考虑分群结构稳定性的自组网无中心分群算法

为了降低路由维护开销,使无中心分群结构很好地适用于大规模自组网,最首要的任务是使分群结构尽量稳定。现有无中心分群算法尚未考虑上述问题,因此,提出一种考虑分群结构稳定性的无中心分群算法及群维护策略。该算法基于移动预测思想,综合考虑群内结构稳定性、群间结构稳定性和分群结构优化。对算法的性能和复杂度分别进行了分析,仿真结果表明：该算法比现有算法更有利于分群结构的稳定,降低了分群维护开销,进而将降低路由维护开销。     

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.     

PassCAR: A passive clustering aided routing protocol for vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) are a promising architecture for vehicle-to-vehicle communications in the transportation field. However, the frequent topology changes in VANETs create many challenges to data delivery because the vehicle velocity varies with time. Thus, designing an efficient routing protocol for stable and reliable communication is essential. Existing studies show that clustering is an elegant approach to efficient routing in a mobile environment. In particular, the passive clustering (PC) mechanism has been validated as a more efficient approach compared to traditional clustering mechanisms. However, the PC mechanism was primarily designed for mobile ad hoc networks (MANETs), and may be unsuitable for constructing a cluster structure in VANETs because it does not account for vehicle behavior and link quality. In this paper, we propose a passive clustering aided routing protocol, named PassCAR, to enhance routing performance in the one-way multi-lane highway scenario. The main goal of PassCAR is to determine suitable participants for constructing a stable and reliable cluster structure during the route discovery phase. Each candidate node self-determines its own priority to compete for a participant using the proposed multi-metric election strategy based on metrics such as node degree, expected transmission count, and link lifetime. Simulation results show that, compared with the original PC mechanism, PassCAR not only increases the successful probability of route discovery, but also selects more suitable nodes to participate in the created cluster structure. This well-constructed cluster structure significantly improves the packet delivery ratio and achieves a higher network throughput due to its preference for reliable, stable, and durable routing paths.     

Clustering in mobile ad hoc networks through neighborhood stability-based mobility prediction

Clustering for mobile ad hoc networks (MANETs) offers a kind of hierarchical organization by partitioning mobile hosts into disjoint groups of hosts (clusters). However, the problem of changing topology is recurring and the main challenge in this technique is to build stable clusters despite the host mobility. In this paper, we present a novel clustering algorithm, which guarantees longer lifetime of the clustering structure in comparison to other techniques proposed in the literature. The basis of our algorithm is a scheme that accurately predicts the mobility of each mobile host based on the stability of its neighborhood (i.e., how different is its neighborhood over time). This information is then used for creating each cluster from hosts that will remain neighbors for sufficiently long time, ensuring the formation of clusters that are highly resistant to host mobility. For estimating the future host mobility, we use provably good information theoretic techniques, which allow on-line learning of a reliable probabilistic model for the existing host mobility.     

带有重叠区域的多跳非均匀分簇路由算法

能耗作为衡量无线传感器网络性能的一项重要指标,通常将延长生命周期、均衡能耗作为网络协议重要的设计目标.针对静态、异构、非均匀分布的网络模型,设计带有重叠区域的分簇及簇内单跳、簇间多跳的路由算法 ----- OMU分簇路由算法,该算法中簇头不再作为数据转发节点,而主要用于簇内数据的接收与融合.通过综合考虑节点剩余能量、节点密度及与基站的距离进行簇头选举并进行分簇,形成簇间重叠区域,产生用于数据转发的中继节点.同时,建立簇头与中继节点轮换机制以达到节点能耗均衡的目的,并为每个节点建立能量最省的多跳数据传输路径.仿真结果表明,所设计的分簇路由算法,特别是在大规模部署的无线传感器网络中,能有效减少和均衡能量消耗.     

基于能量异构的无线传感器网络的分簇算法研究

分簇路由算法是无线传感器网络中减少能量消耗的关键技术之一，因而成为目前国际上的研究重点。考虑到大多数分簇算法都是在同构网络中提出的，对异构网络的适应性差，本文提出一种新的异构网络分簇算法，该算法采用ECS网络结构，在选举簇头时将节点刺余能量和与所在簇的质心间的距离结合起来考虑。仿真结果表明算法在延长网络生存时间上比LEACH和基于ECS结构的分簇算法有很大的提高。     

Multiagent driven dynamic clustering of vehicles in VANETs

Cluster formation in vehicular ad hoc networks (VANETs) is a challenging problem due to rapidly changing network topology and frequent network disconnections of vehicles. Dynamic clustering is a technique to form grouping of vehicles on the fly. We propose a multiagent driven dynamic clustering scheme for VANETS on a lane between two intersections by considering vehicle speed, direction, connectivity degree to other vehicles and mobility pattern. The scheme comprises of heavy-weight static and light-weight mobile agents. Initially, cluster members are identified based on vehicle's relative speed and direction for dynamic clustering. Cluster head is selected among the cluster members based on stability metric derived from connectivity degree, average speed and time to leave the road intersection. Cluster head predicts future association of cluster members based on mobility patterns. The announcement of cluster mobility pattern to all cluster members is made by cluster head. The cluster members with similar mobility pattern can reconnect with cluster head after passing an intersection of the lane. We have evaluated the performance and effectiveness of proposed scheme by comparing with an existing clustering scheme. It is observed that proposed scheme performs better than existing stable clustering scheme in terms of cluster formation time, cluster member selection time, cluster head selection time and control overheads.     

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.     

一种改进的WSN成簇算法

从保证无线传感器网络(WSN)感知覆盖性能角度出发,分析节点剩余能量、重叠感知覆盖率与簇头选择的关系,改进LEACH协议中簇头阈值选择前的信息采集过程,提出一种适用于高密度随机部署的WSN成簇算法。实验结果表明,该算法可有效保持网络感知覆盖率,从而延长网络寿命。     

常规分簇的超宽带传感网生存期的上界

考虑了n个传感节点和一个Sink组成的跳时脉冲无线电超宽带(time hopping impulse radio ultra wide band,简称TH-IR UWB)传感网,其中,n个传感节点均匀且独立地分布在正方形上.分别推导了常规分簇的密集和扩展TH-IR UWB传感网的生存期上界.常规分簇网络的簇头仅对本簇内的节点发送的数据进行融合,而对其他簇头发送的数据仅转发.研究结果表明,常规分簇密集TH-IR UWB传感网的生存期上界与节点密度(或节点数)成正比,扩展网络的生存期上界与部署区域大小(或节点数)成反比.研究结果也表明,对于密集网络,理想情形下的生存期上界比静态网络提高了(n/logn)1/2倍;而对于扩展网络,理想情形下生存期的上界比静态网络提高了(λn/log(λn))1/2倍.因此,节点或Sink在正方形内随机移动能够提高传感网生存期.     

C-DTB-CHR: centralized density- and threshold-based cluster head replacement protocols for wireless sensor networks

Advances introduced to electronics and electromagnetics leverage the production of low-cost and small wireless sensors. Wireless sensor networks (WSNs) consist of large amount of sensors equipped with radio frequency capabilities. In WSNs, data routing algorithms can be classified based on the network architecture into flat, direct, and hierarchal algorithms. In hierarchal (clustering) protocols, network is divided into sub-networks in which a node acts as a cluster head, while the rest behave as member nodes. It is worth mentioning that the sensor nodes have limited processing, storage, bandwidth, and energy capabilities. Hence, providing energy-efficient clustering protocol is a substantial research subject for many researchers. Among proposed cluster-based protocols, low-energy adaptive clustering hierarchy (LEACH) and threshold LEACH (T-LEACH), as well as modified threshold-based cluster head replacement (MT-CHR) protocols are of a great interest as of being energy optimized. In this article, we propose two protocols to cluster a WSN through taking advantage of the shortcomings of these protocols (i.e., LEACH, T-LEACH, and MT-CHR), namely centralized density- and threshold-based cluster head replacement (C-DTB-CHR) and C-DTB-CHR with adaptive data distribution (C-DTB-CHR-ADD) protocols that mainly aim at optimizing energy through minimizing the number of re-clustering operations, precluding cluster heads nodes premature death, deactivating some nodes located at dense areas from cluster’s participation, as well as reducing long-distance communications. In particular, in C-DTB-CHR protocol, some nodes belong to dense clusters are put in the sleeping mode based on a certain node active probability, thereby reducing the communications with the cluster heads and consequently prolonging the network lifetime. Moreover, the base station is concerned about setting up the required clusters and accordingly informing sensor nodes along with their corresponding active probability. C-DTB-CHR-ADD protocol provides more energy optimization through adaptive data distribution where direct and multi-hoping communications are possible. Interestingly, our simulation results show impressive improvements over what are closely related in the literature in relation to network lifetime, utilization, and network performance degradation period.     

基于K-Means和FCM的增强型Wi-Fi指纹定位策略

研究了通过数据处理算法以提高Wi-Fi指纹库室内定位性能的问题.首先采集Wi-Fi指纹样本,将其放入MySQL数据库中和R工程;其次将Wi-Fi指纹库分成若干个簇,使用K-均值聚类（K-Means）和模糊C-均值聚类（FCM）对待定位的Wi-Fi指纹进行聚类分析;最后,提出增强型的聚类策略（ECS）应用于Wi-Fi指纹匹配定位中.实验结果表明,ECS较仅使用FCM算法,其定位耗时缩短约50%-80%,且定位精度上有所改善;ECS较仅使用K-Means算法,其定位精度提高约20%-40%,且定位稳定性较强并自动更新Wi-Fi指纹库.     

基于区域划分的WSN非均匀多跳分簇路由算法

延长网络生存周期是WSN的核心问题之一.为均衡网络能耗,有效延长网络生存周期,提出一种保证区域能耗均衡的非均匀多跳分簇路由算法.通过对监测区域的等间距环形划分和等夹角扇形划分,得到同环簇大小相等、不同环簇大小由外到里依次递减的非均匀分簇方案,保证网络能耗效率最优.在簇头选取阶段,通过与距离相关的通信代价评价函数在每个子区域选择最合适的节点作为簇头,减少网络局部能耗.仿真结果表明了所提出算法的有效性.     

Link stability based multicast routing scheme in MANET

The group-oriented services are one of the primary application classes that are addressed by Mobile Ad hoc Networks (MANETs) in recent years. To support such services, multicast routing is used. Thus, there is a need to design stable and reliable multicast routing protocols for MANETs to ensure better packet delivery ratio, lower delays and reduced overheads. In this paper, we propose a mesh based multicast routing scheme that finds stable multicast path from source to receivers. The multicast mesh is constructed by using route request and route reply packets with the help of multicast routing information cache and link stability database maintained at every node. The stable paths are found based on selection of stable forwarding nodes that have high stability of link connectivity. The link stability is computed by using the parameters such as received power, distance between neighboring nodes and the link quality that is assessed using bit errors in a packet. The proposed scheme is simulated over a large number of MANET nodes with wide range of mobility and the performance is evaluated. Performance of the proposed scheme is compared with two well known mesh-based multicast routing protocols, i.e., on-demand multicast routing protocol (ODMRP) and enhanced on-demand multicast routing protocol (EODMRP). It is observed that the proposed scheme produces better packet delivery ratio, reduced packet delay and reduced overheads (such as control, memory, computation, and message overheads).     

Optimizing communication in mobile ad hoc network clustering

Mobile ad hoc networks (MANETs) are gaining popularity in recent years due to their flexibility, the proliferation of smart computing devices, and developments in wireless communications. Clustering is an important research problem for MANETs because it enables efficient utilization of resources, and must strike a delicate balance between battery energy, mobility, node degree, etc. In this paper, we consider the typical communication workload of every mobile node as well as the additional communication workload of clusterheads in MANET clustering. We propose an algorithm that optimizes communication workload, power consumption, clusterhead lifetime, and node degree. Experiment results show that our clustering approach produces effectively balanced clusters over a diverse set of random scenarios.     

An improved overlapping k-means clustering method for medical applications

Data clustering has been proven to be an effective method for discovering structure in medical datasets. The majority of clustering algorithms produce exclusive clusters meaning that each sample can belong to one cluster only. However, most real-world medical datasets have inherently overlapping information, which could be best explained by overlapping clustering methods that allow one sample belong to more than one cluster. One of the simplest and most efficient overlapping clustering methods is known as overlapping k-means (OKM), which is an extension of the traditional k-means algorithm. Being an extension of the k-means algorithm, the OKM method also suffers from sensitivity to the initial cluster centroids. In this paper, we propose a hybrid method that combines k-harmonic means and overlapping k-means algorithms (KHM-OKM) to overcome this limitation. The main idea behind KHM-OKM method is to use the output of KHM method to initialize the cluster centers of OKM method. We have tested the proposed method using FBCubed metric, which has been shown to be the most effective measure to evaluate overlapping clustering algorithms regarding homogeneity, completeness, rag bag, and cluster size-quantity tradeoff. According to results from ten publicly available medical datasets, the KHM-OKM algorithm outperforms the original OKM algorithm and can be used as an efficient method for clustering medical datasets.     

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.     

Workflow-and-Platform Aware task clustering for scientific workflow execution in Cloud environment

A scientific workflow, usually consists of a good mix of fine and coarse computational granularity tasks displaying varied runtime requirements. It has been observed that fine grained tasks incur more scheduling overhead than their execution time, when executed on widely distributed platforms. Task clustering is extensively used, in such situations, as a runtime optimization method which involves combining multiple short duration tasks into a cluster, to be scheduled on a single resource. This helps in minimizing the scheduling overheads of the fine grained tasks. However, tasks grouping curtails the degree of parallelism and hence needs to be done optimally. Though a number of task clustering techniques have been developed to reduce the impact of system overheads, they fail to identify the appropriate number of clusters at each level of workflow in order to achieve maximum possible parallelism. This work proposes a level based autonomic Workflow-and-Platform Aware (WPA) task clustering technique which takes into consideration both; the workflow structure and the underlying resource set size for task clustering. It aims to achieve maximum possible parallelism among the tasks at a level of a workflow while minimizing the system overheads and resource wastage. A comparative study with current state of the art task clustering approaches on four well-known scientific workflows show that the proposed method significantly reduces the overall workflow execution time and at the same time is able to consolidate the load onto minimum possible resources.     

一种新颖的传感器网络聚类路由协议

提出了一种能量有效的基于聚类的传感器网络路由协议—EEHCA（an Energy-Efficient Hierarchical Clustering Algorithm for wireless sensor networks）。该协议通过最小化通信能量消耗并在所有节点之间实现能量消耗负载平衡的方式,达到了延长传感器网络生存时间的目的。协议提出了一种新颖的簇首确定机制,该机制可以避免感知区域内的节点进行频繁的簇首选举,从而节约了能量。为提高传感器网络的容错性能,引入了备用簇首的概念。在簇首与基站通信方面,采用多跳传输的方式进行,从而避免了距离基站较远的簇首进行长距离通信时所造成的能量过早耗尽的问题。仿真结果表明提出的协议拥有比LEACH和HEED协议更长的网络生存时间。     

一种基于信誉评估与能量辅助约束的混合MANET分簇协议

如今在移动自组网(MANETs)中,应用更多地依赖于层次结构的支持.簇结构是MANET中应用最广泛的层次结构.然而既有的方法往往比较片面,并且缺乏可信机制的支持.提出了一种基于信誉评估与能量辅助约束的混合MANET分簇协议--HMCPRE,并深入分析了其极大独立性.HMCPRE方法充分考虑到了簇首选择所要求的能量前提和高信誉要求,有效扩展了HELLO消息;协议在每个节点上运行时计算复杂性小,迭代次数为O(1)级,簇首消息数为O(N)级,各种开销较小.模拟实验表明:HMCPRE协议能够有效抑制恶意不合作节点对网络的影响,减轻能量耗损对分簇造成的动荡,从而能形成更稳定的簇结构.