• 제목/요약/키워드: Hierarchical Cluster Tree

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HRKT: A Hierarchical Route Key Tree based Group Key Management for Wireless Sensor Networks

  • Jiang, Rong;Luo, Jun;Wang, Xiaoping
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.8
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    • pp.2042-2060
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    • 2013
  • In wireless sensor networks (WSNs), energy efficiency is one of the most essential design considerations, since sensor nodes are resource constrained. Group communication can reduce WSNs communication overhead by sending a message to multiple nodes in one packet. In this paper, in order to simultaneously resolve the transmission security and scalability in WSNs group communications, we propose a hierarchical cluster-based secure and scalable group key management scheme, called HRKT, based on logic key tree and route key tree structure. The HRKT scheme divides the group key into cluster head key and cluster key. The cluster head generates a route key tree according to the route topology of the cluster. This hierarchical key structure facilitates local secure communications taking advantage of the fact that the nodes at a contiguous place usually communicate with each other more frequently. In HRKT scheme, the key updates are confined in a cluster, so the cost of the key updates is reduced efficiently, especially in the case of massive membership changes. The security analysis shows that the HRKT scheme meets the requirements of group communication. In addition, performance simulation results also demonstrate its efficiency in terms of low storage and flexibility when membership changes massively.

A Hierarchical Cluster Tree Based Address Assignment Method for Large and Scalable Wireless Sensor Networks (대규모 무선 센서 네트워크를 위한 계층적 클러스터 트리 기반 분산 주소 할당 기법)

  • Park, Jong-Jun;Jeong, Hoon;Hwang, So-Young;Joo, Seong-Soon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.34 no.12B
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    • pp.1514-1523
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    • 2009
  • It is well known that the current wireless sensor networks addressing methods do not work efficiently in networks more than a few hundred nodes. A standard protocol in ZigBee-Standard feature in ZigBee 2007 gives balanced tree based address assignment method with distributed manner. However, it was limited to cover less than hundreds of sensor nodes due to the wasteful use of available address space, because composed sensor networks usually make an unbalanced tree topology in the real deployment. In this paper, we proposed the hierarchical cluster tree based address assignment method to support large and scalable networks. This method provides unique address for each node with distributed manner and supports hierarchical cluster tree on-demand. Simulation results show that the proposed method reduces orphan nodes due to the address exhaustion and supports larger network with limited address space compared with the ZigBee distributed address assignment method defined in ZigBee-Standard feature in ZigBee 2007.

A Hierarchical Cluster Tree Based Fast Searching Algorithm for Raman Spectroscopic Identification (계층 클러스터 트리 기반 라만 스펙트럼 식별 고속 검색 알고리즘)

  • Kim, Sun-Keum;Ko, Dae-Young;Park, Jun-Kyu;Park, Aa-Ron;Baek, Sung-June
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.20 no.3
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    • pp.562-569
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    • 2019
  • Raman spectroscopy has been receiving increased attention as a standoff explosive detection technique. In addition, there is a growing need for a fast search method that can identify raman spectrum for measured chemical substances compared to known raman spectra in large database. By far the most simple and widely used method is to calculate and compare the Euclidean distance between the given spectrum and the spectra in a database. But it is non-trivial problem because of the inherent high dimensionality of the data. One of the most serious problems is the high computational complexity of searching for the closet spectra. To overcome this problem, we presented the MPS Sort with Sorted Variance+PDS method for the fast algorithm to search for the closet spectra in the last paper. the proposed algorithm uses two significant features of a vector, mean values and variance, to reject many unlikely spectra and save a great deal of computation time. In this paper, we present two new methods for the fast algorithm to search for the closet spectra. the PCA+PDS algorithm reduces the amount of computation by reducing the dimension of the data through PCA transformation with the same result as the distance calculation using the whole data. the Hierarchical Cluster Tree algorithm makes a binary hierarchical tree using PCA transformed spectra data. then it start searching from the clusters closest to the input spectrum and do not calculate many spectra that can not be candidates, which save a great deal of computation time. As the Experiment results, PCA+PDS shows about 60.06% performance improvement for the MPS Sort with Sorted Variance+PDS. also, Hierarchical Tree shows about 17.74% performance improvement for the PCA+PDS. The results obtained confirm the effectiveness of the proposed algorithm.

Study of Cluster Tree Routing Protocols (클러스터 트리 라우팅 프로토콜 연구)

  • Cho, Moo-Ho
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.19 no.8
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    • pp.138-143
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    • 2005
  • A hierarchical tree structure of clusters has advantages for the network design due to its scalability and simple routing protocol. In this paper, the cluster tree routing protocol is studied for the wireless sensor network. From the numerical analysis results, the data aggregation in the intermediate nodes reduces the number of communication message and saves the energy of sensor nodes, but it may result in increased data traffic latency. And also the selection of cluster head can increase the relaying hops very high.

DDCP: The Dynamic Differential Clustering Protocol Considering Mobile Sinks for WSNs

  • Hyungbae Park;Joongjin Kook
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.6
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    • pp.1728-1742
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    • 2023
  • In this paper, we extended a hierarchical clustering technique, which is the most researched in the sensor network field, and studied a dynamic differential clustering technique to minimize energy consumption and ensure equal lifespan of all sensor nodes while considering the mobility of sinks. In a sensor network environment with mobile sinks, clusters close to the sinks tend to consume more forwarding energy. Therefore, clustering that considers forwarding energy consumption is desired. Since all clusters form a hierarchical tree, the number of levels of the tree must be considered based on the size of the cluster so that the cluster size is not growing abnormally, and the energy consumption is not concentrated within specific clusters. To verify that the proposed DDC protocol satisfies these requirements, a simulation using Matlab was performed. The FND (First Node Dead), LND (Last Node Dead), and residual energy characteristics of the proposed DDC protocol were compared with the popular clustering protocols such as LEACH and EEUC. As a result, it was shown that FND appears the latest and the point at which the dead node count increases is delayed in the DDC protocol. The proposed DDC protocol presents 66.3% improvement in FND and 13.8% improvement in LND compared to LEACH protocol. Furthermore, FND improved 79.9%, but LND declined 33.2% when compared to the EEUC. This verifies that the proposed DDC protocol can last for longer time with more number of surviving nodes.

Genomic Tree of Gene Contents Based on Functional Groups of KEGG Orthology

  • Kim Jin-Sik;Lee Sang-Yup
    • Journal of Microbiology and Biotechnology
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    • v.16 no.5
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    • pp.748-756
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    • 2006
  • We propose a genome-scale clustering approach to identify whole genome relationships using the functional groups given by the Kyoto Encyclopedia of Genes and Genomes Orthology (KO) database. The metabolic capabilities of each organism were defined by the number of genes in each functional category. The archaeal, bacterial, and eukaryotic genomes were compared by simultaneously applying a two-step clustering method, comprised of a self-organizing tree algorithm followed by unsupervised hierarchical clustering. The clustering results were consistent with various phenotypic characteristics of the organisms analyzed and, additionally, showed a different aspect of the relationship between genomes that have previously been established through rRNA-based comparisons. The proposed approach to collect and cluster the metabolic functional capabilities of organisms should make it a useful tool in predicting relationships among organisms.

Anthropometry for clothing construction and cluster analysis ( I ) (피복구성학적 인체계측과 집낙구조분석 ( I ))

  • Kim Ku Ja
    • Journal of the Korean Society of Clothing and Textiles
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    • v.10 no.3
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    • pp.37-48
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    • 1986
  • The purpose of this study was to analyze 'the natural groupings' of subjects in order to classify highly similar somatotype for clothing construction. The sample for the study was drawn randomly out of senior high school boys in Seoul urban area. The sample size was 425 boys between age 16 and 18. Cluster analysis was more concerned with finding the hierarchical structure of subjects by three dimensional distance of stature. bust girth and sleeve length. The groups forming a partition can be subdivided into 5 and 6 sets by the hierarchical tree of the given subjects. Ward's Minimum Variance Method was applied after extraction of distance matrix by the Standardized Euclidean Distance. All of the above data was analyzed by the computer installed at Korea Advanced Institute of Science and Technology. The major findings, take for instance, of 16 age group can be summarized as follows. The results of cluster analysis of this study: 1. Cluster 1 (32 persons means $18.29\%$ of the total) is characterized with smaller bust girth than that of cluster 5, but stature and sleeve length of the cluster 1 are the largest group. 2. Cluster 2 (18 Persons means $10.29\%$ of the total) is characterized with the group of the smallest stature and sleeve length, but bust girth larger than that of cluster 3. 3. Cluster 3(35persons means $20\%$ of the total) is classified with the smallest group of all the stature, bust girth and sleeve length. 4. Cluster 4(60 persons means $34.29\%$ of the total) is grouped with the same value of sleeve length with the mean value of 16 age group, but the stature and bust girth is smaller than the mean value of this age group. 5. Cluster 5(30 persons means $17.14\%$ of the total) is characterized with smaller stature than that of cluster 1, and with larger bust girth than that of cluster 1, but with the same value of the sleeve length with the mean value of the 16 age group.

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HIERARCHICAL CLUSTER ANALYSIS by arboART NEURAL NETWORKS and its APPLICATION to KANSEI EVALUATION DATA ANALYSIS

  • Ishihara, Shigekazu;Ishihara, Keiko;Nagamachi, Mitsuo
    • Proceedings of the Korean Society for Emotion and Sensibility Conference
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    • 2002.05a
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    • pp.195-200
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    • 2002
  • ART (Adaptive Resonance Theory [1]) neural network and its variations perform non-hierarchical clustering by unsupervised learning. We propose a scheme "arboART" for hierarchical clustering by using several ART1.5-SSS networks. It classifies multidimensional vectors as a cluster tree, and finds features of clusters. The Basic idea of arboART is to use the prototype formed in an ART network as an input to other ART network that has looser distance criteria (Ishihara, et al., [2,3]). By sending prototype vectors made by ART to one after another, many small categories are combined into larger and more generalized categories. We can draw a dendrogram using classification records of sample and categories. We have confirmed its ability using standard test data commonly used in pattern recognition community. The clustering result is better than traditional computing methods, on separation of outliers, smaller error (diameter) of clusters and causes no chaining. This methodology is applied to Kansei evaluation experiment data analysis.

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Analysis of Land-cover Types Using Multistage Hierarchical flustering Image Classification (다단계 계층군집 영상분류법을 이용한 토지 피복 분석)

  • 이상훈
    • Korean Journal of Remote Sensing
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    • v.19 no.2
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    • pp.135-147
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    • 2003
  • This study used the multistage hierarchical clustering image classification to analyze the satellite images for the land-cover types of an area in the Korean peninsula. The multistage algorithm consists of two stages. The first stage performs region-growing segmentation by employing a hierarchical clustering procedure with the restriction that pixels in a cluster must be spatially contiguous, and finally the whole image space is segmented into sub-regions where adjacent regions have different physical properties. Without spatial constraints for merging, the second stage clusters the segments resulting from the previous stage. The image classification of hierarchical clustering, which merges step-by step two small groups into one large one based on the hierarchical structure of digital imagery, generates a hierarchical tree of the relation between the classified regions. The experimental results show that the hierarchical tree has the detailed information on the hierarchical structure of land-use and more detailed spectral information is required for the correct analysis of land-cover types.

A Novel K-hop Cluster-based Ad hoc Routing Scheme with Delegation Functions (위임 기능을 이용한 새로운 K-hop 클러스터 기반 Ad hoc 라우팅 구조)

  • Kim Tae-yeon;Wang Ki-cheoul
    • Journal of Internet Computing and Services
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    • v.5 no.5
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    • pp.27-37
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    • 2004
  • The existing ad hoc network protocols suffer the scalability problem due to the inherent characteristics of node mobility. Cluster-based routing protocols divide the member nodes into a set of clusters and perform a hierarchical routing between these clusters. This hierarchical feature help to improve the scalability of ad hoc network routing. However, previous k-hop cluster-based routing protocols face another problems, that is, control overhead of the cluster headers. This paper proposes a novel k-hop cluster-based routing scheme with delegation functions for mobile ad hoc networks. The scheme employs is based on tree topology to manage cluster members in effectively. The cluster headers do not manage the routing table for whole members, while the header keeps the routing table for its neighbor members and the member list for one hop over nodes within k-hop cluster. Then the in-between leveled nodes manage the nested nodes which is structured in the lower level. Therefore, the proposed mechanism can reduce some control overhead of the cluster leaders.

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