Ad Hoc网络中一种基于相关度的分布式分簇算法

无线自组网ad hoc是一种不依赖于基础设备的无线移动网络,分簇是管理ad hoc的一种较为有效的方式。本文提出ad hoc网络中一种新的分簇算法,此算法以节点间的相关度以及节点密度作为选择簇头和分簇的标准,算法在各节点分布执行,并且通过节点间消息的传递来得到最终的簇结构。实验结果表明,该算法产生的簇结构比节点度算法更均衡,也因此在应用上具有更好的表现。     

一个ad hoc网络中的簇结构模式

提出一种ad hoc网络中新的簇结构模式。采用约束特征值的概念对ad hoc网络中节点移动的约束环境进行数学抽象，定义了节点属性的数学表达式。以节点的通信能力值和状态变化值作为选择簇头节点的依据，在此基础上提出了分簇算法GM-BFS，对平面ad hoc网络进行分簇，最后使用偏移度概念实现节点在簇间的移动和动态切换。     

一种考虑障碍物的ad hoc网络分簇算法

现有的很多ad hoc网络分簇算法都没有考虑实际的物理环境因素，如地球表面的各种障碍物。而障碍物既阻碍节点移动，又限制无线传输，对分簇结果影响很大，可能会导致簇的尺寸过小，簇的数目较多，从而引入大量的通信和计算开销。结合Voronoi图，在最小ID启发式算法的基础上，提出一种考虑障碍物的分簇算法。通过设置备用节点，可以解决障碍物环境下ad hoc网络的连接性问题。最后通过实例仿真对该算法和最小ID算法进行性能比较和评价。     

分级ad-hoc网络中的一种移动节点故障诊断算法

讨论了ad—hoc网络中现有的故障诊断算法，并分析了“基于比较的故障诊断”算法由于节点重复诊断和诊断消息在整个ad-hoc网络中洪泛导致的系统开销大的问题，同时指出了该算法在诊断期间网络拓扑变化则部分节点无法诊断的缺陷．然后提出了一种“基于簇的比较诊断算法”，该算法在分级ad—hoc网络中利用簇首对簇内节点的集中控制功能优化了诊断过程，实现了诊断期间网络拓扑变化时对移动节点的诊断．仿真结果表明．与“基于比较的故障诊断”算法相比，“基于簇的比较诊断算法”具有更小的系统开销．     

基于概率度分簇的ad hoc网络实时多媒体通信方案

Ad hoc网络的移动性、高度动态拓扑使得实时多媒体传输受到严重影响，为了解决多媒体通信中的传输不稳定和丢包问题，应用Markov过程提出了基于概率度的分簇算法（PD）。该算法首先在当前时刻预测下一时刻任意两点间链路的连通性，然后选取链路的连通概率较高的节点作为簇头。仿真结果表明，PD算法成簇数目少于最高连通度算法（HD），而且簇内节点无重叠，从而有效降低了分簇结构的维护开销。由于簇成员数量并不明显多于HD，因此说明PD算法效率较高，而且没因分簇数目减少给簇首增加负担。最后在此分簇基础上，结合UDP协议，给出了一种基于概率度分簇的ad hoc网络实时多媒体通信方案，不仅使得传输稳定，而且解决了数据包失序和丢包问题。     

一种适合移动自组网的分簇算法

分级结构的Ad hoc网络可以减少路由开销,满足网络规模扩充的需要。提出了一种基于最小ID分簇算法的改进算法,根据节点移动性和电量消耗重新分配ID,然后利用分簇快速、操作简单、效率高的最小ID分簇算法进行重新分簇,仿真实验显示与传统最小ID分簇算法（LID）、最大节点度分簇算法（HD）和基于权值的分簇算法（WCA）相比,算法能使网内节点电量消耗更平衡,簇结构更稳定,能够延长网络的服务时间。     

一种应用于无线传感器网络的层次型拓扑结构生成算法

在无线传感器网络中,层次型的拓扑结构将整个网络划分为不同的簇,并通过一定机制选择簇头来负责数据转发和融合。本文在LEACH等现有层次型拓扑控制算法的基础上,借鉴ad hoc网络层次拓扑生成算法WCA的设计原理,提出一种应用于无线传感器网络的新型层次型拓扑结构生成算法(HTGA)。该算法综合考虑节点的能量和位置状况,为每个节点定义不同的权值,从中选出性能优越的节点担任簇首,同时通过设置节点度参数来确保最优的拓扑结构。仿真实验结果显示,新算法在降低能耗、延长网络生存时间以及保证监测覆盖度等方面比LEACH具有更加优良的性能。     

一种移动Adhoc网络分簇算法及性能分析

提出了一种新的Ad hoc网络分簇路由算法.该算法引入异常度的概念,根据异常度判断何时启动簇结构调整,如何使原簇中保留较多的节点,提高分簇结构稳定性.分析及实验表明,该分簇算法具有簇首的更换频率低和高稳定性的特点.     

基于频谱感知的认知Ad hoc网络分簇算法

为了提高认知Ad hoc网络频谱感知效率,解决认知Ad hoc网络分簇问题,本文提出一种基于频谱感知的认知Ad hoc网络分簇算法。通过引入检测因子,综合考虑多个主用户信号交叠与阴影衰落的情况,将认知Ad hoc网络中的次用户节点与需要检测的主用户信道建模为二部图模型,使得分簇问题简化为最大权边二部图（Constraint maximum-weight edge biclique, C-MWEB）分解问题,并设计一种贪婪算法对其求解。仿真结果表明,在多个主用户信号交叠与阴影衰落并存的情况下,相较于传统算法,本文算法分簇更为合理,具有更好的有效性与可靠性。     

一种用于MEO/LEO卫星网络管理的分簇算法

卫星网络是一种新型的无线网络形式,可以看作是一种特殊的ad hoc网络。但由于其本身具有的特殊性质,现有的无线网络管理协议不能高效地应用于卫星网络中。通过引入ad hoc网络中分簇的概念,提出了一种适用于MEO/LEO卫星网络管理的基于地理信息辅助的分簇算法。簇首及管理员由MEO卫星担任,管理代理设置在LEO卫星之上。LEO卫星轨道所处的外层空间被划分成移动的分区,根据结点的密度分区合并为簇,然后在簇内根据组合加权方式选择一个MEO结点作为簇首。该算法考虑了不同层卫星之间的相对移动性及卫星运动的规律性,明确了管理员及管理代理的设置位置,为卫星网络管理提供了一种有价值的研究方法。     

Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization

A mobile ad hoc network (MANET) is dynamic in nature and is composed of wirelessly connected nodes that perform hop-by-hop routing without the help of any fixed infrastructure. One of the important requirements of a MANET is the efficiency of energy, which increases the lifetime of the network. Several techniques have been proposed by researchers to achieve this goal and one of them is clustering in MANETs that can help in providing an energy-efficient solution. Clustering involves the selection of cluster-heads (CHs) for each cluster and fewer CHs result in greater energy efficiency as these nodes drain more power than noncluster-heads. In the literature, several techniques are available for clustering by using optimization and evolutionary techniques that provide a single solution at a time. In this paper, we propose a multi-objective solution by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in an ad hoc network as well as energy dissipation in nodes in order to provide an energy-efficient solution and reduce the network traffic. In the proposed solution, inter-cluster and intra-cluster traffic is managed by the cluster-heads. The proposed algorithm takes into consideration the degree of nodes, transmission power, and battery power consumption of the mobile nodes. The main advantage of this method is that it provides a set of solutions at a time. These solutions are achieved through optimal Pareto front. We compare the results of the proposed approach with two other well-known clustering techniques; WCA and CLPSO-based clustering by using different performance metrics. We perform extensive simulations to show that the proposed approach is an effective approach for clustering in mobile ad hoc networks environment and performs better than the other two approaches.     

基于IK-means聚类的分簇路由算法

为了均衡移动自组织网络的能耗,提高簇结构稳定性,提出了一种基于IK-means聚类的分簇路由算法(IK-CRA).首先利用IK-means算法对网络中的节点进行分簇;然后通过节点的剩余能量、邻居节点数与连通代价的比值、链路维持时间构造适应度函数,采用改进的人工蜂群算法(IABC)解决分簇路由算法中的最优簇头问题.仿真结果表明,IK-CRA算法缩短了成簇时间,降低了节点的状态变化率,减少了节点的能量消耗,延长了簇首的持续时间,提高了动态环境下簇结构的稳定性.     

A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks

Vehicular ad hoc network (VANET) is special type of mobile ad hoc networks which establish communications between adjacent vehicles and also between vehicles and roadside units. Thanks to their dynamic and fast topology changes, inter-vehicular ad hoc networks are like dynamic networks without organizations. Hence, developing a reliable routing algorithm is regarded as a notable challenge in these networks. In this paper, a clustering-based reliable routing algorithm was proposed for VANETs with reliable applications. In this way, simulated annealing was used for appropriate clustering of nodes and the parameters of node degree, coverage and ability were considered in the proposed method. For selecting cluster head, radial basis function neural network was used and a suitable fitness function with velocity and free buffer size parameters was used. Each cluster has two gateway nodes which are used as the communication interface for transmitting data from one cluster to another cluster. The simulation results indicated the efficiency of the proposed method in terms of route discovery rate and packet delivery rate.     

Robust self-stabilizing weight-based clustering algorithm

Ad hoc networks consist of wireless hosts that communicate with each other in the absence of a fixed infrastructure. Such networks cannot rely on centralized and organized network management. The clustering problem consists of partitioning network nodes into non-overlapping groups called clusters. Clusters give a hierarchical organization to the network that facilitates network management and that increases its scalability.In a weight-based clustering algorithm, the clusterheads are selected according to their weight (a node’s parameter). The higher the weight of a node, the more suitable this node is for the role of clusterhead. In ad hoc networks, the amount of bandwidth, memory space or battery power of a node could be used to determine weight values.A self-stabilizing algorithm, regardless of the initial system configuration, converges to legitimate configurations without external intervention. Due to this property, self-stabilizing algorithms tolerate transient faults and they are adaptive to any topology change.In this paper, we present a robust self-stabilizing weight-based clustering algorithm for ad hoc networks. The robustness property guarantees that, starting from an arbitrary configuration, after one asynchronous round, the network is partitioned into clusters. After that, the network stays partitioned during the convergence phase toward a legitimate configuration where the clusters verify the “ad hoc clustering properties”.     

一种基于簇的移动自组网络被动式故障比较诊断算法

针对移动自组网络中的应用层故障,讨论并分析了已有基于比较的故障诊断算法和基于簇的比较诊断算法存在的问题,提出基于簇的被动比较诊断算法。该算法基于分簇算法将移动自组网络划分为若干簇,由簇首负责本簇节点的故障诊断。在完成全网节点初始诊断后,簇内对节点的诊断无需专门诊断报文,而是在正常通信数据报文中增加诊断结果字段,基于比较模型,实现对节点状态的诊断。该算法由于无专门诊断消息,因此不会出现基于比较算法中诊断消息在整个网络中洪泛的情况,大大降低了其它算法中诊断消息所造成的系统开销。该算法还克服了网络拓扑变化频繁时已有算法存在的节点无法诊断和频繁触发诊断的问题。     

基于分簇结构的无线移动网络多播路由协议

移动自组网（MANET,Mobile Ad hoc Networks）是一种特殊的、应用前景广阔的新型移动无线网络。特有的网络特性使它在诸如灾难救助、战场、传感器网络、分布式计算等领域有着广泛的应用前景。结合在移动自组网络中应用最为广泛的分层结构——簇结构的特点,提出了一种基于簇结构的移动自组网多播波路由协议。协议通过对移动自组网进行簇划分,形成由簇头、簇间节点和簇内节点构成的分簇网络结构,并且在由簇头和簇间节点形成的虚拟骨干网基础上,对其进行回路检测和冗余剪枝算法处理,最终得到一个能满足多播要求的多播树,从而实现在移动自组网中的多播路由。通过对仿真实验结果的分析,该协议具有稳定的数据包发送率和较低的路由建立时间。