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A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks

机译:利用模拟退火算法和神经网络的车辆自组织网络路由协议

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Vehicular ad hoc network (VANET) is special type of mobile ad hoc networks which establish communications between adjacent vehicles and also between vehicles and roadside units. Thanks to their dynamic and fast topology changes, inter-vehicular ad hoc networks are like dynamic networks without organizations. Hence, developing a reliable routing algorithm is regarded as a notable challenge in these networks. In this paper, a clustering-based reliable routing algorithm was proposed for VANETs with reliable applications. In this way, simulated annealing was used for appropriate clustering of nodes and the parameters of node degree, coverage and ability were considered in the proposed method. For selecting cluster head, radial basis function neural network was used and a suitable fitness function with velocity and free buffer size parameters was used. Each cluster has two gateway nodes which are used as the communication interface for transmitting data from one cluster to another cluster. The simulation results indicated the efficiency of the proposed method in terms of route discovery rate and packet delivery rate.
机译:车载自组织网络(VANET)是一种特殊类型的移动自组织网络,可在相邻车辆之间以及车辆与路边单元之间建立通信。由于其动态和快速的拓扑变化,车辆间的自组织网络就像没有组织的动态网络。因此,在这些网络中,开发可靠的路由算法被视为一项重大挑战。本文针对具有可靠应用的VANET,提出了一种基于聚类的可靠路由算法。通过这种方式,将模拟退火算法用于节点的适当聚类,并在该方法中考虑了节点度,覆盖度和能力的参数。为了选择簇头,使用了径向基函数神经网络,并使用了具有速度和自由缓冲区大小参数的合适适应度函数。每个群集具有两个网关节点,这些网关节点用作将数据从一个群集传输到另一群集的通信接口。仿真结果表明了该方法在路由发现率和分组传输率方面的有效性。

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