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Development and Simulation of Artificial Neural Network based decision on parametric values for Performance Optimization of Reactive Routing Protocol for MANET using Qualnet

机译:使用Qualnet使用Prynet的无功路由协议性能优化性能优化的基于人工神经网络决策的开发与仿真

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Collection of wireless nodes like mobile, laptops, palmtops etc. can creates a dynamic wireless network without using any existing infrastructure known as Mobile Ad-Hoc Network (MANET). MANET performs peer to peer interactions among the wireless nodes participating in the network. Each node in the network must be able to function as a router as well to relay the packets generated by other nodes. Due to the mobility of nodes network topology changes frequently and the topology changes information must update every time to all the nodes in the network. Limited bandwidth and transmission characteristic impose administrative and control information to update the dynamic charge in the network topology, due to the mobility of the wireless nodes. Information update at some fixed time interval can cause unnecessary traffic in the wireless network so to make adaptive the time interval of these messages is a technique to improve the performance of the network. In this paper a soft computing technique Artificial Neural Network based reactive AODV routing protocol is proposed to determine the frequency of hello interval to improve the performance of the network.
机译:无线节点的集合,如移动,笔记本电脑,掌上型等,可以在不使用称为移动ad-hoc网络(MANET)的现有基础设施的情况下创建动态无线网络。 MANET在参与网络的无线节点之间执行对等相互作用。网络中的每个节点必须能够用作路由器,也能够继电由其他节点生成的数据包。由于节点网络拓扑的移动性频繁变化,并且拓扑更改信息必须每次到网络中的所有节点都更新。由于无线节点的移动性,有限的带宽和传输特性施加管理和控制信息以更新网络拓扑中的动态电荷。在一些固定时间间隔的信息更新可能导致无线网络中的不必要流量,以便使自适应这些消息的时间间隔是提高网络性能的技术。本文提出了一种软计算技术基于人工神经网络的基于反应性AODV路由协议,以确定Hello间隔的频率,以提高网络性能。

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