Weight Based Clustering Algorithm Research Articles

One shot alpha numeric weight based clustering algorithm with user threshold

<p>Information Retrieval from Files and data bases like data sources is a major issue now days. After Information Retrieval clustering is also a one of the important things. In the market so many clustering algorithms were available. But choosing of the clustering algorithm depends on the user requirements. This paper addresses the study of agglomerative approach for different constraints or metrics or user preferences like Number of levels in the clustering process, number of clusters that should be generated at each level and range of the attributes at each level for doing the clustering for the given data set. In brief overview we discuss the agglomerative approach for clustering algorithm with their user preferences.</p>

An intelligent AODV routing with energy efficient weight based clustering algorithm (EEWCA) in wireless Ad hoc network (WANET)

An intelligent AODV routing with energy efficient weight based clustering algorithm (EEWCA) in wireless Ad hoc network (WANET)

Weight-Based Clustering Algorithm for Military Vehicles Communication in VANET

In vehicular ad-hoc network (VANET), every vehicle node indicates a mobile node and it acts as a transmitter, receiver and router for the delivery of the information. VANET is a subgroup of mobile ad-hoc network (MANET) and is related to the dynamic topology. Dynamic network scenarios are more challenging issues as compared to MANET topologies, so finding a suitable algorithm for all VANET applications is the major challenge for the researchers. Routing protocols in VANET are divided into six parts i.e., cluster-based, geocast-based, topology-based, position-based, and broadcast-based. Autonomous robots and unmanned military vehicles (UMVs) become part of the advanced warfare strategy to execute dangerous war field operations and military combat missions. The military vehicles (MVs) transfer information to each other in order to achieve required military tasks collectively. In the proposed work, rhombus shaped area is divided into multiple clusters using a weight-based clustering algorithm for transmitting the event information to the vehicles. Intersection clustering with rhombus shaped area which are very effective for clustering. To choose cluster head (CH), the proposed method has used two weighted metrics, one is real time average speed and the other parameter is degree. This work is useful for choosing right CH in the network. Each vehicle in the same cluster transmits the data to the CH instead of broadcasting it. The simulation has been done in the SUMO and NETSIM simulator, which shows the network performance for the different protocols like Ad-hoc on-demand distance vector (AODV), dynamic source routing (DSR) in terms of packet delivery ratio, throughput, delay, overhead transmission, mean and standard deviation.

A Weight Based Clustering Algorithm for Internet of Vehicles

Owing to the rapid growth in networking field in the recent few years, Internet of vehicles (IoV) has become one of the vast-growing networks, according to the high number of interacted connected nodes. The emergence of the new concept of Internet of Things (IoT) has given vehicles the ability to connect to everything anywhere and anytime. Even so, the increasing number of connected nodes such as vehicles, road sides, and smart phones causes several problems like network congestion that obstructs the quality of service of network. In case of an emergency situation, time is a critical factor to broadcasted messages on network, where the process has to be done as fast as possible to prevent disastrous consequences. Moreover, the high dynamism of vehicles drives routing process to be a very challenging task. Clustering algorithms are the commonly employed techniques to solve these problems. The key purpose of this paper is to propose an efficient mechanism to make IoV network more manageable and stable. In this paper, we propose a new weight-based clustering algorithm using safety, density and speed metrics. The proposed solution was verified and compared with the recent proposed works in this field (MADCCA and CAVDO) with the use of NS3, SUMO and MOVE simulation tools. Simulation results confirm the superiority of our algorithm by showing that our schema achieves better nodes connectivity and clusters stability than the other protocols.

Workload Mining in Cloud Computing using Extended Cloud Dempster–Shafer Theory (ECDST)

Cloud computing is a growing technology where the resources are provided as a service on demand basis. The services offered are Infrastructure as a Service, Platform as a Service, Software as a Service, Network as a Service etc., Based on the requests or the workloads received from the customer side, the resources are fairly allocated to the cloud customers to complete their jobs in time. As there exists huge volume of resources in cloud computing, plenty of workloads from various users are submitted to the cloud workload analyzer. Identifying and analyzing the huge volume of workloads in the cloud computing environment within a particular time is found to be an important and highly complexity. Hence this paper proposes an Extended Cloud Dempster–Shafer Theory based clustering algorithm for identifying, analyzing, classifying and clustering the workloads efficiently. The experimental result demonstrates that the proposed Extended Cloud Dempster–Shafer Theory based clustering algorithm performs clustering accurately and also reduces the execution time of cloud workloads efficiently by comparing its performance with QoS attribute’s weight based clustering algorithm.

An Enhanced-Node Feature Based Clustering Algorithm for MANET (Mobile Ad-hoc Network)

In mobile Ad-hoc Network cluster stability is considered as a very serious issue. Due to the frequent failure of the network it may reduce the stability of the cluster. In re-clustering and re-election of Cluster Head (CH) higher energy is required, which ultimately reduces the overall network performance. To resolve the cluster stability problems, Weight Based Clustering algorithm is used often. In this paper, a new weight based algorithm called Enhanced-Node Feature Based Clustering Algorithm (ENFBCA) is proposed, which uses the following parameters for cluster head selection process mainly Link Estimate Time, Degree of the node, Node Closeness, Residual Energy & Trust value. This algorithm reduces the End-to-End delay, enhances the Network Lifetime and improves the quality of service (QOS) in MANETs. Simulation results show that Enhanced-Node Feature Based Clustering Algorithm (ENFBCA) performs better in comparison to Node Quality Clustering Algorithm (NQCA) and Weight Based Clustering algorithm (WCA).

Comparative study of clustering algorithms for MANETs

Clustering algorithm in Mobile Ad Hoc Networks is considered as the suitable technique of routing. Endurance of clusterhead in a cluster for data sending improves the working in terms of established cluster composition, concentrated routing overhead and some degree of power utilization. On the other hand, due to the movement of clusterhead, common changes in clusterhead increases clustering overhead in terms of re-election of clusterhead within the current cluster. The constancy of the cluster extremely dependents on constancy of the clusterhead and hence extraordinary care should be taken during clusterhead selection and maintenance phase so that an efficient stable clusterhead is elected for longer time. In this manuscript we have carried out performance analysis of Novel Weight Based Clustering algorithm (WBC), An Optimized Stable Clustering Algorithm (OSCA) and PBWCA (Priority Based Weighted Clustering Algorithm) clustering algorithms first and foremost paying attention on reducing frequent clusterhead changing procedure. These algorithms help in reducing the frequent clusterhead changes, communication overhead and increase of broadcast in the network. The algorithm works on node degree and battery power of mobile nodes to calculate node priority. PB-WCA also focuses on reducing the cluster reformation, clusterhead re-selection and clusterhead changes.

Enhanced weight-based clustering algorithm to provide reliable delivery for VANET safety applications.

A vehicular ad hoc network (VANET) is an emerging and promising wireless technology aimed to improve traffic safety and provide comfort to road users. However, the high mobility of vehicles and frequent topology changes pose a considerable challenge to the reliable delivery of safety applications. Clustering is one of the control techniques used in VANET to make the frequent topology changes less dynamic. Nevertheless, research has shown that most of the existing clustering algorithms focus on cluster head (CH) election with very few addressing other critical issues such as cluster formation and maintenance. This has led to unstable clusters which could affect the timely delivery of safety applications. In this study, enhanced weight-based clustering algorithm (EWCA) was developed to address these challenges. We considered any vehicle moving on the same road segment with the same road ID and within the transmission range of its neighbour to be suitable for the cluster formation process. This was attributed to the fact that all safety messages are expected to be shared among the vehicles within the vicinity irrespective of their relative speedto avoid any hazardous situation. To elect a CH, we identified some metrics on the basis of the vehicle mobility information. Each vehicle was associated with a predefined weight value based on its relevance. A vehicle with the highest weight value was elected as the primary cluster head (PCH). We also introduced a secondary cluster head (SeCH) as a backup to the PCH to improve the cluster stability. SeCH took over the leadership whenever the PCH was not suitable for continuing with the leadership. The simulation results of the proposed approach showed a better performance with an increase of approximately40%– 45% in the cluster stability when compared with the existing approaches. Similarly, cluster formation messages were significantly minimized, hence reducing the communication overhead to the system and improving the reliable delivery of the safety applications.

CHEM-WCA Cluster Head Election Method using Weight based Clustering Algorithm

CHEM-WCA Cluster Head Election Method using Weight based Clustering Algorithm

Trust Factor Based Secure Congestion Control For Vanet

Weight-based clustering algorithm in ad hoc network is an on demand clustering algorithm for multi-hop packet radio network. These types of networks are ad hoc networks and dynamic in nature due to mobility of nodes. Clustering in mobile ad hoc network can be defined as various partitions into various groups. It is an important concept of VANNET, because clustering makes it possible to guarantee of system performance, such as throughput delay and also security issues. So in the algorithm they select the cluster head whose performance is well and formation of cluster in ad hoc network. The cluster head is elected based on weight factor, so it is called weighted clustering algorithm. Weighted clustering algorithm is basically appropriate cluster selection in wireless adhoc network where it is necessary to provide robustness in face of topological changes caused by node motion, node failure and node insertion or node removal. The WCA select the cluster head which has lowest weight among the nodes and some other factor also consider the election for cluster head. The cluster head, form a dominant set in the network, determine the topology and its stability. The weighted clustering algorithm takes into consideration the ideal degree, transmission power, mobility and battery power of nodes. Traffic efficiency applications are becoming increasingly popular over the road networks in the last few years. This type of applications aims mainly at increasing the traffic fluency over the road network, which minimizes the travel time of each vehicle towards its targeted destinations. The Vehicular Ad-Hoc Networks (VANETs) technology has been utilized to design these applications. Communications between vehicles, V2V, and between vehicles and installed Road Side Units (RSUs), V2I, helped designing these applications. Malicious, selfish and intruder drivers can take advantages of other cooperative drivers and use their trust. a Secure Congestion control protocol. This protocol aims to guarantee integrity and authenticity of transmitted data. It is designed to provide the security requirements of traffic efficiency protocols that have been proposed using the technology of VANETs. SCOOL also aims to preserve the privacy of the cooperative vehicles and drivers. The main contribution of work is new strategy for clustering a wireless ad hoc network and improvement in WCA. Author derived a simple stability model and a load balancing clustering scheme. They showed that the algorithm outperform in term of cluster formation and stability. Main idea of approach is to avoid cluster re-election and reduce the computation and communication cost. The improved weighted clustering algorithm, the goals of algorithm are maintaining stable clustering structure, minimizing the overhead for clustering set up, maximize lifetime of nodes in the system and achieving good performance. Keyword - Weight-based clustering, SCOOL, WCA, VANETs.

A novel weight based clustering algorithm for routing in MANET

Cluster based routing in Mobile AdHoc Networks are considered one of the convenient method of routing. Existence of Cluster Head (CH) in a group of nodes for data forwarding improves the performance of routing in terms of routing overhead and power consumption. However, due to the movement of CH and frequent change in cluster members, cluster reformation is required and increases cluster formation overhead. The stability of the cluster highly dependent of stability of the CH and hence during CH selection special care should be taken so that the cluster head survives for longer time. In this paper a method of cluster formation is proposed which will take into account two most vital factor node degree and bandwidth requirement for construction of the cluster and selection of the cluster head. Further, when two clusters come closer to each other they merge and form a single cluster. In such case out of two CHs one has to withdraw the role and other will take over. A new mechanism of merging two clusters is also proposed in the paper. We call this method as an Improved Cluster Maintenance Scheme and primarily focused on minimizing CH changing process in order to enhance the performance. The stated method makes cluster more stable, and minimizes packet loss. The proposed algorithm is simulated in ns-2 and compared with Least Cluster head Change (LCC) and CBRP. Our algorithm shows better behavior in terms of number of clusterhead changes or number of cluster member changes.

English

Adhoc clustering in MANET is to divide mobile nodes into different virtual groups. Mobile nodes are allocated geographically adjacent in the cluster. Clustering is done by some rule specific in the network. In this paper, we have concentrated to modify weight based clustering algorithm to improve the performance in this wireless technology. By slight improvement in existing weight based clustering algorithm [1] reduction in cluster as well as cluster head can be observed through experiment. Proposed Algorithm specifies relaxing the weight criteria for isolated cluster head nodes in range of other cluster head as well as reconsidering the nodes in the range which is already participated in cluster formation. Reconsidering the participated node again in clustering formation could help in creation of better cluster, forming better routes as well as in conserving the cluster head energy.

Cluster Head Election Approach based on Weighted Clustering Algorithm for MANET

The mobile ad hoc network is self-configurable network technology where not any fixed infrastructure is available. Due to frequent mobility and dynamic topology the network suffers from the frequent path breaks and the connectivity issues. The clustering techniques are utilized to improve the connectivity and scalability of network. Therefore a new weight based clustering algorithm is developed using the remain energy, signal to noise ratio, mobility and connectivity for better performance in a cluster. The implementation and the comparative performance study are performed in network simulator 2 platforms. The performance studies of the technique are given in terms of packet delivery ratio, remaining energy, end to end delay, throughput and overhead in network. The obtained results demonstrate the proposed technique improves the reliability of cluster.

Typical Genomic Framework on Disease Analysis

The challenging and major role of the doctor in human life is to predict as well as diagnose the disease which has got infected in the human body. This typical genomic framework on disease analysis algorithm is designed to store and drive each and every gene characteristics like shape, weight, location and normal growth culture. Whenever the disease report is feed into this data mining algorithm triggers the similarity test built upon the data mining classification rules. A gene is usually comprised of hundreds of individual nucleotides arranged in a particular order. There are almost an unlimited number of ways that the nucleotides can be ordered and sequenced to form distinct genes. The algorithm delivers the difference between diseased and healthy status shall guide us to conclude the disease severity, stage and its nature. This powerful Typical Genomic Framework on Disease Analysis (TGFDA) algorithm is built to deliver instant result over Very Large Database using density and weight based Clustering Algorithm. .

A Hybrid Weight-Based Clustering Algorithm for Wireless Sensor Networks

Clustering in wireless sensor network (WSN) is an efficient way to structure and organize the network. The cluster head (CH) forms dominant set in the network responsible for the creation of clusters, maintenance of the topology and data aggregation. A cluster head manages the resource allocation to all the nodes belonging to its cluster. In this paper, we propose a novel distributed clustering approach called Hybrid Weight-based Clustering Algorithm (HWCA). HWCA considers the neighborhood, the distance from the base station combined with the consumed energy as a hybrid metric to elect cluster head. The time required to identify the cluster head does not depend on the number of node and can be computed in a finite number of iterations. Our solution also aims to provide better performance such as maximizing the life time, reducing the number of lost frames in order to satisfy application requirements. Simulation results show that HWCA improves the network lifetime and reduces the number of lost frames compared with other similar approaches.

무선 센서 네트워크에서 콘텐츠 스트리밍을 위한 전송율 우선순위 기반 트래픽제어

무선 센서 네트워크에서 트래픽과 혼잡제어는 처리율과 QoS (Quality of Service)를 결정하는 중요한 파라미터이다. 본 논문에서는 무선 센서 네트워크에서 디지털 콘텐츠 스트리밍 서비스를 위한 전송율 우선순위 기반의 트래픽 제어 기법을 제안한다. 제안된 기법에서 전송율 우선순위는 버스트 시간과 크기에 따른 실시간 트래픽과 비실시간 트래픽에 의해 결정된다. 전송율 기반 우선순위는 지연을 줄이고 신뢰성을 향상시킴으로서 스트림이 요청될 때 트래픽이 효율적으로 제어되게 된다. 트래픽 제어는 트래픽 탐지 과정, 트래픽 공지, 그리고 트래픽 제어 조절 과정을 거쳐 스트리밍 서비스가 차별화 되도록 하였다. 시뮬레이션 결과 제안된 기법이 CCF (Congestion Control and Fairness)기법, WCA (Weight-based Clustering Algorithm)기법에 비해서 전송지연, 패킷손실, 그리고 처리율에 대한 성능이 효율적임을 보였다. Traffic and congestion control in the wireless sensor network is an important parameter that decides the throughput and QoS (Quality of Service). This paper proposes a transmission rate priority-based traffic control scheme to serve digital contents streaming in wireless sensor networks. In this paper, priority for transmission rate decides on the real-time traffic and non-real-time with burst time and length. This transmission rate-based priority creates low latency and high reliability so that traffic can be efficiently controlled when needed. Traffic control in this paper performs the service differentiation via traffic detection process, traffic notification process and traffic adjustment. The simulation results show that the proposed scheme achieves improved performance in delay rate, packet loss rate and throughput compared with those of other existing CCF and WCA.

An enhanced clustering algorithm for mobile ad hoc networks to improve the throughput

Mobile ad hoc networks (MANETs) consist of a number of wireless hosts that forms network without a fixed infrastructure. Clustering plays a vital role for MANETs in managing the resources. Clustering involves two processes such as cluster head election and cluster maintenance. Since cluster head ensures the network performance, it is important to elect the desired cluster heads. In this paper, an enhanced weight-based clustering algorithm using a mobility prediction scheme (EWCAMP) for finding the desired cluster head is proposed. In the cluster maintaining phase the proposed algorithm defines the admission criteria for newly arriving nodes, which helps the nodes in joining the correct cluster. The goals of the proposed algorithm are reducing the number of clusters and re-affiliation of nodes. We have evaluated the performance of the proposed algorithm using network simulator (NS2). The simulation results demonstrate the superior performance of the proposed algorithm.

SWBCA: Stability-oriented weight-based clustering algorithm for VANETs

SWBCA: Stability-oriented weight-based clustering algorithm for VANETs

A novel weighted clustering algorithm in mobile ad hoc networks using discrete particle swarm optimization (DPSOWCA)

AbstractIn this paper, a novel weighted clustering algorithm in mobile ad hoc networks using discrete particle swarm optimization (DPSOWCA) is proposed. The proposed algorithm shows how discrete particle swarm optimization can be useful in enhancing the performance of clustering algorithms in mobile ad hoc networks. Consequently, it results in the minimum number of clusters and hence minimum cluster heads. The goals of the algorithm are to minimize the number of cluster heads, to enhance network stability, to maximize network lifetime, and to achieve good end‐to‐end performance. Analysis and simulation of the algorithm have been implemented and the validity of the algorithm has been proved. Results show that the proposed algorithm performs better than the existing weight‐based clustering algorithm and adapts to different kinds of network conditions. Copyright © 2009 John Wiley & Sons, Ltd.

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”.