• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.6
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• pp.1173-1178
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• 2012

In the Cluster-Based Routing Protocol (CBRP) a cluster header in each cluster should be elected. The cluster headers consume energy much more than other nodes because they manage and operate all of mobile nodes in their cluster. The traditional CBRP elects a cluster header without considering the remaining electric energy of each node. So, there exists problems that the cluster header has short average lifetime, and another cluster header should be elected again frequently. In this paper, we propose the improved protocol which prolongs the lifetime of the cluster header and enhances the stability of the path. In order to achieve this, when a cluster header is elected in a cluster, the remaining electric energies of all the nodes are compared with one another, and the node with the highest energy is elected as the cluster header.

• The KIPS Transactions:PartC
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• v.13C no.6 s.109
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• pp.757-766
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• 2006

Mobile Ad-hoc Network(MANET) is an autonomous network consisted of mobile hosts. A considerable number of studies have been conducted on the MANET with studies of ubiquitous computing. Several studies have been made on the clustering schemes which manage network hierarchically to Improve flat architecture of MANET. But the conventional schemes have the lack of multi-hop clustering and load balancing. This paper proposes a clustering scheme to support multi-hop clustering and to consider load balancing between cluster heads. We define the split of clusters and states of cluster, and propose join, merge, divide, and election of cluster head schemes for load balancing of between cluster heads

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.3
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• pp.79-99
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• 2020

The purpose of this study is to investigate the model of cluster analysis techniques for grouping public libraries and analyze their characteristics. Statistical data of public libraries of the National Library Statistics System were used, and three models of cluster analysis were applied. As a result of the study, cluster analysis was conducted based on the size of public libraries, and it was largely divided into two clusters. The size of the cluster was largely skewed to one side. For grouping based on size, the ward method of hierarchical cluster analysis and the k-means cluster analysis model were suitable. Three suggestions were presented as implications of the grouping method of public libraries. First, it is necessary to collect library service-related data in addition to statistical data. Second, an analysis model suitable for the data set to be analyzed must be applied. Third, it is necessary to study the possibility of using cluster analysis techniques in various fields other than library grouping.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.801-812
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• 2002

On-line reorganization in a shared nothing database cluster is crucial to the performance of the database system in a dynamic environment like WWW where the number of users grows rapidly and changing access patterns may exhibit high skew. In the existing method of on-line reorganization have a drawback that needs excessive data migrations in case more than two nodes within a cluster have overload at the same time. In this paper, we propose an advanced B$^{+}$ tree based on-line reorganization method that solves data skew on multi-nodes. Our method facilitates fast and efficient data migration by including spare nodes that are added to cluster through on-line scaling. Also we apply CSB$^{+}$ tree (Cache Sensitive B$^{+}$ tree) to our method instead of B$^{+}$ tree for fast select and update queries. We conducted performance study and implemented the method on Ultra Fault-Tolerant Database Cluster developed for high scalability and availability. Empirical results demonstrate that our proposed method is indeed effective and fast than the existing method. method.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.105-113
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• 2009

The previous key management methods are not appropriate for secure data communication in cluster-based routing scheme. Because cluster heads are elected in every round and communicate with the member nodes for authentication and share-key establishment phase in the cluster. In addition, there are not considered to mobility of nodes in previous key management mechanisms. In this paper, we propose the secure and effective key management mechanisim in the cluster-based routing scheme that if there are no share keys between cluster head and its nodes, we create the cluster key using authentication with base station or trust autentication and exchange the their information for a round.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.29-35
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• 2021

In this paper, we propose a method that combines KNN(K-Nearest Neighbor), Local Map Classification and Bayes Filter as a way to increase the accuracy of location positioning. First, in this technique, Local Map Classification divides the actual map into several clusters, and then classifies the clusters by KNN. And posterior probability is calculated through the probability of each cluster acquired by Bayes Filter. With this posterior probability, the cluster where the robot is located is searched. For performance evaluation, the results of location positioning obtained by applying KNN, Local Map Classification, and Bayes Filter were analyzed. As a result of the analysis, it was confirmed that even if the RSSI signal changes, the location information is fixed to one cluster, and the accuracy of location positioning increases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.435-437
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• 2016

Hadoop Distributed File System(HDFS) is a file system for distributed processing of big data by replicating data to distributed data nodes. HDFS cluster shows a great scalability up to thousands of nodes, but it assumes a exclusive node cluster with numerous nodes for the big data processing. Various operational-purpose worker systems used by office are hardly considered as a part of cluster. This paper discusses this problem and proposes a dynamic cluster management technique to increase storage capability and analytic performance of hadoop cluster. The propsed technique can add legacy systems to the cluster and can remove them from the cluster dynamically depending on their availability.

• The Journal of the Acoustical Society of Korea
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• v.33 no.5
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• pp.341-350
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• 2014

In this paper, an energy efficient clustering scheme using self organization method is proposed. The proposed scheme selects a cluster head considering not only the number of neighbor nodes but also the residual battery amount. In addition, the network life time is extended by re-selecting the cluster heads only in case the current cluster head's residual energy falls down below a certain threshold level. Accordingly, the energy consumption is evenly distributed over the entire network nodes. The cluster head delivers the collected data from member nodes to a Sink node in a way of multi-hop relaying. In order to evaluate the proposed scheme, we run computer simulation in terms of the total residual amount of battery, the number of alive nodes after a certain amount of time, the accumulated energy cost for network configuration, and the deviation of energy consumption of all nodes, comparing with LEACH which is one of the most popular network clustering schemes. Numerical results show that the proposed scheme has twice network life-time of LEACH scheme and has much more evenly distributed energy consumption over the entire network.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.169-179
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• 2021

This study shows that the two-stage k-means clustering method can improve prediction performance by predicting the stock price, To this end, this study introduces the two-stage k-means clustering algorithm and tests the prediction performance through comparison with various machine learning techniques. It selects the cluster close to the prediction target obtained from the k-means clustering, and reapplies the k-means clustering method to the cluster to search for a cluster closer to the actual value. As a result, the predicted value of this method is shown to be closer to the actual stock price than the predicted values of other machine learning techniques. Furthermore, it shows a relatively stable predicted value despite the use of a relatively small cluster. Accordingly, this method can simultaneously improve the accuracy and stability of prediction, and it can be considered as the new clustering method useful for small data. In the future, developing the two-stage k-means clustering is required for the large-scale data application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.11
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• pp.2404-2410
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• 2011

This paper describes the design overview of cluster file system $SANique^{TM}$ based on SAN(Storage Area Network) environment. The design issues and problems of the conventional global buffer manager are also illustrated under a large set of clustered computing hosts. We propose the efficient global buffer management method that provides the more scalability and availability. In our proposed global buffer management method, we reuse the maintained list of lock information from our cluster lock manager. The global buffer manger can easily find and determine the location of requested data block cache based on that lock information. We present the pseudo code of the global buffer manager and illustration of global cache operation in cluster environment.