基于蚁群的Ad hoc网络QoS多播路由算法研究

发布时间：2018-06-20 21:34

本文选题：Ad + Hoc　；参考：《中国石油大学(华东)》2014年硕士论文

【摘要】：Ad Hoc网络是一种临时的多跳的能够快速自动组网的通信网络。在这种网络中,所有的节点都是带有无线收发器的移动终端,节点之间全部通过带宽有限的无线链路进行通信。它不需要固定基础设施的支持,没有固定的拓扑结构,具有很强的鲁棒性和抗毁性。因此,它被广泛的应用在军事领域、自然灾害应急处理、科学考察、探险、交互式演讲、共享信息的商业会议、紧急通信等等社会领域。因为现有的基于传统固定网络的路由协议无法在其中取得满足实际需求的路由性能,所以研究能够均衡网络流量和节点的能量消耗的Ad Hoc网络Qos多播路由算法就成为了Ad Hoc网络技术研究的重要内容。蚁群优化算法(ACO)是由意大利学者Marco Dorigo在1991年受到蚂蚁觅食行为的启发而提出的一种基于群体智能的启发式搜索算法。蚁群算法具有很强的鲁棒性、自适应性、正反馈性和并行性,且不依赖于具体问题的数学描述,是一种解决NP-完全问题的有效方法。因为蚂蚁寻路的行为与Ad Hoc网络中数据分组的传输方式有着很多相似之处,所以蚁群算法为Ad Hoc网络路由协议的设计就提供了一个重要的参考。本文基于AntNet算法和Ad Hoc网络的特点,提出了一种基于AntNet算法且能够均衡网络能量状态的Ad Hoc网络OoS多播路由算法-AEQMR。该算法通过使用路径的能量状态和其他QoS参数来更新信息素表,这就使得蚂蚁的寻路行为均衡了网络节点的能耗和网络中数据的流向,并最终延长了整个网络的生存时间。在蚂蚁代理的数据结构中,利用改进的数据栈来记录蚂蚁代理在构建路径过程中形成的一段段子路径的能量状态和其他QoS参数情况。在节点上,利用改进的网络状态统计模型和信息素更新规则并结合蚂蚁代理所收集的网络信息来更新节点信息素矩阵。数据包根据每个中间节点上的与当前网络状态相关的信息矩阵来选择下一个要到达的节点。因此,数据包在到达目的节点的过程中,每次选路都基于当前最优的路径。在相同的场景下,通过OMNET++仿真软件,本文对该算法和其他的Ad Hoc网络路由协议分别进行了仿真实验,并分析比较了实验结果。仿真数据说明了该算法在缩短时延和均衡网络能量状态分布上具有优良的性能。
[Abstract]:Ad Hoc network is a kind of temporary multi-hop communication network which can be quickly and automatically organized. In this network, all nodes are mobile terminals with wireless transceivers, and all nodes communicate via wireless links with limited bandwidth. It does not need the support of fixed infrastructure, has no fixed topology, and has strong robustness and survivability. Therefore, it is widely used in the military field, natural disaster emergency response, scientific exploration, interactive lectures, information sharing business meetings, emergency communications and other social fields. Because the existing routing protocols based on traditional fixed networks cannot achieve routing performance that meets the actual requirements, Therefore, the research of QoS multicast routing algorithm for Ad Hoc networks, which can balance network traffic and energy consumption of nodes, has become an important part of Ad Hoc network technology. Ant colony optimization (ACO) is a heuristic search algorithm based on swarm intelligence proposed by Italian scholar Marco Dorigo in 1991 inspired by ant foraging behavior. Ant colony algorithm has strong robustness, adaptability, positive feedback and parallelism, and does not depend on the mathematical description of specific problems. It is an effective method to solve NP-complete problems. Because the behavior of ant routing is similar to the transmission of data packets in Ad Hoc networks, ant colony algorithm provides an important reference for the design of routing protocols in Ad Hoc networks. Based on the characteristics of AntNet algorithm and Ad Hoc network, this paper presents an OoS multicast routing algorithm based on AntNet algorithm, which can balance the energy state of Ad Hoc network. The algorithm updates the pheromone table by using the energy state of the path and other QoS parameters, which makes the routing behavior of ants equalize the energy consumption of the network node and the flow of data in the network, and finally prolong the lifetime of the whole network. In the data structure of ant agent, the improved data stack is used to record the energy state and other QoS parameters of a segment of sub-path formed by ant agent in the course of constructing path. The node pheromone matrix is updated by using the improved network state statistics model and pheromone updating rules and the network information collected by ant agents. The packet selects the next node to be reached based on the information matrix on each intermediate node related to the current network state. Therefore, in the process of reaching the destination node, each routing is based on the current optimal path. In the same scenario, the algorithm and other Ad Hoc network routing protocols are simulated by OMNET simulation software, and the experimental results are analyzed and compared. The simulation results show that the algorithm has good performance in reducing delay and equalizing network energy state distribution.
【学位授予单位】：中国石油大学(华东)
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN929.5;TP18

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