基于双层规划的港口集疏运网络优化模型

为了提高港口集疏运网络中转站选址布局的合理性并优化货流分配，本文考虑实际环境中节点失效因素和多货种货主选择偏好因素对港口集疏运网络的影响，建立了双层规划模型刻画选址配流决策和货主偏好行为之间的关系。上层决策加入中转站失效惩罚成本，下层模型建立考虑货主选择偏好的用户均衡（user-equilibrium,UE）配流模型，用于估算运输网络中各路段的均衡货流量。根据模型特点设计了基于遗传框架并融合Frank-Wolfe算法的启发式算法，通过案例分析对模型及算法的有效性进行验证。结果表明：在模型中考虑节点失效能够显著提高港口集疏运网络的可靠性，并有效降低网络惩罚成本；考虑多货种货主选择偏好能够优化集装箱中转站选址布局及货流分配。     

计及级联失效的电力信息系统脆弱性评估

电力信息系统对保障电网正常运行至关重要。电力信息系统发生故障会导致系统中负载的重新分配,由此引发的网络拥塞等问题可能导致新的设备或子系统故障,从而形成级联失效。将电力信息系统抽象为复杂网络,在此基础上,改进了传统的负载-容量模型使其满足电力信息系统数据流特点,同时将节点在物理世界中的分布反映在节点负载中;从拓扑结构和网络性能两方面建立了脆弱性评估指标,并给出了完整的脆弱性评估流程;最后,通过对测试系统进行仿真计算,分析了级联失效的过程和冗余系数对系统脆弱性的影响,验证了方法的有效性和合理性。     

水下网络移动节点分布式定位算法

随着水下网络系统的发展,水下移动节点的分布式定位技术的研究受到关注。提出了一种自组织、可容忍节点失效与测距误差、计算量和通信开销小的分布式水下节点定位算法,该算法利用自适应加权、循环三角组合测量法提高定位精度。对基于TOA测距的循环三角组合算法的定位精度进行了仿真,分析参考节点数对平均定位误差的影响,并与三边测量法的定位精度进行了比较。仿真结果表明,该算法具有较高的定位精度,对节点失效和测距误差的鲁棒性高,并且降低了水下网络对参考节点的密度要求。     

基于改进网络最大流的道路通行能力优化研究

基于图论网络最大流理论基础,建立了一种改进的网络最大流模型。首先,根据最基本的网络最大流模型,采用Ford-Fulkerson算法求解出理论最大通行能力为46人/s;其次,考虑通行的道路选择性,建立最短路模型,利用Dijkstra算法计算各个单源到各个单汇的最短路径,并通过A*算法排除与最短距离相差较大的路径,从而筛选出有效路径;然后,利用最短路模型结果加强原模型中的约束条件,利用单纯形法求解出实际最大通行能力为23人/s;最后,建立以道路扩宽成本最低为目标函数的线性规划模型对道路进行优化改造。研究结果表明,现有道路设计能够满足道路通行需求,若需提高道路通行能力且要求道路改造最小,可以适当扩宽路网中的关键道路。     

面向分布式环境的分层数据采集技术研究

目的:研究使用基于Flume的流式数据分层采集技术,以提高数据采集的可靠性与有效性,为流计算引擎提供稳定的数据流。方法:使用基于Flume的流式数据分层采集模型,并在此基础上提出分层采集进程监控方法和基于分组的双层哈希负载均衡方法。结果:从数据迁移率、吞吐量、节点平均耗时三个指标进行评价,实验结果表明,运用基于分组的双层哈希负载均衡方法能有效地均衡节点间的负载,提高数据采集效率。结论:分布式环境下,运用基于Flume的流式数据分层采集模型,所提的分层采集进程监控方法和双层哈希负载均衡方法可有效提高数据采集的稳定性与可靠性,因而可为流计算引擎提供实时、稳定的数据流。     

无线中继网络系统容量最大化中继选择机制

分析了基于用户频谱效率的中继选择算法的不足,基于最大流最小割定理,给出了系统容量最优化问题模型,分析了两跳中继网络接入链路和中继链路对系统容量的不同影响.基于链路权重因子,提出一种基于系统容量最大化的中继选择算法.对不同中继选择算法下的系统容量差异的理论与仿真分析结果表明,提出的系统容量最大化中继选择算法可以获得更优的系统容量性能,并对网络拓扑和节点个数具有良好的鲁棒性.     

时间异步无线传感器网络的目标跟踪动态成簇算法

为了解决时间异步无线传感器网络在目标跟踪时的节点协作管理和跟踪时间配准问题,提出了一种适用于时间异步条件下目标跟踪的动态成簇算法。该方法通过分析目标的无线信号强度和各节点至目标的距离来动态组建跟踪簇,然后依据目标及簇头的通信距离对簇头射频信号的覆盖区域进行功能划分,实现节点对目标的协作跟踪,同时以簇为跟踪时间的计算单元,通过簇内计时和簇间贯序传递的方法实现跟踪时间的配准。仿真实验表明,该算法进行目标跟踪时能有效均衡网络能耗,且具有较好的跟踪精度和系统鲁棒性。     

基于节点冲切破坏的板柱结构连续倒塌可靠性分析

从结构体系可靠度的角度认识钢筋混凝土板柱结构的抗连续倒塌性能,用Monte-Carlo连续成组抽样方法对以节点冲切破坏为主的板柱结构连续倒塌进行可靠度分析。在建立板柱节点功能函数的基础上,推演了抗冲切承载力分布及荷载基本变量分布和荷载效应组合的方法。通过计算初始条件下任意一个板柱节点失效的概率和一个节点失效条件下其他节点失效的条件概率,分析了不同位置板柱节点在抗连续倒塌中的重要性以及板柱结构的抗连续倒塌鲁棒性指标。结果表明:底层中柱节点冲切失效最容易引起其他节点的连续性破坏及倒塌,按我国规范设定了柱顶贯通受压钢筋的板柱结构鲁棒性指标比较高。     

大跨桥梁监测无线传感网络节点布置方法研究

为了避免无线传感网络出现“能量洞”而造成整个网络的过早死亡,提出一种新型大跨桥梁监测无线传感网络节点布置方法.首先分析了无线传感网络节点的能量消耗原理,提出以数据获取效率作为无线传感网络的性能评价指标,推导了基于数据获取效率的无线传感网络节点布置方法的理论公式,并对几种桥梁测试工况进行了无线传感网络布置仿真分析.仿真结果表明,与传统的节点均匀布置方法和传感节点不参与数据传输的网络节点布置方法相比,基于数据获取效率的无线传感网络节点布置方法能够很好地保证网络中各个节点能耗的均匀性,在相同的成本投入下获得最多的结构状态信息.基于数据获取效率的无线传感网络节点布置方法可充分利用无线传感网络中的所有节点,有效地消除“能量洞”现象.     

网络拓扑结构对IP组播效率影响的研究

研究了不同网络拓扑结构对IP组播效率的影响,给出了一种度量组播效率的方法,并进行了分析与仿真.结果表明:在随机选择组播树根节点的情况下,平均节点度越大的网络,组播效率越低;限制组播路由的通过性和适当选择组播根节点的位置能够有效提高组播效率;有源最短路径树在不同网络拓扑下的性能差异较大,并且不是一种能够适合于各种网络结构的高效组播树构建算法.     

Internet of Things Based Solutions for Transport Network Vulnerability Assessment in Intelligent Transportation Systems

Intelligent Transportation System (ITS) is essential for effective identification of vulnerable units in the transport network and its stable operation. Also, it is necessary to establish an urban transport network vulnerability assessment model with solutions based on Internet of Things (IoT). Previous research on vulnerability has no congestion effect on the peak time of urban road network. The cascading failure of links or nodes is presented by IoT monitoring system, which can collect data from a wireless sensor network in the transport environment. The IoT monitoring system collects wireless data via Vehicle-to-Infrastructure (V2I) channels to simulate key segments and their failure probability. Finally, the topological structure vulnerability index and the traffic function vulnerability index of road network are extracted from the vulnerability factors. The two indices are standardized by calculating the relative change rate, and the comprehensive index of the consequence after road network unit is in a failure state. Therefore, by calculating the failure probability of road network unit and comprehensive index of road network unit in failure state, the comprehensive vulnerability of road network can be evaluated by a risk calculation formula. In short, the IoT-based solutions to the new vulnerability assessment can help road network planning and traffic management departments to achieve the ITS goals.     

EAMTR: energy aware multi-tree routing for wireless sensor networks

IEEE 802.15.4 is the prevailing standard for low-rate wireless personal area networks. It specifies the physical layer and medium access control sub-layer. Some emerging standards such as ZigBee define the network layer on top of these lower levels to support routing and multi-hop communication. Tree routing is a favourable basis for ZigBee routing because of its simplicity and limited use of resources. However, in data collection systems that are based on spanning trees rooted at a sink node, non-optimal route selection, congestion and uneven distribution of traffic in tree routing can adversely contribute to network performance and lifetime. The imbalance in workload can result in hotspot problems and early energy depletion of specific nodes that are normally the crucial routers of the network. The authors propose a novel light-weight routing protocol, energy aware multi-tree routing (EAMTR) protocol, to balance the workload of data gathering and alleviate the hotspot and single points of failure problems for high-density sink-type networks. In this scheme, multiple trees are formed in the initialisation phase and according to network traffic, each node selects the least congested route to the root node. The results of simulation and performance evaluation of EAMTR show significant improvement in network lifetime and traffic distribution.     

Spatial econometrics models for congestion prediction with in-vehicle route guidance

The congestion dependence relationship among links using microsimulation is explored, based on data from a real road network. The work is motivated by recent innovations to improve the reliability of Dynamic Route Guidance (DRG) systems. The reliability of DRG systems can be significantly enhanced by adding a function to predict the congestion in the road network. The application of spatial econometrics modelling to congestion prediction is also explored, by using historical traffic message channel (TMC) data stored in the vehicle navigation unit. The nature of TMC data is in the form of a time series of geo-referenced congestion warning messages, which is generally collected from various traffic sources. The prediction of future congestion could be based on the previous year of TMC data. Synthetic TMC data generated by microscopic traffic simulation for the network of Coventry are used in this study. The feasibility of using spatial econometrics modelling techniques to predict congestion is explored. The results are presented at the end.     

Attacker-defender models and road network vulnerability

The reliability of road networks depends directly on their vulnerability to disruptive incidents, ranging in severity from minor disruptions to terrorist attacks. This paper presents a game theoretic approach to the analysis of road network vulnerability. The approach posits predefined disruption, attack or failure scenarios and then considers how to use the road network so as to minimize the maximum expected loss in the event of one of these scenarios coming to fruition. A mixed route strategy is adopted, meaning that the use of the road network is determined by the worst scenario probabilities. This is equivalent to risk-averse route choice. A solution algorithm suitable for use with standard traffic assignment software is presented, thereby enabling the use of electronic road navigation networks. A variant of this algorithm suitable for risk-averse assignment is developed. A numerical example relating to the central London road network is presented. The results highlight points of vulnerability in the road network. Applications of this form of network vulnerability analysis together with improved solution methods are discussed.     

Cooperative Perception Optimization Based on Self-Checking Machine Learning

In the process of spectrum perception, in order to realize accurate perception of the channel state, the method of multi-node cooperative perception can usually be used. However, the first problem to be considered is how to complete information fusion and obtain more accurate and reliable judgment results based on multi-node perception results. The ideas put forward in this paper are as follows: firstly, the perceived results of each node are obtained on the premise of limiting detection probability and false alarm probability. Then, on the one hand, the weighted fusion criterion of decision-making weight optimization of each node is realized based on a genetic algorithm, and the useless nodes also can be screened out to reduce energy loss; on the other hand, through the linear fitting ability of RBF neural network, the self-inspection of the perceptive nodes can be realized to ensure the normal operation of the perceptive work of each node. What's more, the real-time training data can be obtained by spectral segmentation technology to ensure the real-time accuracy of the optimization results. Finally, the simulation results show that this method can effectively improve the accuracy and stability of channel perception results, optimize the structure of the cooperative network and reduce energy consumption.     

车载自组织网络信息流量特性分析

为了揭示车载自组织网络动态拓扑特性,预测车载自组织网络行为和缓解网络拥堵,本文从微观角度分析了信息流量分布的一般特性,研究了在不同交通需求下车载自组织网络信息流量分布特性及信息流量与节点度值之间的关系.首先提出度、度分布指数与信息流量的关系,用复杂网络理论以静态、动态两种方式建立无容量限制的无标度网络模型,采用数值模拟实验和仿真实验方法讨论了参数变化时车载自组织网络信息流量变化规律;其次,借助非线性动力学理论研究了考虑容量限制的信息流量分布特性.研究结果表明,信息流量分布指数是无标度网络的通用特征参数,度值大的节点对网络影响较大,传播信息更快,信息流量与度值之间满足幂律分布规律;当信息流量需求增大时,Hub节点可能负担过重,许多信息流量可能会选择其他节点避免与Hub节点连通,这时较小度值的节点承担这部分信息流量.     

Robust mobility adaptive clustering scheme with support for geographic routing for vehicular ad hoc networks

There are a number of critical problems related to road safety in intelligent transportation systems (ITS) caused by increased vehicle usage, urbanisation, population growth and density, and faster rates of movements of goods and people. It is envisaged that vehicular ad hoc networks (VANETs) will bring about a substantial change to the way our road transport operates to improving road safety and traffic congestion. A major challenge in VANETs is to provide real-time transfer of information between vehicles within a highly mobile environment. The authors propose a new clustering scheme named robust mobility adaptive clustering (RMAC) to strategically enable and manage highly dynamic VANETs for future ITS. It employs a novel node precedence algorithm to adaptively identify the nearby 1-hop neighbours and select optimal clusterheads based on relative node mobility metrics of speed, locations and direction of travel. Furthermore, the zone of interest concept is introduced for optimised approach to the network structure such that each vehicular node maintains a neighbour table of nodes, beyond its communications range, that reflects the frequent changes on the network and provides prior knowledge of neighbours as they travel into new neighbourhoods. RMAC predominantly employs more reliable unicast control packets and supports geographic routing by providing accurate neighbour information crucial when making routing decisions in multi-hop geographic routing. It is shown by simulations that RMAC on IEEE802.11 ad hoc WLAN protocol is very effective in a highly dynamic VANETs environment, being robust on link failures, and having very high cluster residence times compared to the well known distributed mobility clustering scheme.     

Modelling of cluster supply network with cascading failure spread and its vulnerability analysis

In this paper, a new entropy approach is developed to study the vulnerability of cluster supply chain network during the cascading failure spread from a holistic point of view. We use the tools of complex network theory and social network analysis to obtain the network representation of cluster supply chain system and explain its cascading phenomenon. Then we build a new cascading model for cluster supply chain network, and further introduce the concept of network load entropy, to analyse and predict the dynamic behaviours of the vulnerability during the process of failure spreading. Using a cluster supply chain network in an industrial district as a case study, we employ the modelling approach to explore its vulnerability. The simulation results demonstrate that the vulnerability of cluster supply chain network with cascading failures can be identified and predicted efficiently by using the modelling approach. In addition, this work may have practical implications for reducing the cluster supply chain network vulnerability in the cascade control and defence, then obtaining its healthy evolution.     

Exact topology discovery algorithm for the capacity and delay constrained loop network

We have developed an exact model and algorithm for the delay-constrained minimum cost loop problem (DC-MCLP) of finding broadcast loops from a source node. While the traditional minimum cost loop problem (MCLP) deals with only the traffic capacity constraint served by a port of source node, the DC-MCLP deals with the mean network delay and traffic capacity constraints simultaneously. The DC-MCLP consists of finding a set of minimum cost loops to link end-user nodes to a source node satisfying the traffic requirements at end-nodes and the required mean delay of the network. In the DC-MCLP, the objective function is to minimise the total link cost. We have formulated the DC-MCLP and proposed an exact algorithm for its solution. The proposed algorithm is composed of two phases: in the first phase, it generates feasible paths to satisfy the traffic capacity constraint; in the second phase it finds the exact loop topology through matching and allocating optimal link capacity to satisfy the mean delay constraint. In addition, we have derived several properties including the memory and time complexity of the proposed algorithm. Performance evaluation shows that the proposed algorithm has good efficiency for networks with less than thirty nodes and light traffic. Our proposed algorithm can be applied to find the broadcast loops for real-time multimedia traffic     

Multicommodity Network Expansion under Elastic Demands

We analyze some issues of network design and bandwidth allocation in telecommunication systems with congestible resources. The work is closely related to network monitoring and traffic measurement functions that must be carried out on line, in order to overcome congestion caused by an unfavorable traffic pattern or by a failure. In addition to the traditional use of routing controls, our approach achieves network efficiency with capacity assignment and also imposing variable prices for the consumers. A generalized Benders decomposition method is applied to a mixed integer nonlinear programming formulation of the integrated problem of network design and operation. The method exploits the nature of the continuous subproblem, that is a large-scale convex network flow problem with demands sensitive to commodity prices. Some numerical experience suggests that the method is usefull to address both questions of global optimality and competitive pricing in such systems.