多电压SoC引线压焊供电引脚分配及电源网络拓扑优化

针对较大电压降易导致电压岛内电路宏模块供电不足,引起电路失效的问题,基于引线压焊技术封装芯片,提出一种面向多电压技术的电压岛供电引脚分配及电源网络拓扑优化方法.首先根据电压岛的物理布图信息,采用弹簧模型确定电压岛供电引脚位置实现电压降优化;然后通过建立稠密的虚拟电源网络,利用增量式方法完成电源网络的拓扑优化.通过对GSRC标准电路测试的实验结果表明,与固定供电引脚方法相比,文中提出的供电引脚分配方法平均降低电压降26.1%;而电源网络拓扑优化方法产生的非规则网络,使得电源网络布线面积较规则电源网络的布线面积平均降低84.5%.     

关于网络拓扑故障的分析

网络拓扑故障多数情况下是由于人为原因造成的,即在不熟悉网络结构的情况下对网络拓扑结构进行更改,或进行网络拓扑结构优化而带来的网络故障.故笔者结合多年网络维护经验,对这两方面的问题进行了分析,并提出了合理的建议.     

基于重填化学反应优化解决无线网络动态最短选路优化问题

采用人工智能优化技巧轻易解决静态最短选路(SP)优化问题,但是随着无线通讯的发展,诸如移动Ad Hoc网络与无线传感网络等新式无线网络被大量广泛使用.在这些新式无线网络中,网络拓扑随着时间而不断变化从而导致最短选路优化问题被转变成动态优化问题.提出了一种新式的基于化学反应优化(CRO)的算法来解决这个问题.化学反应优化...     

基于软件定义网络的数据中心网络拓扑优化研究

近年来,随着互联网的快速发展和数据中心的兴起,网络流量的快速增长已经成为了当前网络中的一个重要问题。传统的网络架构已经无法满足这一快速增长的需求,需要寻找新的网络架构来优化数据中心网络的拓扑结构和流量控制,以提高网络整体服务质量。基于软件定义网络的数据中心网络拓扑优化可以极大地提高网络利用率和性能。本文针对现有数据中心网络拓扑结构的不足,提出软件定义网络的数据中心网络拓扑优化策略,旨在提高数据中心网络性能和可管理性,以满足未来数据中心网络应用场景的需求。     

基于动态优化因子的Zigbee协议优化仿真算法

研究Zigbee无线网络协议的优化.当前的无线传感网络中以Zigbee协议为基础,传递过程存在很大的盲目性,节点分布存在较大的随机性.目前协议中,节点最优评估通信方法大多针对静态无权网络拓扑结构,一旦随机性增大,网络拓扑结构发生松动,造成非最优节点参与通信,引起协议效率低下.为了避免上述缺陷,提出了一种基于动态优化因子的Zigbee协议优化仿真算法.应用动态优化因子方法,对网络节点进行搜索,为网络协议优化提供准确的数据基础.利用簇树拓扑方法,为网络节点分配地址,从而实现Zigbee协议优化.实验结果表明,应用改进算法进行Zigbee协议优化处理,能够提高网络服务效率.     

无线传感器网络动态路由研究与展望

无线传感器网络是集信息采集、信息传榆、信息处理于一体的综合智能信息系统,具有广阔的应用前景,是信息网络技术中的一个新领域.节点资源极端受限、大规模网络的随机散布以及网络拓扑的动态性都给路由选择与优化问题的研究带来了前所未有的挑战.重点介绍了无线传感器网络路由问题面临的挑战,分析了路由问题的多种制约因素,并提出了动态网络拓扑模型建立的必要性和路由问题的发展方向.     

现代优化技术在无线传感器网络中的应用

无线传感器网络是由大量廉价、微小的传感器节点构成的,用于实时监测环境的无线自组织网络。由于无线传感器网络具有规模庞大、节点的计算和存储资源受限、网络拓扑动态不确定等特性,设计高性能的无线传感器网络常常会涉及传统的技术难以建模和求解的复杂优化问题。现代优化技术是一类新兴的具有较强全局搜索能力的启发式优化算法,是解决无线传感器网络中的复杂优化问题的有力工具。作者在研究现代优化技术在无线传感器网络中的主要应用领域,包括传感器节点的定位、网络节点的部署优化、网络覆盖优化、网络路由优化和数据融合问题的基础上,分析了现代优化技术在求解无线传感器网络中的复杂优化问题上的优缺点,并对该领域未来的发展趋势进行了展望。     

一种基于网络顶点割的拓扑优化算法

优化网络设计是网络管理的目标之一。介绍一种基于关键链路分析和图的顶点割来优化网络拓扑结构设计的BTop算法,它结合流量工程和图形学理论对已有的网络拓扑进行优化设计。对算法的特性进行了分析,使用Abilene流量和拓扑数据验证了算法的有效性。     

园区网优化仿真分析

分析园区网现状提出园区网优化的必要性,在优化原则的基础上提出了优化方案,通过OPNET平台对比优化前后的网络拓扑结构、网络延时、核心交换及吞吐量及瓶颈节点吞吐量等网络性能参数,结果表明网络仿真建模能够较为真实的发现问题,对园区网优化具有一定参考意义。     

网络编码中的优化问题研究

简要回顾了网络编码的理论研究,阐述了网络编码优化问题研究的重要意义.在介绍网络信息流模型的基础上,针对优化问题的陈述、特点和解法,结合最新的研究成果进行了综述.根据优化目标的不同,优化问题可分成4类:最小花费组播,无向网络的最大吞吐率,最小编码节点、编码边,基于网络编码的网络拓扑设计.归纳了问题的求解性质,对其中的(线性或凸)规划问题总结了求解的一般方法,对NP完全问题讨论了最新的启发式算法及其设计难点.同时,展望了未来的发展方向.     

Topology Design of Network-Coding-Based Multicast Networks

It is anticipated that a large amount of multicast traffic needs to be supported in future communication networks. The network coding technique proposed recently is promising for establishing multicast connections with a significantly lower bandwidth requirement than that of traditional Steiner-tree-based multicast connections. How to design multicast network topologies with the consideration of efficiently supporting multicast by the network coding technique becomes an important issue now. It is notable, however, that the conventional algorithms for network topology design are mainly unicast-oriented, and they cannot be adopted directly for the efficient topology design of network-coding-based multicast networks by simply treating each multicast as multiple unicasts. In this paper, we consider for the first time the novel topology design problem of network-coding-based multicast networks. Based on the characteristics of multicast and network coding, we first formulate this problem as a mixed-integer nonlinear programming problem, which is NP-hard, and then propose two heuristic algorithms for it. The effectiveness of our heuristics is verified through simulation and comparison with the exhaustive search method. We demonstrate in this paper that, in the topology design of multicast networks, adopting the network coding technique to support multicast transmissions can significantly reduce the overall topology cost as compared to conventional unicast-oriented design and the Steiner-tree-based design.     

Fault-tolerant topology control algorithm for mobile robotic networks

In multi-agent systems, it is crucial to maintain a robust and fault-tolerant network topology while minimizes power consumption, especially for the multiple unmanned combat platforms based on mobile robotic networks. This work studies the problem of fault-tolerant topology control in mobile robotic networks. With the aim of constructing self-healing networks, a K-connected topology control algorithm that can cope with faults such as node failures and link disruptions is proposed. The robotic team stays connected in the dynamic interaction topology, even in the face of K-1 nodes departure. Our approach combines power transmission and motion control for constructing a K-connected network topology with approximately minimum power to prolong their working life. Extensive numerical simulations demonstrate the effectiveness of the proposed solution are presented.     

Time-domain analysis methodology for large-scale RLC circuits and its applications

With soaring work frequency and decreasing feature sizes, VLSI circuits with RLC parasitic components are more like analog circuits and should be carefully analyzed in physical design. However, the number of extracted RLC components is typically too large to be analyzed efficiently by using present analog circuit simulators like SPICE. In order to speedup the simulations without error penalty, this paper proposes a novel methodology to compress the time-descritized circuits resulted from numerical integration approximation at every time step. The main contribution of the methodology is the efficient structure-level compression of DC circuits containing many current sources, which is an important complement to present circuit analysis theory. The methodology consists of the following parts: 1) An approach is proposed to delete all intermediate nodes of RL branches. 2) An efficient approach is proposed to compress and back-solve parallel and serial branches so that it is error-free and of linear complexity to analyze circuits of tree topology. 3) The Y toπtransformation method is used to error-free reduce and back-solve the intermediate nodes of ladder circuits with the linear complexity. Thus, the whole simulation method is very accurate and of linear complexity to analyze circuits of chain topology. Based on the methodology, we propose several novel algorithms for efficiently solving RLC-model transient power/ground (P/G) networks. Among them, EQU-ADI algorithm of linear-complexity is proposed to solve RLC P/G networks with mesh-tree or mesh-chain topologies. Experimental results show that the proposed method is at least two orders of magnitude faster than SPICE while it can scale linearly in both time- and memory-complexity to solve very large P/G networks.     

自组网中基于自适应波束天线的拓扑控制算法

定向天线自组网拓扑的构建问题比全向天线网络复杂.基于自适应波束定向天线模型提出一种分布式拓扑控制算法,通过调整节点发射功率,改变天线波束的朝向、宽度和增益来构建拓扑.网络中每个节点收集其邻居节点信息,采用功率控制调度策略选择最优相邻节点,并选取覆盖所有最优相邻节点的最小发射功率为此节点的发射功率.算法在保证网络连通性与无向性的同时,降低了节点的发射功率,减小了节点的平均度数,从而降低节点能耗,减少了节点间干扰,提高了网络吞吐量.仿真结果表明,算法显著提高了网络性能.     

拓扑控制对Ad hoc网络能耗及生存期的影响分析

Ad hoc网络的能耗主要与节点的发射功率、数据包转发次数及端到端通过量三者有关。通过建立网络能耗模型分析和实验仿真发现,对负载较低的网络实施拓扑控制技术可以降低网络能耗、延长网络生存期;而对负载较高的网络实施拓扑控制技术,虽不能有效降低网络能耗,但仍然可以延长网络的生存期。     

无线传感器网络拓扑控制的理论探讨

拓扑控制是无线传感器网络中重要的节能技术,并且已经形成了功率控制和睡眠调度两个主流研究方向。针对当前研究工作中存在的问题,对拓扑控制进行了基础性的研究。全面地考虑了网络的通信能耗和空闲能耗,在理想情况下给出了以最小化能耗为目标的拓扑控制问题的一个明确定义;证明了这个问题是NP-难的,同时非形式化地讨论了更实际的拓扑控制问题的计算复杂性;并且在此基础上,进一步提出了关于如何设计能量高效的拓扑控制协议的3个必要性原则。希望本研究成果有助于探索更好的拓扑控制协议。     

一种基于模糊控制的无线传感器网络拓扑控制算法

在无线传感器网络乃至无线网络邻域中,拓扑控制一直是研究热点之一,是无线传感器网络中一种重要的能量节省技术。当前已有很多能量高效的拓扑控制算法,它们试图寻求一个合适的节点发射功率或者一个良好的网络拓扑结构,实际应用中两者往往都需要考虑。提出一种新的拓扑控制方法——HFLTC,该方法基于模糊控制和链路质量评估模型优化进行功率控制,并引入XTC算法思想成链。仿真结果表明,这种把拓扑结构和功率控制结合考虑的方法,更节省网络的平均能耗,提高了整个网络的生命周期。     

一种基于数据流跟踪的无线传感网能量模型及网络优化

提出了一种基于数据流跟踪的能量模型,通过跟踪数据流在网络中的整个过程来计算全网的能量消耗,是一种不受网络结构限制的普遍适用的能量模型;在此基础上.建立了基于能耗的网络优化模型,针对链式和簇式结构进行了拓扑、功率和路由方面的优化设计,仿真结果证明了理论分析的正确性.     

一种无线传感器网络能耗均衡的自适应拓扑博弈算法

针对无线传感器网络节点能量有限与能耗不均衡导致网络生命周期提前结束的问题,运用势博弈理论将节点的平均寿命、节点最短寿命、网络的连通性以及覆盖性应用到效益函数的设计中,建立一种基于序数势博弈的能耗均衡的拓扑控制模型,以证明博弈模型是序数势博弈.基于该势博弈模型,提出一种能耗均衡的自适应拓扑博弈算法.该算法根据节点平均寿命调整自身的功率,帮助最短寿命节点降低功率,延长整个网络的生存时间.仿真实验及对比分析表明,所提出的算法相比于其他基于博弈论的拓扑控制算法,能够改善网络能量的均衡性,提高网络能量效率,保证网络拓扑的健壮性,增强网络拓扑的自适应性.     

Neural networks for shortest path computation and routing incomputer networks

The application of neural networks to the optimum routing problem in packet-switched computer networks, where the goal is to minimize the network-wide average time delay, is addressed. Under appropriate assumptions, the optimum routing algorithm relies heavily on shortest path computations that have to be carried out in real time. For this purpose an efficient neural network shortest path algorithm that is an improved version of previously suggested Hopfield models is proposed. The general principles involved in the design of the proposed neural network are discussed in detail. Its computational power is demonstrated through computer simulations. One of the main features of the proposed model is that it will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology.