• The KIPS Transactions:PartC
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• v.8C no.5
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• pp.637-646
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• 2001

The multicasting telecommunication is an important research as an applicable technique in the development of adaptable technique for modern mobile computing and mobile IP, because that is supposed to be a solution for transferring the large-size information on the mobile and wireless network with the narrow bandwidth. This paper will suggest one solution for the bidirectional tunneling and the local re-registration problems to support transferring mobile multicasting datagram by the partitioned network. The bidirectional tunneling technique is in use for mobile host moving around in a region and the local re-registration technique for crossing regions to reduce traffic load caused by transferring datagram along a long distance, and we compare those models with our suggested model by simulation.

• Journal of Satellite, Information and Communications
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• v.11 no.3
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• pp.37-42
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• 2016

In this paper, in today's highly complex video and broadcast operations, broadcasters are constantly challenged to reliably deliver low-latency, high-quality video to multiscreen audiences on-air and online. The Adaptive Bit Rate (ABR) protocols enable internet video to a wide range of multiscreen devices. However, video quality is often marginal and would prove unacceptable for valued linear broadcast content delivered to the Big Screen today. The Media information processing technology advances in ABR enables service providers to take control and offer quality managed linear video services to ALL screens in the home, including the Big Screen, with a single unified IP Video infrastructure. The New Multiscreen-Assisted ABR (MSA-ABR) delivery management system proposed using Cloud based multicast-assisted ABR for a broadcast facility that performs routing of contribution content and online publishing services within a virtual, centralized cloud infrastructure.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.53-58
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• 2018

MANET transmits data by hop-by-hop method because it is composed of mobile nodes without support of any infrastructure. Its structure is very similar to a block chain. However, it is exposed to various threats such as data tampering or destruction by malicious nodes because of transmission method. So, ensuring the integrity of transmitted data is an important complement to MANET. In this paper, we propose a method to apply the block chain technique in order to protect the reliability value of the nodes consisting the network from malicious nodes. For this, hierarchical structure of a cluster type is used. Only cluster head stores the reliability information of the nodes in a block and then, this can be spread. In addition, we applied block generation difficulty automatic setting technique using the number of nodes selecting cluster head and the reliability of cluster head to prevent the spread of wrong blocks. This can prevent block generation and spread by malicious nodes. The superior performance of the proposed technique can be verified by comparing experiments with the SAODV technique.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.65-78
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• 2009

The most commonly adopted approach to find valuable information from tree data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) modeling data as hierarchical tree structure, (2) the computationally high cost of the candidate maintenance, (3) the repetitious input dataset scans, and (4) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. The proposed method not only gets rid of the process for infrequent subtrees pruning, but also totally eliminates the problem of generating candidate subtrees. Hence, it significantly improves the whole mining process.