LLACA: An adaptive localized clustering algorithm for wireless ad hoc networks

Performance of ad hoc networks dramatically declines as network grows. Cluster formation in which the network hosts are hierarchically partitioned into several autonomous non-overlapping groups, based on proximity, is a promising approach to alleviate the scalability problem of ad hoc networks. In this paper, we propose a localized learning automata-based clustering algorithm for wireless ad hoc networks. The proposed clustering method is a fully distributed algorithm in which each host chooses its cluster-head based solely on local information received from neighboring hosts. The proposed algorithm can be independently localized at each host. This results in a significantly reduction in message overhead of algorithm, and allows cluster maintenance can be locally performed only where it is required. To show the performance of proposed algorithm, obtained results are compared with those of several existing clustering methods in terms of the number of clusters, control message overhead, clustering time, and load standard deviation.     

Alliance-based clustering scheme for group key management in mobile ad hoc networks

Several protocols have been proposed to deal with the group key management problem in mobile ad hoc networks (MANETs). Most of these protocols organize the network into clusters to reduce the cost of key refresh or rekeying. Rekeying constitutes a challenging issue in group key management because it must be launched whenever the constitution of the group is altered following a leave or a join operation. However, cluster maintenance may also generate significative communication overhead. So, the clustering algorithm is an important factor in the performance of any key management solution. A clustering algorithm that ensures stable clusters in spite of mobility is very appreciable in mobile ad hoc networks. In fact, all the overhead due to the traffic generated by cluster adjustments and the related rekeying procedures will be saved. As far as we know, no existing clustering algorithm takes into account self-stabilization while relying on the mobility resilience of graph alliances. In this paper, we propose a fully distributed and self-stabilizing clustering algorithm for key management in MANETs where each cluster is an alliance.     

A distributed group mobility adaptive clustering algorithm for mobile ad hoc networks

This paper proposes a distributed group mobility adaptive (DGMA) clustering algorithm for mobile ad hoc networks (MANETs) on the basis of a revised group mobility metric, linear distance based spatial dependency (LDSD), which is derived from the linear distance of a node’s movement instead of its instantaneous speed and direction. In particular, it is suitable for group mobility pattern where group partitions and mergence are prevalent behaviors of mobile groups. The proposed clustering scheme aims to form more stable clusters by prolonging cluster lifetime and reducing the clustering iterations even in highly dynamic environment. Simulation results show that the performance of the proposed framework is superior to two widely referenced clustering approaches, the Lowest-ID clustering scheme and the mobility based clustering algorithm MOBIC, in terms of average clusterhead lifetime, average resident time, average number of clusterhead changes, and average number of cluster reaffiliations.     

Eventual Clusterer: A Modular Approach to Designing Hierarchical Consensus Protocols in MANETs

This paper proposes a modular approach to the design of hierarchical consensus protocols for the mobile ad hoc network with a static and known set of hosts. A two-layer hierarchy is imposed on the network by grouping mobile hosts into clusters, each with a clusterhead. The messages from and to the hosts in the same cluster are merged/unmerged by the clusterhead so as to reduce the message cost and improve the scalability. The proposed modular approach separates the concerns of clustering hosts from achieving consensus. A clustering function, called eventual clusterer (denoted as diamC), is designed for constructing and maintaining the two-layer hierarchy. Similar to unreliable failure detectors, diamC greatly facilitates the design of hierarchical protocols by providing the fault-tolerant clustering function transparently. We propose an implementation of diamC based on the failure detector diamS. Using diamC, we design a new hierarchical consensus protocol. As shown by the performance evaluation results, the proposed consensus protocol can save both message cost and time cost. Our proposed modular design is therefore effective and can lead to efficient solutions to achieving consensus in mobile ad hoc networks.     

Mosaic-Net: a game theoretical method for selection and allocation of replicas in ad hoc networks

Due to the continuous mobility of hosts, an ad hoc network suffers from frequent disconnections. This phenomenon is undesirable when mobile hosts are accessing data from each other, and thus, data accessibility is lower than that in conventional fixed networks. Because one cannot control network disconnections, an alternative solution to this problem is to replicate data objects onto mobile hosts so that when disconnections occur, mobile hosts can still access data. In this paper, a mathematical model for data object replication in ad hoc networks is formulated. The derived model is general, flexible and adaptable to cater for various applications in ad hoc networks. We prove that this problem in general is NP-complete and propose a game theoretical technique in which players (mobile hosts) continuously compete in a non-cooperative environment to improve data accessibility by replicating data objects. The technique incorporates the access frequency from mobile hosts to each data object, the status of the network connectivity, and communication costs. In the proposed scheme, players (mobile hosts) compete through bids in a non-cooperative environment to replicate data objects that are beneficial to themselves and the system as a whole. To cater for the possibility of cartel type behavior of the players, the scheme uses the Vickrey payment protocol that leaves the players with no option than to bid in such a fashion that is beneficial to the system as a whole. The paper also identifies some useful properties of the proposed scheme and the necessary conditions of optimality. The proposed technique is extensively evaluated against some well-known ad hoc network replica allocation methods, such as: (a) randomized, (b) extended static access frequency, (c) extended dynamic access frequency and neighborhood, and (d) extended dynamic connectivity grouping. The experimental results reveal that the proposed approach outperforms the four techniques in solution quality and projects a competitive execution time.     

无线自组网中基于移动预测与功率调整的适应性分簇算法

传送功率控制是无线自组网中资源管理和控制干扰的中心技术.传统上功率控制只是用作消除信道消隐负作用的一种手段,当前观点一般认为功率控制是一种可以为单个用户提供服务质量的灵活机制.提出一种分簇算法以达到减轻网络拓扑的动态变化的同时节省电能.此提案是基于全球定位系统的(GPS).根据移动主机的历史轨迹预测它下一个最可能处在的位置,预先调整传送功率.为了最大化网络的吞吐量,算法自适应性的控制每个簇在合适的大小.在GlomoSim模拟器上仿真了提出的算法.仿真结果表明,它是无线自组网中有效的拓扑管理机制,对由高速运动主机组成的网络特别有效.     

Ant Colony Optimization based clustering methodology

In this work we consider spatial clustering problem with no a priori information. The number of clusters is unknown, and clusters may have arbitrary shapes and density differences. The proposed clustering methodology addresses several challenges of the clustering problem including solution evaluation, neighborhood construction, and data set reduction. In this context, we first introduce two objective functions, namely adjusted compactness and relative separation. Each objective function evaluates the clustering solution with respect to the local characteristics of the neighborhoods. This allows us to measure the quality of a wide range of clustering solutions without a priori information. Next, using the two objective functions we present a novel clustering methodology based on Ant Colony Optimization (ACO-C). ACO-C works in a multi-objective setting and yields a set of non-dominated solutions. ACO-C has two pre-processing steps: neighborhood construction and data set reduction. The former extracts the local characteristics of data points, whereas the latter is used for scalability. We compare the proposed methodology with other clustering approaches. The experimental results indicate that ACO-C outperforms the competing approaches. The multi-objective evaluation mechanism relative to the neighborhoods enhances the extraction of the arbitrary-shaped clusters having density variations.     

基于网络性能的计算网格主机聚类

网络主机聚类是随着网格任务调度技术发展而产生的一个新技术，基于网络性能的主机聚类算法的时间效率和结果准确性有待于进一步提高．为解决这一问题，提出了实用且高效的基于密度的计算网格主机聚类启发式算法．对该算法性能进行多角度分析和大规模仿真实验，有力地证明了该算法不仅具有较优的时间效率，而且在有效结果簇、平均变化系数和平均优势比等方面具有较好的综合性能．     

聚类邻域自适应调整的多载蚁群算法

针对基本蚁群聚类算法较长时间开销和易产生冗余聚类数目的缺陷,提出了一种基于动态邻域的多载蚁群聚类算法。算法通过邻域动态自适应调整寻找纯净的邻域,增强蚂蚁记忆体记忆纯净邻域的大小,蚂蚁之间协同交流进行多载整合相似邻域形成最终聚类结果。实验结果表明新算法能有效提高算法效率且取得较好的聚类效果。     

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.     

Nested clusters with intercluster routing

Very large scale networks have become common in distributed systems. To efficiently manage these networks, various techniques are being developed in the distributed and networking research community. In this paper, we focus on one of those techniques, network clustering, i.e., the partitioning of a system into connected subsystems. The clustering we compute is size-oriented: given a parameter K of the algorithm, we compute, as far as possible, clusters of size K. We present an algorithm to compute a binary hierarchy of nested disjoint clusters. A token browses the network and recruits nodes to its cluster. When a cluster reaches a maximal size defined by a parameter of the algorithm, it is divided when possible, and tokens are created in both of the new clusters. The new clusters are then built and divided in the same fashion. The token browsing scheme chosen is a random walk, in order to ensure local load balancing. To allow the division of clusters, a spanning tree is built for each cluster. At each division, information on how to route messages between the clusters is stored. The naming process used for the clusters, along with the information stored during each division, allows routing between any two clusters.     

Fault-resilient sensing in wireless sensor networks

Research on wireless sensor networks (WSNs) has received tremendous attention in the past few years due to their potential applications and advances in the VLSI design. In WSNs with tiny sensors, mobility of a sink may provide an energy efficient way for data dissemination. Having a mobile sink in WSN, however, creates new challenges to routing and sensor distribution modeling in the network. In this paper, based on clustering and routing optimization algorithms, we propose a new scheme called K-means and TSP-based mobility (KAT mobility). After clustering the sensor nodes, the proposed method navigates the mobile sink to traverse through the cluster centers according to the trajectory of an optimized route. The mobile sink then collects the data from sensors at the visited clusters. Simulation results have demonstrated that the proposed scheme can provide not only better energy efficiency as compared to those obtained by conventional methods which assume random waypoint for the mobile sink, but also fault-resilience in case of malfunctions of some sensors due to attacks.     

结合空间信息的模糊聚类侧扫声纳图像分割

目的 针对侧扫声纳图像具有背景复杂、噪声污染重等特点,提出一种结合空间信息的模糊聚类分割算法(FCM),以提高侧扫声纳图像的分割精度和分割速度。方法 采用结合组合邻域中值滤波的FCM算法,首先选取正十字邻域和斜十字邻域,分别计算两个邻域内的像素灰度中值;然后,结合其中的较小值,引入惩罚项,得到融合灰度信息和空间信息的灰度值;最后,对融合后的灰度图像进行FCM分割。结果 利用该算法对不同尺寸和不同目标的侧扫声纳图像进行分割,并将分割结果与其他5种聚类算法的分割结果进行比较,对比分析每种算法的分割精度和运算时间。结合中值滤波的FCM算法的分割精度和运算时间均优于传统的FCM算法和结合均值滤波的FCM算法,其中结合组合邻域中值滤波的FCM算法的运算速度较快,分割精度略高于结合传统中值滤波的FCM算法。结论 结合组合邻域中值滤波的FCM算法在对侧扫声纳图像进行分割时,具有较强的抗噪性、实时性和较强的边缘保持能力。     

一种基于地理定位信息的Ad Hoc分簇算法

Ad hoc网络的分簇机制目的是为了利用网络的分级结构来管理众多的移动设备。为了创建和维持一个当节点高速移动时仍然有效的分级结构,必须满足下面的需求:(1)分级结构中每个移动设备能够根据本地信息自动转换它的角色;(2)分级结构的管理负载要小,每个簇内的节点数目要尽可能地相同。该文提出了一个基于地理定位系统的自适应的多跳分簇机制,试验结果表明新的算法不依赖于节点的移动速度和传播范围,满足上面两个要求。     

一种PMIPv6网络中基于流的区分服务方案

PMIPv6协议是由IETF提出的基于网络的区域移动性管理解决方案,其目的在于实现无需终端参与的、基于网络的IP移动性管理.PMIPv6协议仅定义了移动管理实体如何实现终端在域内移动的过程中通信不中断,但是不提供通信的服务质量保证.针对这个问题对PMIPv6协议进行改进,提出了一种PMIPv6域内基于流的区分服务方案.为在PMIPv6城内实现区分服务,提出了通过基于逻辑隧道的业务流区分方法和业务流与逻辑隧道的绑定方法.通过基于逻辑隧道的业务流区分方法,一对区域移动管理实体之间可以建立多条逻辑隧道,以解决PMIPv6区域移动管理实体之间数据通信共享单一隧道的问题;通过业务流与逻辑隧道的绑定方法,区域移动管理实体可以根据用户的需求为不同的业务流建立具有不同服务能力和类型的逻辑隧道并将业务流与逻辑隧道绑定,以实现为业务流提供有差别的服务.基于NS2的仿真结果证明:相比PMIPv6,可以根据各种应用对于延迟、丢包、吞吐量等服务质量参数的要求,为不同的业务流提供有差别的服务,能够更好地满足不同业务对于关键服务质量参数的要求并提供一定的服务质量保证.     

基于邻域归属信息混合度量的粗糙K-Means算法

粗糙K-Means及其衍生算法在处理边界区域不确定信息时,其边界区域中的数据对象因与各类簇中心点的距离相差较小,导致难以依据距离、密度对数据点进行区分判断。提出一种新的粗糙K-Means算法,在对数据进行划分时,综合数据对象的局部密度与邻域归属信息来衡量数据点与类簇的相似性,边界数据与类簇之间的关系由其局部的空间分布所决定,使得模糊不确定信息之间的差异更明显。在人工数据集和UCI标准数据集上的实验结果表明,该算法对边界区域数据的划分具有更高的准确率。     

一种改进的移动Agent通信算法

如何实现远程Agent通信的位置透明性，保证消息不会因为目标Agent迁移而丢失，一直是移动Agent通信所面临的难题，在现有的很多移动Agent系统中都没有得到解决，作者在Mogent系统中提出的通信算法初步实现了通信的位置透明和可靠的消息传输，该文在原有处的基础上提出了一种改进的适于多种迁移和通信模式的移动Agent通信算法，进一步减少了Agent的地址注册开销和迁移受到的限制，并给出了一种避免地址欺骗攻击的解决方案。     

Clustering biomedical and gene expression datasets with kernel density and unique neighborhood set based vein detection

It is a crucial need for a clustering technique to produce high-quality clusters from biomedical and gene expression datasets without requiring any user inputs. Therefore, in this paper we present a clustering technique called KUVClust that produces high-quality clusters when applied on biomedical and gene expression datasets without requiring any user inputs. The KUVClust algorithm uses three concepts namely multivariate kernel density estimation, unique closest neighborhood set and vein-based clustering. Although these concepts are known in the literature, KUVClust combines the concepts in a novel manner to achieve high-quality clustering results. The performance of KUVClust is compared with established clustering techniques on real-world biomedical and gene expression datasets. The comparisons were evaluated in terms of three criteria (purity, entropy, and sum of squared error (SSE)). Experimental results demonstrated the superiority of the proposed technique over the existing techniques for clustering both the low dimensional biomedical and high dimensional gene expressions datasets used in the experiments.