基于流量感知的动态自适应协同能效优化

发布时间：2018-03-04 08:07

本文选题：移动多跳绿色网络　切入点：能效优化　出处：《东北大学》2014年硕士论文　论文类型：学位论文

【摘要】：移动多跳绿色网络是计算机网络和移动通信结合而产生的新型网络,其无需任何基础设施就可以实现通信,由于其具有移动性、高传输速率、易于扩展性等优点,因此移动多跳绿色网络具有非常广泛且乐观的应用前景。然而,目前移动多跳绿色网络中的节点只能利用携带电池作为其能量来源,一旦电池耗电完毕,节点就无法正常工作。近年来,我国都在践行可持续发展策略,为此节能减排已成为通信行业研究的焦点和热点。并且随着互联网的飞速发展,使得现阶段无线网络流量呈现出了动态性、波动性、时变性、随机性、突发性和分形特性等特点。因此,研究基于流量感知的动态自适应协同能效优化策略具有非常重要的战略意义和研究价值。本文的主要工作如下：首先,本文研究了基于链路流量感知的自适应协同能效优化策略,建立了链路级的能效优化机制。由于链路网络流量具有自相似性、长相关性、突发性和分形特性等特性,本文首先基于多时间序列分析理论,构建了移动多跳网络的链路级流量感知模型；同时根据该流量感知模型和节点的位置信息,通过减少高能耗的节点选取,本文提出了基于链路流量感知的最大能效单播路由算法(LTMUR),建立了源到目的节点间的能效最大路径；本文由提出了利用修改ACK响应帧的方法,构建了链路自适应协同休眠算法(LACS);同时利用SNR能够反映信道状态,本文提出了基于接收端的SNR的链路速率自适应调整算法(LRAA)。本文,通过这一系列的能效优化机制,降低了网络能耗,提高了网络能效,达到了链路级的能效优化的目的。然后,本文研究了基于端到端流量感知的自适应协同能效优化策略,建立了网络级的能效优化机制。本文首先通过逐级配对的方法建立了多个源节点到目的节点间的能耗最小路径,建立了多源单目的能耗最小路由算法(MMSR);然后,基于流量矩阵的概念,同时结合端到端网络流量的特性,本文基于压缩感知理论构建了端到端流量感知模型；同时,本文通过数据特征信息提取的方法,将待传输数据流量进行有效地压缩,构建了基于端到端流量感知的数据融合机制算法(ETDFM)。本文,通过这一系列的有效节能机制,降低了网络能耗,提高网络能效,达到了网络级的能效优化的目的。通过仿真实验比较分析,本文提出的两个算法在降低网络能耗,提高网络能效方面均体现出了其优越性。
[Abstract]:Mobile multi-hop green network is a new type of network produced by the combination of computer network and mobile communication. It can realize communication without any infrastructure. Because of its advantages of mobility, high transmission rate, easy expansibility and so on, mobile multi-hop green network can realize communication without any infrastructure. Therefore, mobile multi-hop green network has a very broad and optimistic application prospect. However, at present, the nodes in mobile multi-hop green network can only use the carrying battery as their energy source. In recent years, our country is practicing the strategy of sustainable development, so energy saving and emission reduction has become the focus and hotspot of the communication industry. And with the rapid development of the Internet, This makes the wireless network traffic present dynamic, fluctuating, time-varying, random, sudden and fractal characteristics. The research of dynamic adaptive cooperative energy efficiency optimization strategy based on traffic awareness has very important strategic significance and research value. The main work of this paper is as follows: first, In this paper, the adaptive cooperative energy efficiency optimization strategy based on link traffic perception is studied, and a link level energy efficiency optimization mechanism is established. Firstly, based on the theory of multi-time series analysis, the link level traffic perception model of mobile multi-hop network is constructed. According to the traffic perception model and node location information, the node selection with high energy consumption is reduced. In this paper, the maximum energy efficiency unicast routing algorithm based on link traffic awareness is proposed, and the maximum energy efficiency path between source and destination node is established. The link adaptive cooperative sleep algorithm is constructed, and the channel state can be reflected by using SNR. In this paper, a link rate adaptive adjustment algorithm based on receiver SNR is proposed. It reduces the network energy consumption, improves the network energy efficiency, and achieves the goal of link level energy efficiency optimization. Then, this paper studies the adaptive cooperative energy efficiency optimization strategy based on end-to-end traffic perception. The energy efficiency optimization mechanism at network level is established. Firstly, the minimum path of energy consumption between multiple source nodes and destination nodes is established by stepwise pairing method, and the minimum energy consumption routing algorithm of multi-source, single-purpose energy consumption is established. Based on the concept of traffic matrix and the characteristics of end-to-end network traffic, this paper constructs an end-to-end traffic perception model based on the theory of compression perception. The data flow to be transmitted is compressed effectively, and an algorithm of data fusion based on end-to-end traffic perception is constructed. In this paper, through this series of effective energy-saving mechanisms, the network energy consumption is reduced, and the network energy efficiency is improved. Through the comparison and analysis of simulation experiments, the two algorithms presented in this paper have shown their advantages in reducing network energy consumption and improving network energy efficiency.
【学位授予单位】：东北大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP393.01;TN929.5

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