本文選題：空基網 + 路由算法��； 參考：《哈爾濱工業(yè)大學》2017年碩士論文

【摘要】：隨著航空航天和空間探索領域的快速發(fā)展,空間信息的影響力變得越來越高,對空間信息的掌握程度,從某種程度上會影響一個國家綜合國力的提升和社會經濟的發(fā)展�？仗斓匾惑w化網絡由深空網絡、近地空間網絡、地面網絡構成,其中近地空間部分由飛機、直升機、戰(zhàn)斗機、無人機等低空飛行器構成,也被稱為空基網�？栈W中的各種飛行器節(jié)點都具有實時移動性,且節(jié)點運動速度很高,導致網絡拓撲結構頻繁變化,因此,空基網的路由技術面臨著很大的問題,包括路由有效時間短、通信路由頻繁斷開、數據包傳輸時延長等。要采取適應空基網特征的路由算法,要求路由算法收斂速度快,路由建立時間短,平均路由跳數小,路徑優(yōu)化速度快。本文主要研究空基網飛行器之間基于智能優(yōu)化算法的路由算法。首先,針對空基網節(jié)點移動性高、拓撲變化頻繁的特點,將蟻群算法、粒子群算法、遺傳算法應用到網絡中,建立智能優(yōu)化路由算法。將三種智能優(yōu)化路由算法與AODV協(xié)議仿真對比,結果表明蟻群優(yōu)化路由算法在路由建立成功率、平均路由跳數性能上表現最好,粒子群優(yōu)化路由算法和遺傳優(yōu)化路由算法次之,而AODV協(xié)議最差。接著,考慮網絡的簇頭節(jié)點不再位于網絡中心位置或者失效、毀壞、突然退出網絡的場景,設計基于圖論的簇頭節(jié)點選擇和更新算法,并使用概率圖模型知識對其進行改進,以得到更加符合空基網場景的結果,提出基于概率圖模型的簇頭節(jié)點選擇和更新算法。分析表明基于概率圖模型的簇頭節(jié)點選擇和更新算法盡管比基于圖論的方法算法復雜度略高,但是其簇頭與網絡內節(jié)點的平均路由跳數更少。最后,對有新節(jié)點加入網絡的情形,利用概率圖模型理論,綜合考慮遲入節(jié)點與其鄰接點的相互關系,為遲入節(jié)點選擇最佳的接入節(jié)點。仿真結果表明,該方法選擇的接入節(jié)點與按照距離最近原則選出的接入節(jié)點有所不同。
[Abstract]:With the rapid development of aerospace and space exploration, the influence of space information becomes more and more high. To some extent, the mastery of space information will affect the promotion of a country's comprehensive national strength and the development of social economy. The integrated network is composed of deep space network, near-earth space network and ground network, in which the near-Earth space is composed of aircraft, helicopters, fighter planes, drones and other low-altitude aircraft, also known as space-based network. All kinds of aircraft nodes in space-based networks have real-time mobility, and the speed of node movement is very high, which leads to frequent changes in network topology. Therefore, the routing technology of space-based networks faces great problems, including the short effective time of routing. The communication route frequently disconnects, the packet transmission time lengthens and so on. In order to adopt the routing algorithm which adapts to the characteristics of space-based network, it is necessary for the routing algorithm to converge fast, to set up the route in a short time, to reduce the average number of hops, and to optimize the route quickly. In this paper, the routing algorithm based on intelligent optimization algorithm is studied. Firstly, the ant colony algorithm, particle swarm optimization algorithm and genetic algorithm are applied to the network to establish an intelligent optimal routing algorithm. The simulation results show that the ant colony optimization routing algorithm has the best performance on the average number of hops, the particle swarm optimization routing algorithm and the genetic optimization routing algorithm are the second, compared with the simulation of AODV protocol, the ant colony optimization routing algorithm has the best performance in routing establishment, and the performance of the average number of hops is the best. AODV protocol is the worst. Then, considering the cluster head node is no longer located in the center of the network or failure, destroy, suddenly quit the network scene, design a graph based cluster head node selection and update algorithm, and use the probability graph model knowledge to improve it. Based on probability graph model, a cluster head node selection and update algorithm is proposed in order to obtain the results that are more consistent with the space-based network scenario. The analysis shows that the clustering head selection and updating algorithm based on probabilistic graph model is a little more complex than the one based on graph theory, but the average number of routing hops between cluster heads and nodes in the network is less. Finally, in the case of adding new nodes to the network, the theory of probabilistic graph model is used to comprehensively consider the relationship between late entry nodes and their adjacent points to select the best access nodes for late entry nodes. The simulation results show that the access nodes selected by this method are different from those selected according to the principle of distance nearest.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：V443.1;TP18;TN915.0

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本文編號：1776931