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基于神经网络的无线Mesh 网络混合路由协议。

A neural networks-based hybrid routing protocol for wireless mesh networks.

机构信息

Digital Image Processing, Telemedicine and Multimedia Laboratory, Faculty of Electrical Engineering, University of Belgrade, Belgrade 11000, Serbia.

出版信息

Sensors (Basel). 2012;12(6):7548-75. doi: 10.3390/s120607548. Epub 2012 Jun 7.

DOI:10.3390/s120607548
PMID:22969360
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3435989/
Abstract

The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic-i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.

摘要

无线网格网络（WMN）的网络基础设施是分散式和相对简单的，但它们可以在具有良好冗余性的同时表现出可靠的运行性能。WMN 为固定和移动无线设备提供互联网接入。在城市和农村地区，它们都为用户在特定覆盖范围内提供高带宽网络。影响这些网络的主要问题是网络拓扑和链路质量的变化。为了提供常规的运行，路由协议对 WMN 的实现有主要影响。在本文中，我们提出了一种新的 WMN 路由协议，它基于主动和反应式路由协议的良好结果，因此可以将其归类为混合路由协议。所提出的解决方案应避免洪泛和创建新的路由度量。我们建议使用人工逻辑，即神经网络（NN）。该协议基于由 Hopfield 神经网络控制的移动代理技术。此外，我们的新路由度量基于多准则优化，以最小化延迟和阻塞概率（拒绝的数据包或它们的重传）。路由协议观察真实的网络参数和真实的网络环境。由于人工智能逻辑，所提出的路由协议应最大限度地利用网络资源并优化网络性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/3d380f144a48/sensors-12-07548f14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/a36c6cff95ab/sensors-12-07548f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/7a44e5fd19d6/sensors-12-07548f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/ed5376d5e9b5/sensors-12-07548f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/33384c037447/sensors-12-07548f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/b9a14c5e2ad3/sensors-12-07548f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/35ea535dc281/sensors-12-07548f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/b7052e96e5fd/sensors-12-07548f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/3f1ba42bdd8e/sensors-12-07548f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/c55d3fcc7737/sensors-12-07548f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/3d380f144a48/sensors-12-07548f14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/a36c6cff95ab/sensors-12-07548f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/7a44e5fd19d6/sensors-12-07548f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/ed5376d5e9b5/sensors-12-07548f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/33384c037447/sensors-12-07548f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/b9a14c5e2ad3/sensors-12-07548f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/35ea535dc281/sensors-12-07548f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/b7052e96e5fd/sensors-12-07548f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/3f1ba42bdd8e/sensors-12-07548f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/c55d3fcc7737/sensors-12-07548f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9547/3435989/3d380f144a48/sensors-12-07548f14.jpg

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