Stochastic modelling and analysis of mobility models for intelligent software defined internet of vehicles

Data traffic forwarding and network optimization is essential to effective congestion management in software-defined vehicular networks, and it is necessary for software-defined vehicle networks (SDVN). SDVN is needed to optimize connection performance and network controls in dense and sparse networks to govern data flow between nodes as effectively as possible. Intelligent software-defined internet of vehicles (iSDIoVs) has recently emerged as a potential technology for future vehicular networks. It manages the vehicular ad hoc networks systematically. The link connection of moving vehicles from the central SDN controller may fail. It impacts the efficiency and communication performance because of the lack of connection between vehicles and infrastructure (V2I). The researchers have analyzed the network performance and mobility models in a dense and sparse network to maximize network performance by iSDIoVs. By integrating heterogeneous systems such as IEEE 802.11p and cellular networks into vehicular ad-hoc networks, it is possible to reduce buffer occupancy in iSDIoV and control the mobility and delay bound analysis in V2V communication. The SDN will provide flexibility and reliability to the vehicular networks. An SDN controller manages the data flow in the vehicular network and controls the flow matching rules in the control plane. The iSDIoV and queuing models improve the response time and resource utilization and enhance the network complexity analysis for traffic management services.     

Optimized network structure and routing metric in wireless multihop ad hoc communication

Inspired by the Statistical Physics of complex networks, wireless multihop ad hoc communication networks are considered in abstracted form. Since such engineered networks are able to modify their structure via topology control, we search for optimized network structures, which maximize the end-to-end throughput performance. A modified version of betweenness centrality is introduced and shown to be very relevant for the respective modeling. The calculated optimized network structures lead to a significant increase of the end-to-end throughput. The discussion of the resulting structural properties reveals that it will be almost impossible to construct these optimized topologies in a technologically efficient distributive manner. However, the modified betweenness centrality also allows to propose a new routing metric for the end-to-end communication traffic. This approach leads to an even larger increase of throughput capacity and is easily implementable in a technologically relevant manner.     

无线认知网络中拓扑控制方法研究

作为下一代通信网络,无线认知网络已成为当前的研究热点。由于节点的移动性,无线网络拓扑结构动态变化,拓扑控制一直是无线网络的难点问题。通过借鉴移动自组织网络(MANETS)中的拓扑控制方法,提出了无线认知网络中基于博弈论和认知功能相结合的拓扑控制方法。无线认知节点能够通过主动决策调节自身节点位置,在保证网络连通性的基础上实现网络覆盖面积最大。仿真实验结果验证了方法的有效性和收敛性。     

Shuttle-run synchronization in mobile ad hoc networks

In this work, we study the collective dynamics of phase oscillators in a mobile ad hoc network whose topology changes dynamically. As the network size or the communication radius of individual oscillators increases, the topology of the ad hoc network first undergoes percolation, forming a giant cluster, and then gradually achieves global connectivity. It is shown that oscillator mobility generally enhances the coherence in such networks. Interestingly, we find a new type of phase synchronization/clustering, in which the phases of the oscillators are distributed in a certain narrow range, while the instantaneous frequencies change signs frequently, leading to shuttle-run-like motion of the oscillators in phase space. We conduct a theoretical analysis to explain the mechanism of this synchronization and obtain the critical transition point.     

Impact of mobility structure on optimization of small-world networks of mobile agents

In ad hoc wireless networking, units are connected to each other rather than to acentral, fixed, infrastructure. Constructing and maintaining such networks create severaltrade-off problems between robustness, communication speed, power consumption, etc., thatbridges engineering, computer science and the physics of complex systems. In this work, weaddress the role of mobility patterns of the agents on the optimal tuning of a small-worldtype network construction method. By this method, the network is updated periodically andheld static between the updates. We investigate the optimal updating times for differentscenarios of the movement of agents (modeling, for example, the fat-tailed trip distances,and periodicities, of human travel). We find that these mobility patterns affect the powerconsumption in non-trivial ways and discuss how these effects can best be handled.     

A Model for Tacit Communication in Collaborative Human-UAV Search-and-Rescue

Tacit communication can be exploited in human robot interaction (HRI) scenarios to achieve desirable outcomes. This paper models a particular search and rescue (SAR) scenario as a modified asymmetric rendezvous game, where limited signaling capabilities are present between the two players—rescuer and rescuee. We model our situation as a co-operative Stackelberg signaling game, where the rescuer acts as a leader in signaling its intent to the rescuee. We present an efficient game-theoretic approach to obtain the optimal signaling policy to be employed by the rescuer. We then robustify this approach to uncertainties in the rescue topology and deviations in rescuee behavior. The paper thus introduces a game-theoretic framework to model an HRI scenario with implicit communication capacity.     

非线性网络的动力学复杂性研究

综述了非线性网络的动力学复杂性研究在网络理论、实证和应用方面所取得的主要进展和重要成果;深刻揭示了复杂网络的若干复杂性特征与基本定量规律;提出和建立了网络科学的统一混合理论体系(三部曲)和网络金字塔,并引入一类广义Farey组织的网络家族,阐明网络的复杂性-简单性与多样性-普适性之间转变关系;揭示了网络的拓扑结构特征与网络的动态特性之间关系;建立具有长程连接的规则网络的部分同步理论并应用于随机耦合的时空非线性系统的同步;提出复杂网络的动力学同步与控制多种方法;提出若干提高同步能力的模型、方法和途径,如同步最优和同步优先模型、同步与网络特征量关系、权重作用、叶子节点影响等;提出复杂混沌网络的多目标控制及具有小世界和无标度拓扑的束流输运网络的束晕-混沌控制方法;提出集群系统的自适应同步模型及蜂拥控制方法;探讨网络上拥塞与路由控制、资源博弈及不同类型网络上传播的若干规律;揭示含权经济科学家合作网及其演化特点;实证研究并揭示了多层次的高科技企业网和若干社会网络的特点;提出一种复杂网络的非平衡统计方法,把宏观网络推进到微观量子网络。     

Research on the search ability of Brownian particles on networks with an adaptive mechanism

In this paper, we focus on the search ability of Brownian particles with an adaptive mechanism. In the adaptive mechanism, nodes are allowed to be able to change their own accepting probability according to their congestion states. Two searching-traffic models, the static one in which nodes have fixed accepting probability to the incoming particles and the adaptive one in which nodes have adaptive accepting probability to the incoming particles are presented for testing the adaptive mechanism. Instead of number of hops, we use the traveling time, which includes not only the number of hops for a particle to jump from the source node to the destination but also the time that the particle stays in the queues of nodes, to evaluate the search ability of Brownian particles. We apply two models to different networks. The experiment results show that the adaptive mechanism can decrease the network congestion and the traveling time of the first arriving particle. Furthermore, we investigate the influence of network topologies on the congestion of networks by addressing several main properties: degree distribution, average path length, and clustering coefficient. We show the reason why random topologies are more able to deal with congested traffic states than others. We also propose an absorption strategy to deal with the additional Brownian particles in networks. The experiment results on Barabási–Albert (BA) scale-free networks show that the absorption strategy can increase the probability of a successful search and decrease the average per-node particles overhead for our models.     

Do Co-Worker Networks Increase or Decrease Productivity Differences?

Do labor mobility and co-worker networks contribute to convergence or divergence between regions? Based on the previous literature, labor mobility contributes to knowledge transfer between firms. Therefore, mobility may contribute to decreasing productivity differences, while limited mobility sustains higher differences. The effect of co-worker networks, however, can be two-fold in this process; they transmit information about potential jobs, which may enhance the mobility of workers—even between regions—and this enhanced mobility may contribute to levelling of differences. However, if mobility between regions involves movement costs, co-worker networks may concentrate locally—possibly contributing to the persistence of regional differences. In this paper, we build an agent-based model of labor mobility across firms and regions with knowledge spillovers that reflects key empirical observations on labor markets. We analyze the impact of network information provided about potential employers in this model and find that it contributes to increasing inter-regional mobility, and subsequently, to decreasing regional differences. We also find that both the density of coworker networks, as well as their regional concentrations, decrease if network information is available.     

Planar unclustered scale-free graphs as models for technological and biological networks

Many real life networks present an average path length logarithmic with the number of nodes and a degree distribution which follows a power law. Often these networks have also a modular and self-similar structure and, in some cases — usually associated with topological restrictions — their clustering is low and they are almost planar. In this paper we introduce a family of graphs which share all these properties and are defined by two parameters. As their construction is deterministic, we obtain exact analytic expressions for relevant properties of the graphs including the degree distribution, degree correlation, diameter, and average distance, as a function of the two defining parameters. Thus, the graphs are useful to model some complex networks, in particular several families of technological and biological networks, and in the design of new practical communication algorithms in relation to their dynamical processes. They can also help understanding the underlying mechanisms that have produced their particular structure.     

Deterministic scale-free small-world networks of arbitrary order

In many real-life networks, both the scale-free distribution of degree and small-world behavior are important features. There are many random or deterministic models of networks to simulate these features separately. However, there are few models that combine the scale-free effect and small-world behavior, especially in terms of deterministic versions. What is more, all the existing deterministic algorithms running in the iterative mode generate networks with only several discrete numbers of nodes. This contradicts the purpose of creating a deterministic network model on which we can simulate some dynamical processes as widely as possible. According to these facts, this paper proposes a deterministic network generation algorithm, which can not only generate deterministic networks following a scale-free distribution of degree and small-world behavior, but also produce networks with arbitrary number of nodes. Our scheme is based on a complete binary tree, and each newly generated leaf node is further linked to its full brother and one of its direct ancestors. Analytical computation and simulation results show that the average degree of such a proposed network is less than 5, the average clustering coefficient is high (larger than 0.5, even for a network of size 2 million) and the average shortest path length increases much more slowly than logarithmic growth for the majority of small-world network models.     

Self-organized network design by link survivals and shortcuts

One of the challenges for future infrastructures is how to design a network with high efficiency and strong connectivity at low cost. We propose self-organized geographical networks beyond the vulnerable scale-free structure found in many real systems. The networks with spatially concentrated nodes emerge through link survival and path reinforcement on routing flows in a wireless environment with a constant transmission range of a node. In particular, we show that adding some shortcuts induces both the small-world effect and a significant improvement of the robustness to the same level as in the optimal bimodal networks. Such a simple universal mechanism will open prospective ways for several applications in wide-area ad hoc networks, smart grids, and urban planning.     

Neural networks for prediction of acoustical properties of polyurethane foams

This paper presents a study of neural networks for prediction of acoustical properties of polyurethane foams. The proposed neural network model of the foam uses easily measured parameters such as frequency, airflow resistivity and density to predict multiple acoustical properties including the sound absorption coefficient and the surface impedance. Such a model is quite robust in the sense that it can be used to develop models for many different classes of materials with different sets of input and output parameters. The current neural network model of the foam is empirical and provides a useful complement to the existing analytical and numerical approaches.     

Coevolution with weights of names in structured language games

We propose a coevolutionary version to investigate the naming game, a model recently introduced to describe how shared vocabulary can emerge and persist spontaneously in communication systems. We base our model on the fact that more popular names have more opportunities to be selected by agents and then spread in the population. A name’s popularity is concerned with its communication frequency, characterized by its weight coevolving with the name. A tunable parameter governs the influence of name weight. We implement this modified version on both scale-free networks and small-world networks, in which interactions proceed between paired agents by means of the reverse naming game. It is found that there exists an optimal value of the parameter that induces the fastest convergence of the population. This illustration indicates that a moderately strong influence of evolving name weight favors the rapid achievement of final consensus, but very strong influences inhibit the convergence process. The rank-distribution of the final accumulated weights of names qualitatively explains this nontrivial phenomenon. Investigations of some pertinent quantities are also provided, including the time evolution of the number of different names and the success rate, as well as the total memory of agents for different parameter values, which are helpful for better understanding the coevolutionary dynamics. Finally, we explore the scaling behavior in the convergence time and conclude a smaller scaling parameter compared to the previous naming game models.     

基于元胞自动机的无线传感网络整体行为研究

探讨自组织通信网络中局部行为与系统整体行为的关联, 对于相关系统的设计和控制具有重要应用价值. 利用二维元胞自动机模型对无线传感网络的拓扑控制过程进行模拟, 可以分析节点间局部交互作用规则对网络整体行为的影响. 研究表明, 在不同的局部演化规则作用下, 该系统呈现出复杂的时空演化现象, 发现系统整体行为空间中存在振荡、衰减、稳定等基本模式, 并且该模型可以反映系统内不同全局目标之间的折中关系, 为进一步研究无线传感网络整体行为的控制问题提供一种新的途径. 关键词： 自组织 元胞自动机 通信网络 无线传感网络     

Secured data transmissions in corporeal unmanned device to device using machine learning algorithm

Cyber–physical systems (CPS) for device-to-device (D2D) communications are gaining prominence in today’s sophisticated data transmission infrastructures. This research intends to develop a new model for UAV transmissions across distinct network nodes, which is necessary since an automatic monitoring system is required to enhance the current D2D application infrastructure. The real time significance of proposed UAV for D2D communications can be observed during data transmission state where individual data will have huge impact on maximizing the D2D security. Additionally, through the use of simulation, an exploratory persistence tool is offered for CPS networks with fully characterized energy issues. This UAV CPS paradigm is based on mobility nodes, which host concurrent systems and control algorithms. In sixth-generation networks, when there are no barriers and the collision rate is low and the connectivity is fast, the method is also feasible. Unmanned aerial vehicles (UAVs) can now cover great distances, even while encountering hazardous obstacles. When compared to the preexisting models, the simulated values for autonomous, collision, and parametric reliability are much better by an average of 87%. The proposed model, however, is shown to be highly independent and exhibits stable perceptual behaviour. The proposed UAV approach is optimal for real-time applications due to its potential for more secure operation via a variety of different communication modules.     

Enhancing synchronizability by rewiring networks

<正>According to different forms of synchronized region,complex networks are divided into typeⅠ(unbounded synchronization region) and typeⅡ(bounded synchronization region) networks.This paper presents a rewiring algorithm to enhance the synchronizability of typeⅠand typeⅡnetworks.By utilizing the algorithm for an unweighted and undirected network,a better synchronizability of network with the same number of nodes and edges can be obtained. Numerical simulations on several different network models are used to support the proposed procedure.The relationship between different topological properties of the networks and the number of rewirings are shown.It finds that the final optimized network is independent of the initial network,and becomes homogeneous.In addition the optimized networks have similar structural properties in the sense of degree,and node and edge betweenness centralities.However,they do not have similar cluster coefficients for typeⅡnetworks.The research may be useful for designing more synchronizable networks and understanding the synchronization behaviour of networks.     

Preferential survival in models of complex ad hoc networks

There has been a rich interplay in recent years between (i) empirical investigations of real-world dynamic networks, (ii) analytical modeling of the microscopic mechanisms that drive the emergence of such networks, and (iii) harnessing of these mechanisms to either manipulate existing networks, or engineer new networks for specific tasks. We continue in this vein, and study the deletion phenomenon in the web by the following two different sets of websites (each comprising more than 150,000 pages) over a one-year period. Empirical data show that there is a significant deletion component in the underlying web networks, but the deletion process is not uniform. This motivates us to introduce a new mechanism of preferential survival (PS), where nodes are removed according to the degree-dependent deletion kernel, D(k)∝k^−α, with α≥0. We use the mean-field rate equation approach to study a general dynamic model driven by Preferential Attachment (PA), Double PA (DPA), and a tunable PS (i.e., with any α>0), where c nodes (c<1) are deleted per node added to the network, and verify our predictions via large-scale simulations. One of our results shows that, unlike in the case of uniform deletion (i.e., where α=0), the PS kernel when coupled with the standard PA mechanism, can lead to heavy-tailed power-law networks even in the presence of extreme turnover in the network. Moreover, a weak DPA mechanism, coupled with PS, can help to make the network even more heavy-tailed, especially in the limit when deletion and insertion rates are almost equal, and the overall network growth is minimal. The dynamics reported in this work can be used to design and engineer stable ad hoc networks and explain the stability of the power-law exponents observed in real-world networks.     

Blocking in wavelength-routed all-optical WDM network with wavelength conversion

The blocking probability in wavelength-routed all optical networks is very important measure of performance of the network, which can be affected by many factors such as network topology, traffic load, number of links, algorithms employed and whether wavelength conversion is available or not. In this paper, we have proposed a mathematical model to reduce the blocking probability of the WDM optical network for wavelength-convertible networks. The model can be used to evaluate the blocking performance of any network topology also it can be useful to improve its blocking performance of the given network topology. The blocking probability variation of the network for a particular load (per link) has been studied based on the load variation and total number of wavelengths used in the network. This model gives good results for high load (per link).     

基于Sierpinski分形垫的确定性复杂网络演化模型研究

近年来,人们发现大量真实网络都表现出小世界和无尺度的特性,由此复杂网络演化模型成为学术界研究的热点问题.本文基于Sierpinski分形垫,通过迭代的方式构造了两个确定性增长的复杂网络模型,即小世界网络模型(S-DSWN)和无尺度网络模型(S-DSFN);其次,给出了确定性网络模型的迭代生成算法,解析计算了其主要拓扑特性,结果表明两个网络模型在度分布、集聚系数和网络直径等结构特性方面与许多现实网络相符合;最后,提出了一个确定性的统一模型(S-DUM),将S-DSWN与S-DSFN纳入到一个框架之下,为复杂网络的相关研究提供理论基础.特别地,发现这些网络模型都是极大平面图.