• The Journal of Information Systems
• /
• v.8 no.1
• /
• pp.109-129
• /
• 1999

This study deals with the problem of information dissemination in a communication network, which is defined to be the process whereby a set of messages, generated by an originator, is transmitted to all the members within the network. Since this type of message generally includes control data to manage the network or global information that all members should know, it is to be required to transmit it to all the members as soon as possible. In this study, it is assumed that a member can either transmit or receive a message and an informed member can transmit it to only one of its neighbors at time. This type of transmission is called 'local broadcasting' Several schemes of call sequencing are designed for a general-type network with nonuniform edge transmission times, and then computer simulations are performed. Some heuristics for information dissemination are proposed and tested. For this, optimal call sequence in a tree-type network, sequencing theory and graph theory are applied. The result shows that call sequencing based on the shortest path tree is the most desirable.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.11 no.4
• /
• pp.77-89
• /
• 2001

Recently, with the explosive growth of communication technologies, group oriented services such as teleconference and multi-player game are increasing. Access control to information is handled by secret communications with group keys shared among members, and efficient updating of group keys is vital to such secret communications of large and dynamic groups. In this paper, we employ (2,4)-tree as a key tree, which is one of height balanced trees, to reduce the number of key updates caused by join or leave of members. Especially, we use CBT(Core Based Tree) to gather network configurations of group members and reflect this information to key tree structure to update group keys efficiently when splitting or merging of subgroups occurs by network failure or recovery.

• Journal of Intelligence and Information Systems
• /
• v.9 no.1
• /
• pp.139-155
• /
• 2003

This paper presents the new methodology of analyzing and classifying patterns of customers in mobile telecommunication market to enhance the performance of predicting the credit information based on the decision tree and neural network. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship and makes special management on the customer who has huh potential of getting out of contract in advance. The real implementation of proposed method shows that the predicted accuracy is higher than existing methods such as decision tree(CART, C4.5), regression, neural network and combined model(CART and NN).

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.1
• /
• pp.227-247
• /
• 2018

For intermittently connected wireless sensor networks deployed in hash environments, sensor nodes may fail due to internal or external reasons at any time. In the process of data collection and recovery, we need to speed up as much as possible so that all the sensory data can be restored by accessing as few survivors as possible. In this paper a novel redundant data storage algorithm based on minimum spanning tree and quasi-randomized matrix-QRNCDS is proposed. QRNCDS disseminates k source data packets to n sensor nodes in the network (n>k) according to the minimum spanning tree traversal mechanism. Every node stores only one encoded data packet in its storage which is the XOR result of the received source data packets in accordance with the quasi-randomized matrix theory. The algorithm adopts the minimum spanning tree traversal rule to reduce the complexity of the traversal message of the source packets. In order to solve the problem that some source packets cannot be restored if the random matrix is not full column rank, the semi-randomized network coding method is used in QRNCDS. Each source node only needs to store its own source data packet, and the storage nodes choose to receive or not. In the decoding phase, Gaussian Elimination and Belief Propagation are combined to improve the probability and efficiency of data decoding. As a result, part of the source data can be recovered in the case of semi-random matrix without full column rank. The simulation results show that QRNCDS has lower energy consumption, higher data collection efficiency, higher decoding efficiency, smaller data storage redundancy and larger network fault tolerance.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.4A
• /
• pp.388-398
• /
• 2011

In this paper, we introduce an EMSP(Efficient Mobility Support Protocol) for mobile sensor network with mobility-aware. We propose virtual cluster and node split scheme considering movements of mobile nodes. The existing M-LEACH protocol suffers from communication cost spent on JOIN request information during invitation phase. To address this issue, the large boundary of the cluster in LUR-tree can reduce superfluous update cost. In addition to the expansion of the cluster, the proposed approach exploits node split algorithms used in R-tree in order to uniformly form a cluster. The simulated results show that energy-consumption has less up to about 40% than LEACH-C and 8% than M-LEACH protocol. Finally, we show that the proposed scheme outperforms those of other in terms of lifetime of sensor fields and scalability in wireless sensor network.

• ETRI Journal
• /
• v.28 no.3
• /
• pp.329-336
• /
• 2006

This paper investigates the problem of protecting multicast sessions in mesh wavelength-division multiplexing (WDM) networks against single link failures, for example, a fiber cut in optical networks. First, we study the two characteristics of multicast sessions in mesh WDM networks with sparse light splitter configuration. Traditionally, a multicast tree does not contain any circles, and the first characteristic is that a multicast tree has better performance if it contains some circles. Note that a multicast tree has several branches. If a path is added between the leave nodes on different branches, the segment between them on the multicast tree is protected. Based the two characteristics, the survivable multicast sessions routing problem is formulated into an Integer Linear Programming (ILP). Then, a heuristic algorithm, named the adaptive shared segment protection (ASSP) algorithm, is proposed for multicast sessions. The ASSP algorithm need not previously identify the segments for a multicast tree. The segments are determined during the algorithm process. Comparisons are made between the ASSP and two other reported schemes, link disjoint trees (LDT) and shared disjoint paths (SDP), in terms of blocking probability and resource cost on CERNET and USNET topologies. Simulations show that the ASSP algorithm has better performance than other existing schemes.

• The Journal of Information Systems
• /
• v.29 no.2
• /
• pp.287-306
• /
• 2020

Purpose Youth unemployment is a social problem that continues to emerge in Korea. In this study, we create a model that predicts the employment of college graduates using decision tree, random forest and artificial neural network among machine learning techniques and compare the performance between each model through prediction results. Design/methodology/approach In this study, the data processing was performed, including the acquisition of the college graduates' vocational path survey data first, then the selection of independent variables and setting up dependent variables. We use R to create decision tree, random forest, and artificial neural network models and predicted whether college graduates were employed through each model. And at the end, the performance of each model was compared and evaluated. Findings The results showed that the random forest model had the highest performance, and the artificial neural network model had a narrow difference in performance than the decision tree model. In the decision-making tree model, key nodes were selected as to whether they receive economic support from their families, major affiliates, the route of obtaining information for jobs at universities, the importance of working income when choosing jobs and the location of graduation universities. Identifying the importance of variables in the random forest model, whether they receive economic support from their families as important variables, majors, the route to obtaining job information, the degree of irritating feelings for a month, and the location of the graduating university were selected.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.8
• /
• pp.2188-2208
• /
• 2023

Quality of Service (QoS) is a critical feature of Wireless Sensor Networks (WSNs) with routing algorithms. Data packets are moved between cluster heads with QoS using a number of energy-efficient routing techniques. However, sustaining high scalability while increasing the life of a WSN's networks scenario remains a challenging task. Thus, this research aims to develop an energy-balancing component that ensures equal energy consumption for all network sensors while offering flexible routing without congestion, even at peak hours. This research work proposes a Gravitational Blackhole Search Optimised splay tree routing framework. Based on the splay tree topology, the routing procedure is carried out by the suggested method using three distinct steps. Initially, the proposed GBSO decides the optimal route at initiation phases by choosing the root node with optimum energy in the splay tree. In the selection stage, the steps for energy update and trust update are completed by evaluating a novel reliance function utilising the Parent Reliance (PR) and Grand Parent Reliance (GPR). Finally, in the routing phase, using the fitness measure and the minimal distance, the GBSO algorithm determines the best route for data broadcast. The model results demonstrated the efficacy of the suggested technique with 99.52% packet delivery ratio, a minimum delay of 0.19 s, and a network lifetime of 1750 rounds with 200 nodes. Also, the comparative analysis ensured that the suggested algorithm surpasses the effectiveness of the existing algorithm in all aspects and guaranteed end-to-end delivery of packets.

• The Transactions of the Korea Information Processing Society
• /
• v.1 no.2
• /
• pp.184-193
• /
• 1994

This paper considers the Updating Minimum-weight Spanning Tree Problem(UMP), that is, the problem to update the Minimum-weight Spanning Tree(MST) in response to topology change of the network. This paper proposes the algorithm which reconstructs the MST after several links deleted and added. Its message complexity and its ideal-time complexity are Ο(m＋n log(t＋f)) and Ο(n＋n log(t＋f)) respectively, where n is the number of processors in the network, t(resp.f) is the number of added links (resp. the number of deleted links of the old MST), And m=t＋n if f=Ο, m=e (i.e. the number of links in the network after the topology change) otherwise. Moreover the last part of this paper touches in the algorithm which deals with deletion and addition of processors as well as links.

• Journal of Environmental Science International
• /
• v.25 no.4
• /
• pp.603-613
• /
• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.