hoc networks
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2020-06-22 , DOI: 10.1002/dac.4502
Raghu Ramamoorthy 1 , Menakadevi Thangavelu 2
Affiliation  

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.

中文翻译:

改进的基于距离的车辆自组织网络蚁群优化路由

车载自组织网络(VANET)由于在无线智能交通系统中的使用而赢得了极大的吸引力。VANET是移动自组织网络(MANET)的一种独特形式。路由问题,例如节点的高移动性,频繁的路径中断,消息的盲目广播以及VANET中的带宽限制,增加了通信成本,频繁的路径故障和开销,降低了路由效率,而路由中的最短路径提供了解决所有问题的解决方案这些问题。在VANET道路情景中,找到源与目的地之间的最短路径是一项艰巨的任务。长路径增加了网络开销,通信成本和频繁的路径故障,并降低了路由效率。为了提高小说的编排效率,提出了一种改进的基于距离的蚁群优化路由(IDBACOR)。提出的IDBACOR确定了车辆之间的距离,并由修改后的蚁群优化(修改后的ACO)触发。改进的ACO方法是一种基于蚂蚁自然行为的元启发式方法。仿真结果表明,在吞吐量,平均通信成本方面,我们提出的方案的总体性能优于蚁群优化(ACO),基于对立的蚁群优化(OACO)和贪婪路由和蚁群优化(GRACO)。 ,平均传播延迟,平均路由开销和平均分组传输率。改进的ACO方法是一种基于蚂蚁自然行为的元启发式方法。仿真结果表明,在吞吐量,平均通信成本方面,我们提出的方案的总体性能优于蚁群优化(ACO),基于对立的蚁群优化(OACO)和贪婪路由和蚁群优化(GRACO)。 ,平均传播延迟,平均路由开销和平均分组传输率。改进的ACO方法是一种基于蚂蚁自然行为的元启发式方法。仿真结果表明,在吞吐量,平均通信成本方面,我们提出的方案的总体性能优于蚁群优化(ACO),基于对立的蚁群优化(OACO)和贪婪路由和蚁群优化(GRACO)。 ,平均传播延迟,平均路由开销和平均分组传输率。
更新日期:2020-06-22
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