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Classification of node degree based on deep learning and routing method applied for virtual route assignment

机译:基于深度学习和路由方法的节点度分类在虚拟路由分配中的应用

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摘要

In recent years, the importance of various wireless network technologies has increased. Specifically, in communication environments noted for severe conditions, such as disasters, war, and terrorism, collaboration between fixed communication infrastructure and wireless ad-hoc networks is indispensable. In this paper, the node degree of wireless communication is classified for disaster situations, and virtual routes are set according to the predetermined node degree. Then, the proposed routing method is employed with base stations as the infrastructure, such that a route may be assigned, maintained, and recovered. Our classification of wireless degree nodes uses deep learning, and virtual routes are created by employing the Viterbi algorithm. The proposed routing method is compared with existing methods (AODV, OLSR, and ZRP) from the viewpoint of route discovery times and reachability via simulations. (C) 2016 Elsevier B.V. All rights reserved.
机译:近年来,各种无线网络技术的重要性已经增加。具体而言,在注意到严重条件(例如灾难,战争和恐怖主义)的通信环境中,固定通信基础架构和无线自组织网络之间的协作是必不可少的。在本文中,针对灾难情况对无线通信的节点等级进行分类,并根据预定的节点等级设置虚拟路由。然后,将所提出的路由方法与基站一起用作基础设施,从而可以分配,维护和恢复路由。我们对无线学位节点的分类使用深度学习,并且通过采用Viterbi算法创建虚拟路线。从路由发现时间和通过仿真的可达性的角度出发,将所提出的路由方法与现有方法(AODV,OLSR和ZRP)进行比较。 (C)2016 Elsevier B.V.保留所有权利。

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