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无线Mesh网中QoS流量均衡策略研究
引用本文:周创明,于明秋,邢瑞康. 无线Mesh网中QoS流量均衡策略研究[J]. 计算机应用研究, 2019, 36(12)
作者姓名:周创明  于明秋  邢瑞康
作者单位:空军工程大学防空反导学院,西安710051;空军工程大学防空反导学院,西安710051;空军工程大学防空反导学院,西安710051
基金项目:国家自然科学基金青年科学基金资助项目
摘    要:由于现有的无线Mesh网路由研究多寻求当前时刻的最优路径,缺少流量均衡机制,影响了网络资源的使用效益。针对这一问题,提出基于QoS的流量均衡策略。通过研究蚁群算法,重新定义启发信息和信息素更新规则,将节点和链路负载加入到路径选择的权衡因素中;同时,考虑到Mesh节点到骨干网的通信,提出针对网关节点的流量均衡机制。改进算法以业务QoS要求作为约束条件,使路径规划倾向于负载度低的链路和节点,为后续业务的路由选择提供良好的网络环境。实验结果表明,随着数据量的增多,该算法始终可以保证业务QoS,实现了严格的QoS约束,并且能有效提高链路利用率,在路由质量方面优于中心节点法和改进信息素法。

关 键 词:无线Mesh网  服务质量  流量均衡  改进蚁群算法
收稿时间:2018-04-23
修稿时间:2018-07-19

Research on QoS flow equilibrium strategy of wireless Mesh network
Yu Mingqiu,Zhou Chuangming and Xing Ruikang. Research on QoS flow equilibrium strategy of wireless Mesh network[J]. Application Research of Computers, 2019, 36(12)
Authors:Yu Mingqiu  Zhou Chuangming  Xing Ruikang
Abstract:The existing research on QoS of wireless Mesh network always seeks the optimal path of the current moment and lacks traffic equilibrium scheme, which affects the use benefit of network resource. Concerning this issue, this paper proposed the traffic equilibrium strategy based on QoS. On the basis of ant colony algorithm, it redefined heuristic information and rules of pheromone updating, and added the load factor to weighing factor. At the same time, considering the communication between Mesh node and backbone network, it proposed the traffic equilibrium mechanism of gateway node. The improved algorithm takes QoS requiremented as the constraint condition, so that path planning tends to lower load link and node. The routing decision of subsequent business would go off in a good network environment. With the increase of data amount, experiments show this algorithm can guarantee QoS services all along, and implement strict QoS constraintly. By this algorithm, the link utilization is improved and quality of routing is better than central node algorithm and improved pheromone algorithm.
Keywords:wireless Mesh network   QoS(quality of service)   traffic equilibrium   improved ant colony optimization
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