嵌入式虚拟机在舰艇编队防御系统中的应用

面对信息化、远程精确打击的空袭作战,必须构建网络化、一体化的防空作战体系。本文主要针对舰艇编队一体化防御系统中无线网络的不可靠性,设计了基于嵌入式虚拟机的架构,提出了任务的动态分配和移植算法。首先根据作战环境中网络的拓扑条件,虚拟机动态的将控制任务分配到可靠的无线节点中,并实时监测网络状态,当网络结构发生变化时,将底层不可靠无线节点中的控制任务移植到可靠的无线节点中。通过这种方法可以解决作战环境恶劣和无线网络本身的不可靠引起的控制的不稳定性问题,确保防御任务的顺利进行。仿真结果表明,当编队一体化防御系统的链路发生故障时,此方法能够在不改变原有控制算法的基础上,迅速重建控制构架,不仅适用于舰艇编队一体化防御系统这种对时间要求和安全性要求高的系统,而且对基于无线网络的控制系统也有一定的借鉴意义。     

虚拟机在舰艇编队一体化防御系统中的应用

面对信息化、远程精确打击的空袭作战,必须构建网络化、一体化的防空作战体系;主要针对舰艇编队一体化防御系统中无线网络的不可靠性,设计了基于嵌入式虚拟机的架构,提出了任务的动态分配和移植算法;首先根据作战环境中网络的拓扑条件,虚拟机动态的将控制任务分配到可靠的无线节点中,并实时监测网络状态,当网络结构发生变化时,将底层不可靠无线节点中的控制任务移植到可靠的无线节点中;通过这种方法可以解决作战环境恶劣和无线网络本身的不可靠引起的控制的不稳定性问题,确保防御任务的顺利进行;仿真结果表明,当编队一体化防御系统的链路发生故障时,此方法能够在不改变原有控制算法的基础上,迅速重建控制构架,不仅适用于舰艇编队一体化防御系统这种对时间要求和安全性要求高的系统,而且对基于无线网络的控制系统也有一定的借鉴意义。     

一种能耗均衡的无线传感器网络加权无标度拓扑研究

针对无线传感器网络节点能耗不均衡问题,通过对节点生命期建模,得出节点生命期受节点剩余能量和通信距离的影响,进而将两端节点生命期作为构建拓扑时边权重的影响因子,通过边权重控制节点权重,最终得出了一种能耗均衡的无线传感器网络加权无标度拓扑模型,并理论证明了该模型的点权、边权和节点度均服从幂律分布.实验结果表明,该模型具有无标度拓扑的强容错性,并有效的均衡了网络中的节点能耗,延长了网络的生命期.     

一种优化无线传感器网络生命周期的容错拓扑研究

<正>由于无线传感器网络的节点能量受限,优化网络生命周期成为设计网络拓扑时首要考虑的问题.通过分析节点的剩余能量和负载量对节点生命周期的影响,提出了一种可延长无线传感器网络生命期的容错拓扑演化模型,并得出了在节点满足网络生存时间的条件下负载调节系数的取值范围.仿真实验结果表明,基于无标度网络的演化拓扑结构具有较好的容错性,并能够均衡网络节点能耗和延长网络生命周期.     

无线传感网节点群的远程控制研究

随着通信技术、嵌入式技术和传感器技术的飞速发展,传感器网络由于具备自组织性、协作性和动态组网变化等特点,在经济生活各方面应用广泛;但目前商用传感网络多为有线的局域网连接,对一些离散型的节点适用性不强,特别是在一些需要远程接入的控制场合就很难实现;针对上述不足,提出了一种基于无线网络与GPRS网络相结合的无线传感网络节点群远程控制方式;离散型终端节点通过433 MHz频点组成无线传感网络,经过本地嵌入式中心节点实现与GPRS网络的异网连接,进而通过手持式终端实现远程监控;实验系统比较了节点群在不同气候条件下数据传输的距离与可靠性,结果表明系统能实现上述两种无线网络的异网数据交换,有效地解决了传感网节点群的远程控制等问题。     

一种链式无线路由协议的液压支架压力监测系统设计

本文研究并设计了一种基于无线传感器网络的液压支架压力信息采集节点,并以此为基础搭建了监测系统平台。首先对无线网络拓扑结构分析,以提高网络整体生命周期为目的,设计了一种链式结构的简单无线路由协议。在此基础上,以CC2530为主处理器完成压力采集节点软、硬件设计。最后,给出了系统采集到的液压支架工作阻力数据。测试结果表明,系统能够实现液压支架压力实时监测功能,满足应用需求。     

无线传感器网络中无标度拓扑的动态容错性分析

增强网络的动态容错性对于抵御级联失效具有重要的现实意义.根据无线传感器网络无标度拓扑中节点可变负载和恒定容量的特点建立级联失效模型,在随机节点失效下研究负载参数和拓扑参数对其级联失效容错性的影响规律,解析推导出该网络大规模级联失效的承载极限,同时模拟结果发现该网络的级联失效容错性与其度分布系数和幂指数正相关.这为从参数优化角度抵御随机节点失效下无线传感器网络无标度拓扑级联失效危害提供了依据.     

基于节点健康度的无线传感器网络冗余通路控制方法

广泛应用于各种物理参数测量领域的无线传感器网络,因其节点具有能量供应有限、硬件资源有限、数目众多、自组织和动态拓扑等特点,使得网络极易发生故障,从而高可靠、低故障是其运行的基本要求.本文针对多冗余通路设计的无线传感器网络故障预防方法存在工作状态冗余节点过多、能量大量浪费的问题,提出一种基于节点健康度的冗余通路控制方法.该方法利用汇聚节点收集网络内所有节点能量状态,计算节点健康度等相关参数,使用A-Star算法选择最优工作通路,控制其余冗余通路分批轮流休眠,从而达到减少和均衡网络工作过程能量消耗、预防某些节点能量提前耗尽导致网络能量故障发生的目的.仿真实验和实际节点实验的结果表明,在保证网络适当冗余通路的前提下,与其他相关方法比较,该方法可以显著均衡网络能量消耗,有效预防节点能量故障提前发生,明显延长网络寿命.     

一种无线传感器网络健壮性可调的能量均衡拓扑控制算法

无线传感器网络中, 应用环境的干扰导致节点间距不能被准确度量. 所以利用以节点间距作为权重的闭包图(EG)模型构建的拓扑没有考虑环境的干扰, 忽略了这部分干扰带来的能耗, 缩短了网络生存时间. 针对无线传感器网络拓扑能量不均的特点和EG模型的缺陷, 首先引入节点度调节因子, 建立通信度量模型和节点实际生存时间模型; 其次量化网络节点度, 从而获取满足能量均衡和网络生命期最大化需求的节点度的取值规律; 然后利用该取值规律和函数极值充分条件解析推导出网络最大能量消耗值和最长生存时间, 并获得最优节点度; 最后基于以上模型提出一种健壮性可调的能量均衡拓扑控制算法. 理论证明该拓扑连通且为双向连通. 仿真结果说明网络能利用最优节点度达到较高的健壮性, 保证信息可靠传输, 且算法能有效平衡节点能耗, 提高网络健壮性, 延长网络生命周期.     

一种基于Zigbee与GPS山体滑坡监测系统开发

针对山体滑坡监测布线难度大,实施困难问题,提出了一种基于无线传感器网络的山体滑坡监测设计方案;该方案结合无线网络传感器技术,GPRS技术以及GPS定位技术,通过无线传感器网络采集山体倾角,液位值实时数据,以及GPS节点坐标信息汇总到嵌入式网关通过GPRS网络接入远程监控中心;通过综合分析可以及时预测滑坡发生的具体位置,为及时疏散居民提供宝贵的时间;文章详细介绍了系统的软硬件设计方法,系统通过实物搭建和实验分析验证了方案可行性;测试表明:系统稳定性好,相对误差控制在3%以内,可以较好地预测滑坡发生。     

Studies on controllability of directed networks with extremal optimization

Almost all natural, social and man-made-engineered systems can be represented by a complex network to describe their dynamic behaviors. To make a real-world complex network controllable with its desired topology, the study on network controllability has been one of the most critical and attractive subjects for both network and control communities. In this paper, based on a given directed–weighted network with both state and control nodes, a novel optimization tool with extremal dynamics to generate an optimal network topology with minimum control nodes and complete controllability under Kalman’s rank condition has been developed. The experimental results on a number of popular benchmark networks show the proposed tool is effective to identify the minimum control nodes which are sufficient to guide the whole network’s dynamics and provide the evolution of network topology during the optimization process. We also find the conclusion: “the sparse networks need more control nodes than the dense, and the homogeneous networks need fewer control nodes compared to the heterogeneous” (Liu et al., 2011  [18]), is also applicable to network complete controllability. These findings help us to understand the network dynamics and make a real-world network under the desired control. Moreover, compared with the relevant research results on structural controllability with minimum driver nodes, the proposed solution methodology may also be applied to other constrained network optimization problems beyond complete controllability with minimum control nodes.     

Synchronization-based topology identification of weighted general complex dynamical networks with time-varying coupling delay

Many existing papers investigated the geometric features, control and synchronization of complex dynamical networks provided with certain topology. However, the exact topology of a network is sometimes unknown or uncertain. Based on LaSalle’s invariance principle, we propose an adaptive feedback technique to identify the exact topology of a weighted general complex dynamical network model with time-varying coupling delay. By receiving the network nodes evolution, the topology of such a kind of network with identical or different nodes, or even with varying topology can be monitored. In comparison with previous methods, time delay is taken into account in this simple, analytical and systematic synchronization-based technique. Particularly, the weight configuration matrix is not necessarily symmetric or irreducible, and the inner-coupling matrix need not be symmetric. Illustrative simulations are provided to verify the correctness and effectiveness of the proposed scheme.     

Network-topology-adaptive quantum conference protocols

As an important application of the quantum network communication,quantum multiparty conference has made multiparty secret communication possible.Previous quantum multiparty conference schemes based on quantum data encryption are insensitive to network topology.However,the topology of the quantum network significantly affects the communication efficiency,e.g.,parallel transmission in a channel with limited bandwidth.We have proposed two distinctive protocols,which work in two basic network topologies with efficiency higher than the existing ones.We first present a protocol which works in the reticulate network using Greeberger-Horne-Zeilinger states and entanglement swapping.Another protocol,based on quantum multicasting with quantum data compression,which can improve the efficiency of the network,works in the star-like network.The security of our protocols is guaranteed by quantum key distribution and one-time-pad encryption.In general,the two protocols can be applied to any quantum network where the topology can be equivalently transformed to one of the two structures we propose in our protocols.     

Topology identification of weighted complex dynamical networks

Recently, various papers investigated the geometry features, synchronization and control of complex network provided with certain topology. While, the exact topology of a network is sometimes unknown or uncertain. Using Lyapunov theory, we propose an adaptive feedback controlling method to identify the exact topology of a rather general weighted complex dynamical network model. By receiving the network nodes evolution, the topology of such kind of network with identical or different nodes, or even with switching topology can be monitored. Experiments show that the methods presented in this paper are of high accuracy with good performance.     

一种基于离散数据从局部到全局的网络重构算法

网络的结构和功能彼此相互影响,网络上的功能往往体现为网络上的动力学过程,网络上的动力学过程通过网络中的行为表象数据进行体现.因此,根据网络上可观测的相关数据对网络结构进行重构将成为可能.本文拟解决如何根据网络上可观测的离散数据还原网络拓扑结构的问题,提出了在网络局部利用每一条离散数据对应节点的相似程度来推测节点间发生连边的可能性,通过多条离散数据重构网络各个局部拓扑并将由多条数据得到的局部拓扑进行叠加,最终重构出整个网络的全局拓扑结构的算法.为了验证算法的可行性与准确性,在小世界、无标度和随机网络中进行了网络重构实验,通过在三种不同类型及不同规模的网络中进行网络重构实验可以看出,网络重构算法在不同类型网络中的表现也不同,且网络的平均度值也会影响网络重构算法对数据的要求.为了验证算法的适用性,对三个实际网络进行了网络重构实验,结果显示算法能够适用实际较大规模网络的重构.该算法具有很好的适用性和准确度,适合不同类型网络的拓扑结构重构场景.     

Network evolution driven by dynamics applied to graph coloring

An evolutionary network driven by dynamics is studied and applied to the graph coloring problem. From an initial structure, both the topology and the coupling weights evolve according to the dynamics. On the other hand, the dynamics of the network are determined by the topology and the coupling weights, so an interesting structure-dynamics co-evolutionary scheme appears. By providing two evolutionary strategies, a network described by the complement of a graph will evolve into several clusters of nodes according to their dynamics. The nodes in each cluster can be assigned the same color and nodes in different clusters assigned different colors. In this way, a co-evolution phenomenon is applied to the graph coloring problem. The proposed scheme is tested on several benchmark graphs for graph coloring.     

A novel method to identify influential nodes based on hybrid topology structure

With the increasing popularity of rail transit and the increasing number of light rail trips, the vulnerability of rail transit has become increasingly prominent. Once the rail transit is maliciously broken or the light rail station is repaired, it may lead to large-scale congestion or even the paralysis of the whole rail transit network. Hence, it is particularly important to identify the influential nodes in the rail transit network. Existing identifying methods considered a single scenario on either betweenness centrality (BC) or closeness centrality. In this paper, we propose a hybrid topology structure (HTS) method to identify the critical nodes based on complex network theory. Our proposed method comprehensively considers the topology of the node itself, the topology of neighbor nodes, and the global influence of the node itself. Finally, the susceptible–infected–recovered (SIR) model, the monotonicity (M), the distinct metric (DM), the Jaccard similarity coefficient (JSC), and the Kendall correlation coefficient (KC) are utilized to evaluate the proposed method over the six real-world networks. Experimental results confirm that the proposed method achieves higher performance than existing methods in identifying networks.     

Modeling the COVID-19 epidemic and awareness diffusion on multiplex networks

The coronavirus disease 2019(COVID-19)has been widely spread around the world,and the control and behavior dynamics are still one of the important research directions in the world.Based on the characteristics of COVID-19’s spread,a coupled disease-awareness model on multiplex networks is proposed in this paper to study and simulate the interaction between the spreading behavior of COVID-19 and related information.In the layer of epidemic spreading,the nodes can be divided into five categories,where the topology of the network represents the physical contact relationship of the population.The topological structure of the upper network shows the information interaction among the nodes,which can be divided into aware and unaware states.Awareness will make people play a positive role in preventing the epidemic diffusion,influencing the spread of the disease.Based on the above model,we have established the state transition equation,through the microscopic Markov chain approach(MMCA),and proposed the propagation threshold calculation method under the epidemic model.Furthermore,MMCA iteration and the Monte Carlo method are simulated on the static network and dynamic network,respectively.The current results will be beneficial to the study of COVID-19,and propose a more rational and effective model for future research on epidemics.     

基于贝叶斯网络的认知引擎设计与重配置

以往的通信行为指导系统未来通信, 以满足用户需求并适应环境变化, 是认知无线电系统的核心所在, 为此提出了一种基于贝叶斯网络的认知引擎, 用于解决在复杂多变的电磁环境与用户需求条件下, 认知无线电系统参数自适应调整的问题. 通过对系统过去通信行为样本数据, 进行结构学习和参数学习建立认知引擎, 将系统当前环境状态和用户需求信息经预处理作为推理的证据, 应用引擎决策出系统此时最佳的工作参数, 完成系统参数重构. 本文利用OPNET工具建立一个移动无线网络完成仿真实验, 仿真结果表明该认知引擎能有效地使移动无线网络适应环境变化, 改善端到端通信性能, 进一步验证了建模方法的可行性.