一种基于P圈的ASON混合故障保护模型

在研究自动交换光网络(ASON)中发生节点/链路混合故障时的保护方法的基础上,设计了一种同时考虑节点故障和链路故障情况的基于P圈的保护模型,同时给出了一种保证在混合故障时可以达到100%业务恢复率的预配置P圈保护的启发式算法,并且综合考虑了实际网络中保护恢复速度和资源效率等因素,给出了具有最大P圈跳数限制的P圈配置方案.利用OPNET软件搭建了ASON仿真平台,在泛欧Cost239网络拓扑和北美NSFnet网络拓扑上,采用静态业务模型对该算法进行了仿真分析.仿真结果证明,该算法在拥有故障业务快速恢复能力的同时,相比已有算法具有更低的资源冗余度.     

基于局部网络拓扑特征的鲁棒节点定位算法

针对无线传感器网络的较大测距误差严重影响定位算法精度和鲁棒性的问题,利用节点均匀部署网络的拓扑特征,提出了一种基于局部网络拓扑特征的鲁棒节点定位算法(LFLS算法).该算法通过构建节点测距高估粗差阈值参数和测距低估粗差阈值参数,在对未知节点1跳测距数据集进行粗差识别及剔除等预处理滤波的基础上,使用高斯加权最小二乘定位算法实现节点定位.仿真结果表明,基于局部网络拓扑特征的鲁棒节点定位算法的定位精度明显优于未采用局部网络拓扑特征进行粗差预处理的加权最小二乘定位算法,其中粗差测距直接相关节点的定位精度改进尤为明显.     

基于RSVP-TE的同源故障通告在ASON中的应用

为了降低自动交换光网络(ASON)中的故障通告处理时间,改进了传统的一条连接(connection)发送一个通告(notify)消息的故障通告机制,提出了一种使通告消息与源节点相对应,一个通告消息可以为多条具有相同源节点的连接进行故障通告的新的同源故障通告机制.搭建了一个15个节点的 ASON 仿真试验平台,采用基于流量工程扩展的资源预留协议(RSVP-TE)作为信令协议,通过扩展 RSVP-TE 协议实现了同源故障通告机制,并基于该平台对该机制进行了试验分析.试验数据显示,与传统的故障通告机制相比,新的故障通告机制不仅减少了故障通告处理时间,同时减少了网络中的流量.     

基于动态虚拟权重的自动交换光网络(ASON)域间路由优化

在研究利用域到域路由协议(DDRP)实现自动交换光网络(ASON)域间路由的基础上,针对实现过程中星型拓扑汇聚策略带来的等代价路由问题提出了基于动态虚拟权重的域间路由优化方法,并在利用OPNET Modeler网络仿真软件搭建的ASON域间路由仿真平台上,对传统随机边界节点选择方式和基于动态虚拟权重选择方式进行了仿真对比,验证了所提出的域间路由优化方法具有减小路由代价、降低业务阻塞率和提高网络资源利用率等性能优势.     

传感器网络移动中继节点部署算法

针对传感器网络节点能量有限性及节点能量消耗不匀性问题,提出一种移动中继节点部署算法。首先假设网络中没有移动中继节点时,对静态节点提出一种最优路由树算法来构建数据传输路径;在此基础上再采用贪婪算法增加移动节点改善网络的拓扑结构提高路由树连通性;接着提出一种高效的分布式迭代算法,使得路由树的拓扑结构收敛于最优位置;最后进行理论分析与仿真实验,结果表明该方法具有一定理论意义与实用价值。     

基于电压灵敏度的模拟电路节点选择策略

测试节点选择问题是当前模拟电路故障诊断领域的研究热点之一。以节点电压灵敏度定义为基础,对电路中的故障状态进行模糊划分,定义测试节点上的电路故障模糊集,提出故障诊断能力指标的概念,并用其描述节点对故障的区分能力。在此基础上给出一种新的测试节点优选方法,最终实现以最少的节点数目覆盖最多的电路故障的目的,应用实例验证了新算法的有效性。     

无线传感器网络节点调度算法

提出一种用于无线传感器网络的正三角形剖分节点调度算法,该算法把网络中各个节点的感知区域剖分成正三角区域。为避免出现网络盲点,在已提出算法的基础上又提出基于邻居节点信息的正三角形剖分节点调度算法。仿真结果表明,基于邻居节点信息的正三角形剖分节点调度算法能达到较好节能的效果,可以在保证覆盖范围的基础上延长网络的生存时间。     

基于拓扑感知的时延优先数据流量优化分配算法

为解决P2P流媒体的流量优化问题,提出了一种新的数据流量优化算法.该算法根据获得的有关底层网络拓扑信息,将节点分为流量路径冲突节点和非流量路径冲突节点,并分别采用加权时间片轮转和流量枚举优化算法对流量进行时延优先的优化分配.仿真实验表明,该算法可降低进入主干网的重复数据达70％ ～ 90％,并降低时延30％.     

分布式传感器网络中基于条件后验克拉美-罗下界的被动声目标跟踪局部节点选择算法

针对分布式传感器网络下的被动声目标跟踪问题,提出了一种基于条件后验克拉美罗下界（Conditional Posterior Cramér-Rao Lower Bounds,CPCRLB）的局部传感器节点选择算法,基于被动声探测背景下的纯方位量测数据,采用粒子滤波器推导得到了CPCRLB的近似解析表达式,进而在该CPCRLB的基础上定义了节点效用贡献作为节点选择准则,结合分布式传感器网络的特点提出了一种局部节点选择方法,节点无需知道全网传感器节点的信息,而是仅利用局部节点信息来决定下一时刻节点的活动状态,从而在实现自治节点选择的同时大大减少网络通信量。通过仿真结果表明,该算法在跟踪精度、能量消耗和计算复杂度方面都表现出较好的性能。     

一种新型水下移动自组织网路由算法

目前针对移动自组织网提出的路由协议普遍存在路由发现和路由维护效率低的问题,在研究按需距离矢量(AODV)路由算法的基础上,提出了一种水下多路径选择按需距离向量算法(MAODV)。该算法采用多节点路由切换机制,在一条链路失效后不必重新启动路由发现过程,而选用备用节点构成新的链路,继续进行数据包的传输,大大节省了信息资源和带宽。计算机统计仿真表明,该MAODV方法的性能在端到端通信的平均时间延迟和数据包接收率上均优于(AODV)。有效地解决了路由协议中路由发现和路由维护效率低的问题。     

Search for all MCs in networks with unreliable nodes and arcs

A simple method is proposed to search for all minimal cutsets (MCs ) for imperfect networks reliability subject to both arc and node failures under the condition that all of the MCs in the network with perfect nodes are given in advance. The proposed method does not require re-enumeration for all of the MCs for additional node failure consideration. All of the MC candidates found in the proposed algorithm are actual MCs without any need for further verification. This algorithm is more effective than the existing algorithm in which every MC candidate is not verified as a MC. No identical MCs are found using the proposed algorithm, which does not duplicate MCs and is more efficient than the existing methods. Only simple concepts are used to implement the proposed algorithm, which makes it easier to understand and implement. With considering unreliable nodes, the proposed method is also more realistic and valuable for reliability analysis in an existing network. The correctness of the proposed algorithm will be analyzed and proven. One example is used to illustrate how all MCs are generated in a network with arc and node failures solved using the proposed algorithm.     

基于非对称往返测距的海洋无线传感网络节点定位算法

提出一种新的基于非对称往返测距的海洋无线传感网络节点定位(LMARR)算法,该算法利用节点间非对称的接收与发送测距信息的时间差,推算出节点间海水声速以及未知节点与其邻居参考节点之间的距离,将三维距离信息转换成二维,运用最小二乘法完成定位计算。与SWN和ARTL算法相比较,仿真结果表明:LMARR算法能有效地提高节点定位的精度,特别是在深度为20～120m海水声速持续变化的区域,定位精度比SWN算法提高了20%,比ARTL算法提高了33%;此外,LMARR算法还具有较高的稳定性。     

改进遗传-狼群对节点序寻优的贝叶斯网络结构算法

贝叶斯网络是数据挖掘领域的一种重要方法。针对贝叶斯网络结构学习算法寻优效率低和易陷入局部最优的问题，提出一种基于改进的混合遗传-狼群对节点序寻优的贝叶斯网络结构学习算法。该算法首先利用深度优先搜索对最大支撑树的节点进行拓扑排序；然后利用动态变异及最优交叉算子构建适用于节点序寻优的改进捕食行为，引入动态参数因子来增强算法局部寻优能力；最后与K2算法结合得到最优的贝叶斯网络结构。用3种不同大小的标准网络数据集中进行实验，结果表明，该算法收敛到较优值，寻优效率高于其它同类优化算法。     

CNR: A Cluster-Based Solution for Connectivity Restoration for Mobile WSNs

Wireless Sensor Networks (WSNs) are an integral part of the Internet of Things (IoT) and are widely used in a plethora of applications. Typically, sensor networks operate in harsh environments where human intervention is often restricted, which makes battery replacement for sensor nodes impractical. Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network. Without a connectivity restoration mechanism, node failures ultimately lead to a network partition, which affects the basic function of the sensor network. Therefore, the research community actively concentrates on addressing and solving the challenges associated with connectivity restoration in sensor networks. Since energy is a scarce resource in sensor networks, it becomes the focus of research, and researchers strive to propose new solutions that are energy efficient. The common issue that is well studied and considered is how to increase the network’s life span by solving the node failure problem and achieving efficient energy utilization. This paper introduces a Cluster-based Node Recovery (CNR) connectivity restoration mechanism based on the concept of clustering. Clustering is a well-known mechanism in sensor networks, and it is known for its energy-efficient operation and scalability. The proposed technique utilizes a distributed cluster-based approach to identify the failed nodes, while Cluster Heads (CHs) play a significant role in the restoration of connectivity. Extensive simulations were conducted to evaluate the performance of the proposed technique and compare it with the existing techniques. The simulation results show that the proposed technique efficiently addresses node failure and restores connectivity by moving fewer nodes than other existing connectivity restoration mechanisms. The proposed mechanism also yields an improved field coverage as well as a lesser number of packets exchanged as compared to existing state-of-the-art mechanisms.     

Node placement for triangular mesh generation by Monte Carlo simulation

A new approach of node placement for unstructured mesh generation is proposed. It is based on the Monte Carlo method to position nodes for triangular or tetrahedral meshes. Surface or volume geometries to be meshed are treated as atomic systems, and mesh nodes are considered as interacting particles. By minimizing system potential energy with Monte Carlo simulation, particles are placed into a near‐optimal configuration. Well‐shaped triangles or tetrahedra can then be created after connecting the nodes by constrained Delaunay triangulation or tetrahedrization. The algorithm is simple, easy to implement, and works in an almost identical way for 2D and 3D meshing. Copyright © 2005 John Wiley & Sons, Ltd.     

An Adaptive Image Calibration Algorithm for Steganalysis

In this paper, a new adaptive calibration algorithm for image steganalysis is proposed. Steganography disturbs the dependence between neighboring pixels and decreases the neighborhood node degree. Firstly, we analyzed the effect of steganography on the neighborhood node degree of cover images. Then, the calibratable pixels are marked by the analysis of neighborhood node degree. Finally, the strong correlation calibration image is constructed by revising the calibratable pixels. Experimental results reveal that compared with secondary steganography the image calibration method significantly increased the detection accuracy for LSB matching steganography on low embedding ratio. The proposed method also has a better performance against spatial steganography.     

基于曲面信息的平面织物铺覆改进渔网算法

通过分析复合材料平面织物预成形体在铺覆过程中的变形模式,提出铺覆仿真计算的织物变形基本假设。分析已存在的几种渔网铺覆算法,提出一种基于曲面信息的改进渔网算法（SIB渔网法）,该算法利用相邻节点处的曲面切向量及曲率信息来确定当前节点位置,避免了高强度的迭代计算,提高了仿真精度。研究采用非均匀有理B样条（NURBS）技术表达的复杂曲面求交算法。通过不同算法对球面、马鞍面仿真的结果对比,验证了SIB渔网法对复杂曲面铺覆计算的精确性及稳定性。     

Adaptive radial importance sampling under directional stratification

We establish radial importance sampling under directional stratification and construct its easy-to-implement algorithm for estimating the probability of failure in structural reliability analysis. The proposed algorithm is expected to run in a fully adaptive manner for averaging the increasing realizations towards the unknown stratum probability of failure, along with updating parameterized importance sampling and adjusting the allocation of computing budget only occasionally yet effectively, fully considering the decreasing stratum variances, all on a single set of replications. The formulation does not require a monotonicity condition on the radial distance in the polar coordinate system to justify a deterministic numerical procedure, such as the root finding of directional simulation in its standard form. A wide variety of numerical results were provided for illustrating the applicability and effectiveness of the proposed framework and algorithm.