Small World Model-Based Polylogarithmic Routing Using Mobile Nodes

The use of mobile nodes to improve network system performance has drawn considerable attention recently The movement-assisted model considers mobility as a desirable feature, where routing is based on the store-carry-forward paradigm with random or controlled movement of resource rich mobile nodes. The application of such a model has been used in several emerging networks, including mobile ad hoc networks （MANETs）, wireless sensor networks （WSNs）, and delay tolerant networks （DTNs）. It is well known that mobility increases the capacity of MANETs by reducing the number of relays for routing, prolonging the lifespan of WSNs by using mobile nodes in place of bottleneck static sensors, and ensuring network connectivity in DTNs using mobile nodes to connect different parts of a disconnected network. Trajectory planning and the coordination of mobile nodes are two important design issues aiming to optimize or balance several measures, including delay, average number of relays, and moving distance. In this paper, we propose a new controlled mobility model with an expected polylogarithmic number of relays to achieve a good balance among several contradictory goals, including delay, the number of relays, and moving distance. The model is based on the small-world model where each static node has ＂short＂ link connections to its nearest neighbors and ＂long＂ link connections to other nodes following a certain probability distribution. Short links are regular wireless connections whereas long links are implemented using mobile nodes. Various issues are considered, including trade-offs between delay and average number of relays, selection of the number of mobile nodes, and selection of the number of long links. The effectiveness of the proposed model is evaluated analytically as well as through simulation.

A Multifractal Detrended Fluctuation Analysis of the Ising Financial Markets Model with Small World Topology

We present a multifractal detrended fluctuation analysis （MFDFA） of the time series of return generated by our recently-proposed Ising financial market model with underlying small world topology. The result of the MFDFA shows that there exists obvious multifractal scaling behavior in produced time series. We compare the MFDFA results for original time series with those for shuffled series, and find that its multifractal nature is due to two factors： broadness of probability density function of the series and different correlations in small- and large-scale fluctuations. This may provide new insight to the problem of the origin of multifractality in financial time series.

Nonlinear Dynamical Behavior in BS Evolution Model Based on Small-World Network Added with Nonlinear Preference

We introduce a modified small-world network adding new links with nonlinearly preferential connectioninstead of adding randomly,then we apply Bak-Sneppen(BS)evolution model on this network.We study severalimportant structural properties of our network such as the distribution of link-degree,the maximum link-degree,and thegth of the shortest path.We further argue several dynamical characteristics of the model such as the important criticalvalue f_c,the f_0 avalanche,and the mutating condition,and find that those characteristics show panticular behaviors.

Comparison of Ising Model and Potts Model on Non-Local Directed Small-World Networks

Further to the investigation of the critical properties of the Potts model with q = 3 and 8 states in one dimension (1D) on directed small-world networks reported by Aquino and Lima, which presents, in fact, a second-order phase transition with a new set of critical exponents, in addition to what was reported in Sumour and Lima in studying Ising model on non-local directed small-world for several values of probability 0 < P < 1. In this paper the behavior of two models discussed previously, will be re-examined to study differences between their behavior on directed small-world networks for networks of different values of probability P = 0.1, 0.2, 0.3, 0.4 and 0.5 with different lattice sizes L = 10, 20, 30, 40, and 50 to compare between the important physical variables between Ising and Potts models on the directed small-world networks. We found in our paper that is a phase transitions in both Ising and Potts models depending essentially on the probability P.

Using probabilistic cache scheme to construct the small world network

Recently some P2P systems have constructed the small world network using the small world model so as to improve the routing performance.In this paper,we propose a novel probabilistic cache scheme to construct the small world network based on the small world model and use it to improve CAN,that is,PCCAN(Probabilistic Cache-based CAN).PCCAN caches the long contact.It uses the worm routing replacing mechanism and probabilistic replacing strategy on the cache.The probabilistic cache scheme proves to be an efficient approach to model the small world phenomenon.Experiments in both the static and the dynamic network show that PCCAN can converge to the steady state with the cache scheme,and the routing performance is significantly improved with additional low overheads in the network compared with CAN.

A local-world evolving hypernetwork model

Complex hypernetworks are ubiquitous in the real system. It is very important to investigate the evolution mecha- nisms. In this paper, we present a local-world evolving hypernetwork model by taking into account the hyperedge growth and local-world hyperedge preferential attachment mechanisms. At each time step, a newly added hyperedge encircles a new coming node and a number of nodes from a randomly selected local world. The number of the selected nodes from the local world obeys the uniform distribution and its mean value is m. The analytical and simulation results show that the hyperdegree approximately obeys the power-law form and the exponent of hyperdegree distribution is 7 = 2 ＋ 1/m. Furthermore, we numerically investigate the node degree, hyperedge degree, clustering coefficient, as well as the average distance, and find that the hypemetwork model shares the scale-flee and small-world properties, which shed some light for deeply understanding the evolution mechanism of the real systems.

Neural Network Predictive Control of Variable-pitch Wind Turbines Based on Small-world Optimization Algorithm

通过将混沌映射用于产生初始节点集和进行算子构造,提出一种新的基于实数编码的混沌小世界优化算法。采用4种算法对多例复杂函数的优化问题进行仿真试验,表明所提算法具有能够有效避免陷入局部极小值、快速搜索到最优值的能力。将上述方法应用于变桨距风电机组启动并网时的转速控制,提出一种基于混沌小世界优化算法的神经网络预测控制策略,其预测模型由基于现场数据的神经网络模型建立。仿真与实际测试结果表明,该系统可以根据风速扰动提前预测电机的转速变化,使控制器超前动作,保证系统输出跟踪参考轨迹的方向稳步改变,确保风电机组平稳并网。

基于复杂网络演化博弈的无线传感器网络入侵检测方法

针对无线传感器网络资源受限和入侵检测系统策略优化问题,本文提出一种基于复杂网络演化博弈的无线传感器网络入侵检测方法。结合小世界模型理论,模拟网络节点之间的连接关系,在不改变节点原有关系的前提下增强网络连通性并降低传输能耗;构建关于簇头节点和恶意节点的无线传感器网络攻防博弈模型,通过收益矩阵计算节点收益,利用奖惩机制描述节点在博弈过程中选择不同策略的收益变化;引入经验加权吸引力学习算法改进传统博弈的策略更新规则并将该算法应用于入侵检测系统,使得簇头节点能够动态更新策略选择,得到不同条件下的入侵检测最优策略。实验结果表明,与传统方法相比,所提算法的簇头节点检测策略扩散深度可以达到79%,该算法下簇头节点在保障自身检测收益的同时尽可能选择检测传感器网络中出现的攻击,保证网络检测率并减少网络各类资源的消耗。

A Novel Neighbor-Preferential Growth Scale-Free Network Model and its Properties

In this paper, we propose a novel neighbor-preferential growth (NPG) network model. Theoretical analysis and numerical simulations indicate the new model can reproduce not only a scale-free degree distribution and its power exponent is related to the edge-adding number m, but also a small-world effect which has large clustering coefficient and small average path length. Interestingly, the clustering coefficient of the model is close to that of globally coupled network, and the average path length is close to that of star coupled network. Meanwhile, the synchronizability of the NPG model is much stronger than that of BA scale-free network, even stronger than that of synchronization-optimal growth network.

小世界超网络传播模型及实证分析

借鉴WS小世界网络的构造算法,在超网络(也称为超图)中加入超边随机重连,构建小世界超网络模型。以该模型为信息传播的底层网络,提出小世界超网络SIR信息传播模型,仿真模拟超边随机重连概率、超网络中的邻居节点数量以及传播率、恢复率等参数对超网络的信息传播过程影响,并与普通网络中的传播过程进行了对比分析,发现超网络中的信息传播速度更快,波及范围更广。进一步,在3类实证超网络上验证了超网络的小世界特性及信息传播规律。在万物互联时代,世界变得更小的背景下,对现实世界中更为复杂的信息与疾病传播机制的深入研究具有一定的借鉴意义。

Stability of weighted spectral distribution in a pseudo tree-like network model

The comparison of networks with different orders strongly depends on the stability analysis of graph features in evolving systems. In this paper, we rigorously investigate the stability of the weighted spectral distribution（i.e., a spectral graph feature） as the network order increases. First, we use deterministic scale-free networks generated by a pseudo treelike model to derive the precise formula of the spectral feature, and then analyze the stability of the spectral feature based on the precise formula. Except for the scale-free feature, the pseudo tree-like model exhibits the hierarchical and small-world structures of complex networks. The stability analysis is useful for the classification of networks with different orders and the similarity analysis of networks that may belong to the same evolving system.

基于复杂网络的新冠病毒群体免疫数值仿真

鉴于构建流行病动力学模型、探索流行病传播规律对疫情防控具有十分重要的理论意义和实际应用价值,在已有的均匀混合模型基础上,针对个体接触关系异质化越发明显,且每个个体都处在不同的接触关系中,建立了兼顾个体状态与接触追踪的动态小世界网络模型。模拟了新冠病毒在社会中的传播过程。通过对比仿真结果,说明了所建模型的合理性。在此基础上,仿真计算了网络拓扑结构与接种免疫人数占比共同作用下对新冠病毒传播的影响,分析得到群体免疫临界值。说明所建传播模型合理,接种疫苗实现群体免疫可行。

一类确定性小世界超网络特性分析

用随机超网络模型去理解一个真实网络的形成机制是不直观的。针对这一问题,提出一种基于节点迭代的确定性超网络模型,利用分形理论和超网络研究方法,解析了平均超度、超度分布、平均距离和直径等拓扑特性,发现此类超网络的超度分布服从指数分布,超网络平均距离和直径与网络规模的对数成比例。给出了一种节点标号方法,并基于此方法推导出了计算超网络拉普拉斯特征值的递推关系式,此关系式可降低计算超网络拉普拉斯特征值的时间复杂度。研究结果表明,利用该文提出的节点迭代法构造的超网络具有小世界特性,这有助于更好地理解真实网络的复杂性和多样性。

基于小世界网络的煤矿工人违规佩戴口罩行为传播和干预研究

为了对煤矿工人防尘口罩违规佩戴行为进行有效控制,寻找违规行为传播和干预的影响因素,基于小世界网络构建了煤矿工人违规佩戴防尘口罩的行为传播模型。研究表明:基于小世界网络的行为传播模型可以模拟比较复杂的煤矿工人违规佩戴防尘口罩行为的传播过程,并对其做出可视化分析及预测;煤矿工人违规佩戴防尘口罩行为的传播速度随煤矿规模增大而加快,但煤矿规模超过300人时增速趋于平缓;当事前干预有效率达到40%,或事后干预有效率达到50%以上时可以防止违规佩戴防尘口罩行为大面积传播;当事前干预有效率达到70%以上时,可以有效阻止违规佩戴行为在工人中传播;即使事后干预有效率达到100%,违规行为传播后仍然能够让违规工人数达到总工人数的18.0%~22.7%;事前干预有效率达到20%~30%,配合不同程度的事后干预,比单纯进行事前或事后干预效果更佳。

基于小世界神经网络的放电特性研究

研究小世界网络参数的改变对神经元网络放电特性的影响会对癫痫等神经疾病的研究工作产生重要的推动作用。癫痫等神经疾病的发作总会伴随着神经网络的同步放电现象,目前神经网络的研究主要是基于层级网络,其拓扑结构不能模拟真实生物神经网络,而小世界网络具有一定的生物神经网络特性,对小世界神经网络的仿真分析可以使仿真结果更贴近真实生物的神经网络放电情况。通过构建小世界网络,进而基于Izhikevich神经元模型、短期突触可塑性调节机制和突触动力学构建了小世界神经网络,并从兴奋性神经元和抑制性神经元的放电模式、放电栅图、平均放电频率和网络整体的放电同步性四个方面进行分析。

银行系统多层网络模型及其演化

基于股票收益率间 Pearson 相关性、Spearman相关性和Kendall相关性,构建了银行系统多层网络模型,其中3种相关性分别对应于多层网络中Pearson层、Spearman层和Kendall层.根据2011—2020年期间中国上市银行数据,实证分析了银行系统多层网络结构的演化特征.结果表明:大型国有银行在整个银行系统中表现出更高的活跃度;银行间多层网络的层间度相关性呈现一定的波动性,其中Spearman层和Kendall层的相关性更高;构建的银行多层网络表现出明显的小世界特征;所有的银行节点在多层网络中的每一层中都发挥作用.研究结论揭示了银行收益率波动不同关联性之间的相依结构,对于维护银行系统稳定具有一定的理论参考价值.

基于轻小型无人机城区1∶500地形图测绘技术研究

随着无人机测绘精度的逐步提高和倾斜摄影测量技术的发展,在三维模型的基础上进行大比例尺地形图测绘已成为可能。基于轻小型无人机大疆精灵4 Pro单镜头摄影测量系统,制定城区1∶500地形图的整体技术流程,首先制作测区倾斜模型,讨论不同数量像控点对三维模型精度的影响,采用实测数据检验模型成果,在不低于3个控制点的情况下,所建模型的平面与高程点位中误差都可达到城区1∶500地形图测制精度要求。最后尝试借助清华山维EPS地理信息工作站测图软件,在三维模型的基础上采集地物要素,完成试验区1∶500地形图的测制工作。文中的研究成果可为城区1∶500地形图的测制及更新提供借鉴。

电气介数及其在电力系统关键线路识别中的应用

提出线路的电气介数并将其用于电力系统关键线路识别。该方法基于电路方程,克服了加权介数模型假设母线间潮流只沿最短路径流动的不足,能有效反映各"发电–负荷"节点对对线路的真实利用情况,其物理背景更符合电力系统特点且可考虑不同发电容量及负荷水平的影响。同时提出系统最大传输能力指标,其变化量同已有指标相结合能更全面地反映系统的故障程度。对IEEE-39系统和西北网的计算表明,高电气介数线路往往属于电网的长程连接,具有关键性地位,且电力系统在针对这些线路的攻击下更为脆弱。对蓄意攻击下系统连锁故障过程的追踪也显示最大传输能力指标的必要性,证明该模型比已有模型更为合理有效。

拓扑相关蠕虫仿真分析

从蠕虫传播的漏洞主机发现、代码传播以及代码启动3个方面提出了一个完整的数据包级拓扑相关蠕虫仿真模型,设计了基于有向图的SmallWorld拓扑结构生成算法,并通过选择性抽象在NS2上实现了一个完整的数据包级拓扑相关蠕虫仿真系统.最后,通过该仿真系统实验分析了逻辑拓扑结构和蠕虫代码启动方式对蠕虫传播的影响.仿真实验结果表明,该仿真系统可以为拓扑相关蠕虫研究提供重要的支持.