MOICA在优化无线监测网络OoM中的应用

发布时间：2018-11-02 17:43

【摘要】：在无线监测网络中,通常将监测节点分布在监测区域内用于监测用户的活动信息,进行故障诊断、资源管理和关键路径分析。由于硬件等资源限制,每一时间段内节点只能工作于其中一条信道上并收集所在信道上的用户活动信息。考虑到无线监测网络的多信道特性,为监测节点设计出合理的信道占有方案,将有助于获得最大的网络监测信息。如何设计有效算法来优化节点的信道选择以实现监测信息收集的最大化,从而实现无线监测网络的监测质量(QoM1)最优是课题研究的关键。针对现有解决方案存在的不足,本文设计出一种实用性强、解的质量较高的节点信道选择算法来优化无线监测网络QOM,研究工作具有重要的理论意义和应用价值。本文主要研究内容及创新之处如下：(1)提出了基于多基因位量子免疫克隆算法的无线监测节点(sniffer)信道选择算法一一MQICA2。本文在充分考虑到无线监测系统的信道特征及监测节点信道选择的特殊性后,以组合优化算法中并行性好、处理速度快的量子免疫克隆算法(QICA3)为基础,融入了等位基因和双向高斯变异等方法,提出了基于等位基因同高斯变异进化相结合的多基因位量子免疫克隆信道选择算法。该算法既是对传统QICA的有益拓展,也为优化无线监测网络QOM提供了新手段。(2)给出了MQICA算法一致收敛性的理论证明及实验验证。文章首先通过严谨的数学推导,在理论方面证明了MQICA算法的有效性,并通过大量仿真、对比试验,验证了MQICA算法比其他QoM优化算法具有更强的寻优能力、更快的收敛速度,能够在有限的时间内使无线监测网络QOM迅速达到最优或近似最优解。
[Abstract]:In wireless monitoring networks, monitoring nodes are usually distributed in monitoring areas for monitoring user activity information, fault diagnosis, resource management and critical path analysis. Due to resource constraints such as hardware, nodes can only work on one of the channels in each time period and collect user activity information on the channel. Considering the multi-channel characteristics of wireless monitoring network, it will be helpful to obtain the maximum network monitoring information by designing a reasonable channel possession scheme for monitoring nodes. How to design an effective algorithm to optimize the channel selection of nodes in order to maximize the monitoring information collection and achieve the optimal monitoring quality (QoM1) of wireless monitoring network is the key of the research. In view of the shortcomings of the existing solutions, it is of great theoretical significance and practical value to design a node channel selection algorithm with strong practicability and high quality solution to optimize the research work of wireless monitoring network QOM,. The main contents and innovations of this thesis are as follows: (1) A novel (sniffer) channel selection algorithm, MQICA2., is proposed for wireless monitoring nodes based on multi-gene quantum immune clone algorithm. After fully considering the channel characteristics of wireless monitoring system and the particularity of channel selection of monitoring nodes, this paper bases on the quantum immune clone algorithm (QICA3), which has good parallelism and high processing speed in combinatorial optimization algorithm. Based on allelic and bidirectional Gao Si mutation, a multi-gene quantum immune clone channel selection algorithm is proposed based on the combination of allelic and Gao Si mutation evolution. This algorithm not only extends the traditional QICA, but also provides a new method for optimizing the wireless monitoring network QOM. (2) the theoretical proof and experimental verification of the uniform convergence of the MQICA algorithm are given. In this paper, the validity of MQICA algorithm is proved in theory through rigorous mathematical derivation. Through a large number of simulations and comparative experiments, it is proved that MQICA algorithm has stronger optimization ability and faster convergence speed than other QoM optimization algorithms. The wireless monitoring network QOM can reach the optimal or approximate optimal solution quickly in a limited time.
【学位授予单位】：合肥工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN98

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