移动自组织网络预测分簇算法的路由协议

针对移动自组织网络中传统分簇算法存在稳定性低、网络开销大的问题,在WCA分簇算法的基础上,提出一种带有预测机制的EWCA-MP(Efficient on-demand Weighted Clustering Algorithm using Mobility Prediction)分簇算法,该算法在簇头选择时充分考虑节点间的链路保持时间,在簇维护阶段引入模糊逻辑的概念,对Hello消息包的广播周期进行优化。并将其应用于CBRP中,提出了一种ECBRP-MP(Efficient Cluster Based Routing Protocol using Mobility Prediction)移动预测的分簇路由协议。仿真结果表明,EWCA-MP算法在簇头数目、单位时间内节点转移次数和统治集更新次数明显减少,ECBRP-MP路由协议在路由开销、分组投递率的性能得到优化。     

面向任务的无人机自组网簇首选举改进算法

针对无人机群分簇网络中由于节点高移动性和能量受限导致簇结构不稳定和簇首频繁更新的问题，提出一种基于稳定性改进的加权簇首选举算法（Stability Improved Weighted Cluster Head Selection Algorithm,SI-WCSA）。首先，根据分配的任务确定簇的规模；其次，综合考量节点的移动性、能量、节点度和距离4种因素加权选举簇首，对移动性度量指标进行改进并提出3种能量消耗速率；最后，采用基于层次分析法和熵值法的组合赋权法计算贴合场景的指标权重。仿真结果表明，该算法能选取最优簇头，优化评估指标以减少节点移动性对网络生存时间的影响，降低簇首更换次数，均衡簇内节点能量，提升网络的鲁棒性，并且组合赋权法选取的权重系数将无人机网络的生存时间增加了6%。     

一种新型双簇头分簇算法的性能研究

提出了一种新的双簇头分簇算法,该算法在单簇头分簇算法的基础上增加了一个备用簇头节点,在簇头节点能量耗尽或出现故障时,备用簇头节点能够实时升成簇头节点以维持簇稳定工作,从而减少网络重建的次数,提後高网络稳定性,仿真实验表明,双簇头分簇算法比单簇头分簇算法有更好的稳定性和公平性.     

基于遗传算法和蚁群算法的LEACH改进协议

针对无线传感器网络低功耗自适应集簇分层(Low Energy Adaptive Clustering Hierarchy, LEACH)路由协议因能耗不均衡导致节点过早死亡的问题,提出了一种基于遗传算法和蚁群算法改进的LEACH路由协议。在分簇阶段,通过遗传算法选举合理的簇头节点并根据节点的分布划分簇群;在数据传输阶段,通过蚁群算法使簇头节点尽可能选择能量充足且距离较短的路径进行数据传输。仿真结果表明,与传统的分簇路由协议LEACH和LEACH-C相比,改进算法可以使网络的能量消耗更加均衡,并延长网络的生命周期。     

高能效无线传感器网络分簇数据收集协议

提出了一种基于簇结构的数据收集协议ECDGP(Energy-efficient Cluster-based Data Gathering Protocol).ECDGP使用了一种基于置信度的分簇算法进行簇头竞选,并根据网路覆盖要求选择活动节点,通过控制分簇中活动节点数目,ECDGP减少了能耗和延长了网络生命期.仿真结果表明在能耗和网络寿命上,ECDGP大幅度优于传统的分簇路由算法.     

一种新型的 Ad Hoc 网络按需加权分簇算法

提出了一种新型的适用于 Ad Hoc 网络的按需加权分簇算法，根据节点的权值来进行簇 的划分，在计算权值时，综合考虑节点度，节点的能量和移动性等多方面因素。为了提高网络体系结构 的稳定性，减少计算和通信开销，采用按需策略来完成簇结构的维护。首先介绍了2种典型的分簇算 法，然后详细说明了新型的按需加权分簇算法并利用仿真实验对3种分簇算法进行了比较。仿真结果 表明，按需加权分簇算法的性能优于另外2种分簇算法。     

无线传感网自适应能量驱动簇头轮换算法研究

分簇结构是大规模无线传感网(WSN)的一种有效的拓扑管理方法。在这种结构下,由于簇头(Cluster Head,CH)节点的能耗速率远高于簇成员节点(Cluster Member,CM),需要做簇头轮换以平衡网络能耗。该文分析了基于能量驱动的簇头轮换策略,并提供一种基于簇头节点实时负载来估计其启动轮换的能量阈值的自适应簇头轮换算法(Adaptive Cluster Rotation Algorithm,ACRA)。仿真结果表明,与现有算法如LEACH,EDAC等比较,ACRA算法最少化簇头轮换次数,延长了网络生存时间。     

能量感知非均匀成簇路由优化算法

无线传感器网络(WSN)具有的能量有限,其能量利用效率的高低直接影响着网络的生命周期.为了提高无线传感器网络的能量利用效率,提出了一种能量感知非均匀成簇路由优化算法(Energy Awareness Unequal Clustering Routing Optimization Algorithm,EUCR).该算法通过节点在网络中所处的位置确定各节点的邻居节点,并以局部能量选举簇头,各簇头根据其邻居节点构建非均匀分簇网络.同时该算法在路由阶段考虑了簇头的剩余能量和转发代价.仿真结果表明,EUCR算法能有效提高网络的能量利用效率,并延长网络的生命周期.     

基于网格分簇的无线传感器网络节能算法

无线传感器网络中,能量消耗问题一直最受人们关注.为了节省网络能量,针对现有算法存在的冗余节点过多以及能量利用率较低等问题,在以往算法的基础上,提出了一种基于网格分簇的节能算法,即基于网格分簇的无线传感器网络节能算法EABGC(Energy-saving Algorithm Based on Grid Clustering).该算法采用虚拟网格和贪婪算法等来节约网络能量.通过仿真实验,该算法与LEACH协议相比,能减少网络的能量消耗,从而达到节能的目的.EABGC算法,能有效地降低网络消耗,实现节能效果.     

基于区域的车载自组织网络节点分簇算法研究

针对车载自组织网络复杂的应用场景,提出了一种基于区域的网络分簇算法(ZACA)。将道路环境划分为路段区域(Segment)和路口区域(Intersection)分别计算节点的链路连通时间和连接度,并结合区域位置和传统综合权重的WCA分簇算法,实现不同道路模型下簇头的推举和分簇维护。仿真证明该算法能够有效地适应车载网络环境,能提高簇的稳定性、减少分簇开销,具有更高的稳定性。     

An Improved Weighted Clustering Algorithm in MANET

The original clustering algorithms in Mobile Ad hoc Network(MANET)are firstly analyzed in this paper.Based on which,an Improved Weighted Clustering Algorithm(IWCA)is proposed.Then,the principle and steps of ouralgorithm are explained in detail,and a comparison is made between the original algorithms and our improved method inthe aspects of average cluster number,topology stability,clusterhead load balance and network lifetime.The experimentalresults show that our improved algorithm has the best performance on average.     

Transmission Range-Aware Clustering for Green Cognitive Radio Ad Hoc Networks

The absence of network infrastructure and opportunistic spectrum access in cognitive radio ad hoc networks (CRAHNs) results in connectivity and stability problems. Clustering is known as an effective technique to overcome this problem. Clustering improves network performance by implementing a logical network backbone. Therefore, how to efficiently construct this backbone among CRAHNs is of interest. In this paper, we propose a new clustering algorithm for CRAHNs. Moreover, we model a novel cluster head selection function based on the channel heterogeneity in term of transmission ranges. To the best of our knowledge, this is the first attempt to model the channel heterogeneity into the clustering formation in cognitive radio networks. Simulation results show that the performance of clustering is significantly improved by the channel heterogeneity considerations.     

An efficient routing protocol for the QoS support of large‐scale MANETs

The hierarchical routing algorithm is categorized as a kind of routing method using node clustering to create a hierarchical structure in large‐scale mobile ad hoc network (LMANET). In this paper, we proposed a new hierarchical clustering algorithm (HCAL) and a corresponded protocol for hierarchical routing in LMANET. The HCAL is designed based on a cost metric in the form of the link expiration time and node's relative degree. Correspondingly, the routing protocol for HCAL adopts a reactive protocol to control the existing cluster head (CH) nodes and handle proactive nodes to be considered as a cluster in LMANET. Hierarchical clustering algorithm jointly utilizes table‐driven and on‐demand routing by using a combined weight metric to search dominant set of nodes. This set is composed by link expiration time and node's relative degree to establish the intra/intercommunication paths in LMANET. The performance of the proposed algorithm and protocol is numerically evaluated in average end‐to‐end delay, number of CH per round, iteration count between the CHs, average CH keeping time, normalized routing overhead, and packet delivery ratio over a number of randomly generated benchmark scenarios. Furthermore, to corroborate the actual effectiveness of the HCAL algorithm, extensive performance comparisons are carried out with some state‐of‐the‐art routing algorithms, namely, Dynamic Doppler Velocity Clustering, Signal Characteristic‐Based Clustering, Dynamic Link Duration Clustering, and mobility‐based clustering algorithms.     

基于结构相似度的大规模社交网络聚类算法

针对社交网络的有向交互性和大规模特性,该文提出一种基于结构相似度的有向网络聚类算法(DirSCAN),以及相应的分布式并行算法(PDirSCAN)。考虑社交网络中节点间的有向交互性,将行为结构相似的节点聚集起来,并进行节点功能分析。针对社交网络规模巨大的特点,提出MapReduce框架下的分布式并行聚类算法,在确保聚类结果一致的前提下,提高处理性能。大量真实数据集上的实验结果表明,DirSCAN比无向网络聚类算法(SCAN)在F1上可提高2.34%的性能,并行算法PDirSCAN比DirSCAN运行速度提升1.67倍,能够有效处理大规模的有向网络聚类问题。     

Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

This paper deals with the lifetime problem in the Internet of Things. We first propose an efficient cluster‐based scheme named “Cuckoo‐search Clustering with Two‐hop Routing Tree (CC‐TRT)” to develop a two‐hop load‐balanced data aggregation routing tree in the network. CC‐TRT uses a modified energy‐aware cuckoo‐search algorithm to fairly select the best cluster head (CH) for each cluster. The applied cuckoo‐search algorithm makes the CH role to rotate between different sensors round by round. Subsequently, we extend the CC‐TRT scheme to present two methods for constructing multi‐hop data aggregation routing trees, named “Cuckoo‐search Clustering with Multi‐Hop Routing Tree (CC‐MRT)” and “Cuckoo‐search Clustering with Weighted Multi‐hop Routing Tree (CC‐WMRT).” Both CC‐MRT and CC‐WMRT rely on a two‐level structure; they not only use an energy‐aware cuckoo‐search algorithm to fairly select the best CHs but also adopt a load‐balanced high‐level routing tree to route the aggregated data of CHs to the sink node. However, CC‐WMRT slightly has a better performance thanks to its low‐level routing strategy. As an advantage, the proposed schemes balance the energy consumption among different sensors. Numerical results show the efficiency of the CC‐TRT, CC‐MRT, and CC‐WMRT algorithms in terms of the number of transmissions, remaining energy, energy consumption variance, and network lifetime.     

NEAR OPTIMAL CLUSTER-HEAD SELECTION FOR WIRELESS SENSOR NETWORKS

Clustering in wireless sensor networks is an effective way to save energy and reuse band- width. To our best knowledge, most of the clustering protocols proposed in literature are of a dynamic type, where cluster heads are selected in each period, followed by cluster formation. In this paper, a new static type clustering method called Hausdorff clustering, which is based on the location of sensor nodes as well as communication efficiency and network connectivity, is proposed. The cluster head, however, is rotated within the cluster by a fuzzy logic algorithm that optimizes the network lifetime. Simulation results show that this approach can significantly increase the lifetime of the sensor network.     

An optimal energy‐efficient clustering method in wireless sensor networks using multi‐objective genetic algorithm

In this study, an optimal method of clustering homogeneous wireless sensor networks using a multi‐objective two‐nested genetic algorithm is presented. The top level algorithm is a multi‐objective genetic algorithm (GA) whose goal is to obtain clustering schemes in which the network lifetime is optimized for different delay values. The low level GA is used in each cluster in order to get the most efficient topology for data transmission from sensor nodes to the cluster head. The presented clustering method is not restrictive, whereas existing intelligent clustering methods impose certain conditions such as performing two‐tiered clustering. A random deployed model is used to demonstrate the efficiency of the proposed algorithm. In addition, a comparison is made between the presented algorithm other GA‐based clustering methods and the Low Energy Adaptive Clustering Hierarchy protocol. The results obtained indicate that using the proposed method, the network's lifetime would be extended much more than it would be when using the other methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Stability‐aware multi‐metric clustering in mobile ad hoc networks with group mobility

Clustering can help aggregate the topology information and reduce the size of routing tables in a mobile ad hoc network (MANET). The maintenance of the cluster structure should be as stable as possible to reduce overhead and make the network topology less dynamic. Hence, stability measures the goodness of clustering. However, for a complex system like MANET, one clustering metric is far from reflecting the network dynamics. Some prior works have considered multiple metrics by combining them into one weighted sum, which suffers from intrinsic drawbacks as a scalar objective function to provide solution for multi‐objective optimization. In this paper, we propose a stability‐aware multi‐metric clustering algorithm, which can (1) achieve stable cluster structure by exploiting group mobility and (2) optimize multiple metrics with the help of a multi‐objective evolutionary algorithm (MOEA). Performance evaluation shows that our algorithm can generate a stable clustered topology and also achieve optimal solutions in small‐scale networks. For large‐scale networks, it outperforms the well‐known weighted clustering algorithm (WCA) that uses a weighted sum of multiple metrics. Copyright © 2008 John Wiley & Sons, Ltd.     

面向高动态移动自组织网络的生物启发分簇算法

分簇可以有效地提高大规模移动自组织网络的性能.但高动态的移动自组织网络具有节点移动性强、网络拓扑变化快的特点,应用传统的分簇算法会造成网络性能迅速下降,频繁的簇拓扑更新造成了簇结构的不稳定和控制开销的增加.为了解决传统分簇算法无法适应高动态的大规模移动自组织网络的问题,提出了一种基于生物启发的移动感知分簇算法,该算法对多头绒泡菌的觅食模型进行了改进,使其适用于移动自组织网络领域.由于该算法与节点的移动特性进行了结合,所以该算法可以有效地在高动态移动自组织网络中进行簇的建立与维护.实验结果表明,相较于其他传统分簇算法,本文算法提高了平均链路连接保持时间和平均簇首保持时间,使得簇结构更加稳定,提高了对高动态、大规模移动自组织网络的适应能力.