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Construction of an Efficient Overlay Multicast for Multi-layer
Bumjae Lee,Kangwhan Lee 대한전자공학회 2008 ICEIC:International Conference on Electronics, Inf Vol.1 No.1
As the MANET is becoming more noticed as a flexible and non-infrastructure network, routing algorism of the MANET is becoming an important issue. Especially, in the mobile Ad hoc, the method of the management of each Node is recognized as an important part of the next generation network from now on. In this paper used that Overlay Multicast Routing Algorism operates flexibly according to the various limited condition and environment of the MANET. Because of this Algorism have maintenance and transmission of the network to used virtual Overlay ID. But the existing Overlay Multicast applies only to Cluster made up single layer. And Clustering and maintenance of the cluster does not treat energy property. So, in this paper proposed Efficient Overlay Multicast for Multilayer(EOMM) is clustering consider residual energy of the node in the based multi-layer. And it has efficient overlay ID generation of the Node for the life time and the advantage of the Multi-layer is maintenance of nodes. Then more it provide faster operation in routing through Masking overlay ID in each layer. Our algorism reduces routing delay and more efficient Packet ratio and life time.
클러스터링 알고리즘기반의 COVID-19 상황인식 분석
이강환,Lee, Kangwhan 한국정보통신학회 2022 한국정보통신학회논문지 Vol.26 No.5
This paper propose a clustered algorithm that possible more efficient COVID-19 disease learning prediction within clustering using context-aware attribute information. In typically, clustering of COVID-19 diseases provides to classify interrelationships within disease cluster information in the clustering process. The clustering data will be as a degrade factor if new or newly processing information during treated as contaminated factors in comparative interrelationships information. In this paper, we have shown the solving the problems and developed a clustering algorithm that can extracting disease correlation information in using K-means algorithm. According to their attributes from disease clusters using accumulated information and interrelationships clustering, the proposed algorithm analyzes the disease correlation clustering possible and centering points. The proposed algorithm showed improved adaptability to prediction accuracy of the classification management system in terms of learning as a group of multiple disease attribute information of COVID-19 through the applied simulation results.
A Study on the Efficient TICC(Time Interval Clustering Control) Algorithm for MANET
Youngsam Kim,Kyoungmin Doo,Samhyeon Chi,Kangwhan Lee 대한전자공학회 2008 ICEIC:International Conference on Electronics, Inf Vol.1 No.1
A MANET(Mobile Ad-hoc Network) is a multi-hop routing protocol formed by a collection without the intervention of infrastructure. So the MANET also depended on the property as like variable energy, high degree of mobility, location environments of nodes etc. In recently years, the various clustering technique and routing algorithm would have proposed for improving the energy efficiency. One of the popular approach methods, It could make a cluster-based routing algorithm using in MANET. However, the proposed clustering method results remain the problems in high energy consumption on the cluster head node management. To solve the problems, many routing scheme have been presented to provide that Dynamic clustering is a method of resolving such a problem by distributing energy consumption through the re-selected head node [1][5]. But the proposed dynamic clustering has a problem when cluster head node is re-selected for energy consumption. These proposed an algorithm were presented about composition of cluster that was being node clustering algorithm process. However, it could not provide a solution that is also improving energy efficiency and making energy consumption to reduce through reconstruction of cluster. So, in this paper, we propose an algorithm techniques which is TICC (Time Interval Clustering Control) based on energy value in property of each node for solving cluster problem. It provides improving cluster energy efficiency how can being node manage to order each node's energy level. TICC is clustering method. It has shown that Node's energy efficiency and life time are improved in MANET.