无线监测网络优化QoM多信道机制研究

发布时间：2018-01-28 01:31

本文关键词： 无线监测网络 QoM 信道选择粒子群优化蒙特卡洛 TDMA时隙调度顶点着色网络冲突序列学习仿真软件　出处：《合肥工业大学》2016年博士论文　论文类型：学位论文

【摘要】：随着无线网络规模的扩大和应用的丰富,网络的性能保障、安全性和稳定性等面临越来越大的挑战。在无线网络中采用多个无线嗅探器(sniffer)实时收集用户传输的数据,可以实现无线网络的故障诊断和资源管理,对提升网络性能、保障网络安全、改善用户体验等具有重要意义。由于sniffer数量有限,因此如何优化各个sniffer的硬件配置和软件调度,使它们覆盖最多的网络用户,获取最多的网络数据,从而最大化网络监测质量(Quality of Monitoring, QoM)成为了当今的热点课题之一。该文全面总结了无线监测网络优化QOM的理论和技术现状,重点研究了优化sniffer的信道分配/选择以提高QOM的方法和算法,并通过理论推导、仿真与实际测试证明了所提出方法的有效性。本文的主要研究工作及创新之处在于：(1)总结了无线监测网络的概念和研究现状。总结了无线监测网络的定义、分类和系统框架等；讨论了无线监测网络优化QOM的方法,重点介绍了通过优化sniffer的信道分配/选择提高QOM的各种模型和方法,并从多种性能评价指标出发对现有方法进行了分析和比较；论述了无线监测网络优化QOM有待解决的关键问题,以及本文的研究思路和安排。(2)针对无线监测网络数据收集过程中的sniffer信道选择问题,提出了一种蒙特卡洛增强粒子群优化的sniffer信道选择算法。设计了二维映射粒子编码和相应的移动方案,并引入蒙特卡洛方法来修正解,使粒子群可以快速收敛到最优解或近似最优解。大量的仿真结果表明蒙特卡洛增强粒子群优化的信道选择算法明显优于现有的相关算法,可以使无线监测网络达到更高的监测质量QOM,算法具有更低的计算复杂度和更快的收敛性速度。实际测试结果也证明了该算法的有效性。(3)针对无线监测网络数据汇聚过程中的多信道TDMA时隙调度问题,提出了一种基于概率选择的分布式顶点着色算法。首先基于无线监测网络拓扑构建路由树,形成干扰图,从而将上述资源调度问题转化为干扰图的顶点二重着色问题,其目标是最小化网络通信冲突；然后根据目标函数,计算顶点选取颜色组合的概率,并按概率完成对信道和时隙的选择。在不同网络条件下的一系列对比仿真结果表明该算法可以有效减少网络冲突数,提高网络吞吐量,减小网络传输延时和调度长度,从而使无线监测网络达到较高的数据汇聚性能。(4)设计了一种基于序列学习的网络用户信息感知策略。作为信道选择算法应用的前提条件,网络的拓扑结构和用户信息(工作信道、通信概率或权值)必须是已知的。本文给出了一种基于序列学习的用户信息预测机制,有助于sniffer在全网信息收集操作的周期内准确掌握用户的工作信道和通信概率或权值,从而为信道选择算法提供重要依据。(5)设计了一种无线监测网络信道选择算法仿真软件。软件支持用户自定义网络场景以及节点用户二分图,集成和编译用户算法,测试算法执行效果,图形显示无线监测网络中监测节点的信道选择过程和结果,评价算法性能指标。基于该仿真软件,对该文所提出的算法和策略进行了有效性测试,得到了一系列仿真数据,进一步验证了所提出方法的综合有效性以及不足之处,并为下一步研究和相关科研工作奠定了基础。本文研究无线监测网络优化QOM的信道选择和资源调度机制,提出了相应的算法、策略和仿真平台,构建了较为完善的理论方法体系,相关成果对无线监测网络技术的发展具有推动作用和参考价值。
[Abstract]:With the expansion of network scale and rich applications, ensure the network performance, security and stability is facing increasing challenges. Using multiple wireless sniffer in wireless networks (sniffer) real-time collection of user data, can realize wireless network fault diagnosis and resource management, to enhance the performance of the network. To ensure network security, it is important to improve the user experience. Due to a limited number of sniffer, the configuration of hardware and software so how to optimize the scheduling of each sniffer, so that they cover the largest number of Internet users, access network data most, thereby maximizing the network quality monitoring (Quality of Monitoring, QoM) has become one of the hot topics today. This paper summarizes the current situation of the theory and technology of wireless monitoring network optimization QOM, channel allocation / focus on the optimization of sniffer method to select high QOM and The algorithm, and through theoretical derivation, the validity of the simulation and actual test results prove that the proposed method in this paper. The main research work and innovations are as follows: (1) summarizes the concepts and research status of wireless monitoring network. Summarizes the definition of wireless monitoring network, classification and system framework; discusses the method of wireless monitoring network optimization of QOM, introduced by sniffer / channel allocation optimization selection to improve various models and methods of QOM, and from a variety of performance evaluation of existing methods are analyzed and compared; the wireless monitoring network optimization QOM the key problems to be solved, and this research ideas and arrangements (2). For the problem of selecting sniffer channel wireless monitoring network data collection process, puts forward a method of Monte Carlo particle swarm optimization enhanced sniffer channel selection algorithm. Design a two-dimensional mapping Particle encoding and mobile solutions, and modify the solution into the Monte Carlo method, the particle swarm can quickly converge to the optimal solution or approximate optimal solution. The results of simulation show that the Monte Carlo enhanced particle swarm optimization algorithm related channel selection algorithm is obviously superior to the existing, can make the wireless monitoring network to monitor higher quality QOM that algorithm has lower complexity and faster convergence speed. The actual test results demonstrate the effectiveness of the algorithm. (3) the gap scheduling problem of multi channel TDMA wireless monitoring network data gathering process, this paper proposes a distributed vertex coloring algorithm based on probability based on the routing tree. The wireless monitoring network topology construction, the formation of an interference graph, thus the resource scheduling problem into the interference graph vertex coloring problem of double, the goal is to minimize network Communication conflict; then according to the objective function, selection probability calculation of vertex color combinations, and the completion of the channel and time slot selection according to the probability. A series of simulation results under different network conditions show that the proposed algorithm can effectively reduce the network conflict, improve the network throughput, reduce network transmission delay and scheduling length, so as to make the radio the monitoring network to achieve higher data convergence performance. (4) to design a strategy of network user's information perception based on sequence learning. As a prerequisite for the application of channel selection algorithm, topology and user information network (working channel, communication probability or weights) must be known. This paper presents a prediction mechanism of sequence learning based on user information, help sniffer in the cycle of the whole network of information collection in accurately grasp the user's work and communication channel probabilities or weights, so as to Channel selection algorithm provides an important basis. (5) the design of a wireless monitoring network channel selection algorithm. Simulation software software supports user-defined network scenarios and node users two figure, integrated and compiled user test algorithm, algorithm performance, graphical display of the process and results of channel wireless monitoring monitoring nodes in the network selection, evaluation algorithm performance. The simulation software based on the algorithm and the proposed strategy for the validity of the test, we obtained a series of simulation data, further verify the comprehensive effectiveness of the proposed method and shortcomings, and to lay a foundation for the next research and the related research work. This paper studies the wireless monitoring network optimization of QOM channel selection and resource scheduling mechanism, put forward the corresponding strategies and algorithms, simulation platform, constructs a theoretical system of relatively perfect method, relevant results of The development of wireless monitoring network technology has the role of promoting and referential value.

【学位授予单位】：合肥工业大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：TN92;TP391.9

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