• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.119-125
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• 2023

IT infrastructure operation has advanced, and the methods for managing systems have become widely adopted. Recently, research has focused on improving system management using Syslog. However, utilizing log data collected through these methods presents challenges, as logs are extracted in various formats that require expert analysis. This paper proposes a system that utilizes edge computing to distribute the collection of Syslog data and preprocesses duplicate data before storing it in a central database. Additionally, the system constructs a data dictionary to classify and count data in real-time, with restrictions on transmitting registered data to the central database. This approach ensures the maintenance of predefined patterns in the data dictionary, controls duplicate data and temporal duplicates, and enables the storage of refined data in the central database, thereby securing fundamental data for big data analysis. The proposed algorithms and procedures are demonstrated through simulations and examples. Real syslog data, including extracted examples, is used to accurately extract necessary information from log data and verify the successful execution of the classification and storage processes. This system can serve as an efficient solution for collecting and managing log data in edge environments, offering potential benefits in terms of technology diffusion.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.669-678
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• 2018

This study developed information technology infrastructures for building a driving environment analysis platform using various big data, such as vehicle sensing data, public data, etc. First, a small platform server with a parallel structure for big data distribution processing was developed with H/W technology. Next, programs for big data collection/storage, processing/analysis, and information visualization were developed with S/W technology. The collection S/W was developed as a collection interface using Kafka, Flume, and Sqoop. The storage S/W was developed to be divided into a Hadoop distributed file system and Cassandra DB according to the utilization of data. Processing S/W was developed for spatial unit matching and time interval interpolation/aggregation of the collected data by applying the grid index method. An analysis S/W was developed as an analytical tool based on the Zeppelin notebook for the application and evaluation of a development algorithm. Finally, Information Visualization S/W was developed as a Web GIS engine program for providing various driving environment information and visualization. As a result of the performance evaluation, the number of executors, the optimal memory capacity, and number of cores for the development server were derived, and the computation performance was superior to that of the other cloud computing.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.9
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• pp.429-436
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• 2017

Unmanned Aerial Vehicles (UAV) that are piloted without human pilots can be commanded remotely via frequencies or perform pre-inputted missions. UAVs have been mainly used for military purposes, but due to the development of ICT technology, they are now widely used in the private sector. Teal Group's 2014 World UAV Forecast predicts that the UAV market will grow by 10% annually over the next decade, reaching $ 12.5 billion by 2023. However, because UAVs are primarily remotely controlled, if a malicious user accesses a remotely controlled UAV, it could seriously infringe privacy and cause financial loss or even loss of life. To solve this problem, a secure channel must be established through mutual authentication between the UAV and the control center. However, existing security techniques require a lot of computing resources and power, and because communication distances, infrastructure, and data flow are different from UAV networks, it is unsuitable for application in UAV environments. To resolve this problem, the study presents a lightweight UAV authentication method based on Physical Unclonable Functions (PUFs) that requires less computing resources in the ground Control and Non-Payload Communication (CNPC) environment, where recently, technology standardization is actively under progress.

• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.57-66
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• 2018

EDISON ( EDucation-research Integration through Simulation On the Net) is a web simulation service based on cloud compu-ting. EDISON provides that web service and provide analysis result to users through pre-built infrastructure and various calcu-lation nodes computational science engineering problems that are difficult or impossible to analysis as user's personal resources to users. As a result, a simulation execution environment is provided in a web portal environment so that EDISON can be ac-cessed regardless of a user's device or operating system to perform computational science engineering analysis simulation. The purpose of this research is to design and construct a workbench based real - time dynamic service to provide an integrat-ed user interface to the EDSION system, which is a computational science engineering simulation and analysis platform, which is currently provided to users. We also devised a workbench-based user simulation service environment configuration. That has a user interface that is similar to the computational science engineering simulation software environment used locally. It can configure a dynamic web environment such as various analyzers, preprocessors, and simulation software. In order to provide these web services, the service required by the user is configured in portlet units, and as a result, the simulation service using the workbench is constructed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12B
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• pp.1450-1458
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• 2011

The HPcN (Hybrid & high Performance Convergence Network) in research networks means environment which can provide both computing resource such as supercomputer, cluster and network resource to application researchers in the field of medical, bio, aerospace and e-science. The most representative research network in Korea, KREONET has been developing following technologies through the HERO (Hybrid Networking project for research oriented infrastructure) from 200S. First, we have constructed and deployed a control plane technology which can provide a connection oriented network dynamically. Second, the integrated resource management system technology has been developing for reservation and allocation of both computing and network resources, whenever users want to utilize them. In this paper, a testbed network is presented, which is possible to reserve and allocate network resource using the integrated resource management system. We reserve network resource through GNSI (Grid Network Service Interface) messages between GRS (Global Resource Scheduler) and NRM (Network Resource Manager) and allocate network resource through GUNI (Grid User Network Interface) messages between the NRM (network resource manager) and routers, based on reservation information provided from a user on the web portal. It is confirmed that GUNI interface messages are delivered from the NRM to each router at the starting of reservation time and traffic is transmitted through LSP allocated by the NRM.

• Journal of the Korea Society for Simulation
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• v.15 no.1
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• pp.35-42
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• 2006

Sensor network, that is an infrastructure of ubiquitous computing, consists of a number of sensor nodes of which hardware is very small. The network topology and routing scheme of the network should be determined according to its purpose, and its hardware and software may have to be changed as needed from time to time. Thus, the sensor network simulator being capable of verifying its behavior and estimating performance is required for better design. Sensor network simulators currently existing have been developed for specific hardwares or operating systems, so that they can only be used for such systems and do not provide any means to estimate the amount of power consumption and program execution time which are major issues for system design. In this study, we develop the sensor network simulator that can be used to design and verify various sensor networks without regarding to types of applications or operating systems, and also has the capability of predicting the amount of power consumption and program execution time. For this purpose, the simulator is developed by using machine instruction-level discrete-event simulation scheme. As a result, the simulator can be used to analyze program execution timings and related system behaviors in the actual sensor nodes in detail. Instruction traces used as workload for simulations are executable images produced by the cross-compiler for ATmega128L microcontroller.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.2
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• pp.149-156
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• 2005

The HLA is the simulation architecture standard that the civilian and military simulation communities are deeply interested in. We can find various successful practices applying HLA to constructive simulations such as war games in domestics and overseas. However, any case of real-time distributed simulations has not been reported. The reason is that a message transmission period via RTI in a network layer varies according to computing power, simulation nodes, transmission types, and packet size; further a message processing time in an application layer depends on its processing methods, thus too difficult to set up real-time constraints for the enhancement of a real-time resolution. Hence, in this paper we have studied the real-time constraints of RTI for the development of the M-SAM simulator. Thus we have developed a HLA based pilot simulator using 6 PC's in LAN and then measured and analysed the performance of the RTI. As the results of our work, we could obtain the quantitative values for message delay, RTI overhead and RTI packet transmission ratio by a real operation scenario and loads, which are not shown in the previous works. We also expect that the results can be used as a guideline to set up the number of targets, transmission frequency and message processing method in the development of the M-SAM simulator and similar applications.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.2
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• pp.29-38
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• 2021

The Internet including mobile networks is developing to overcoming the limitation of physical distance and providing or acquiring information from remote locations. However, the systems that use video as primary information require higher bandwidth for recognizing the situation in remote places more accurately through high-quality video as well as lower latency for faster interaction between devices and users. The emergence of the 5th generation mobile network provides features such as high bandwidth and precise location recognition that were not experienced in previous-generation technologies. In addition, the Mobile Edge Computing that minimizes network latency in the mobile network requires a change in the traditional system architecture that was composed of the existing smart device and high availability server system. However, even with 5G and MEC, since there is a limit to overcome the mobile network state fluctuations only by enhancing the network infrastructure, this study proposes a high-definition video streaming system in ultra-low latency based on the SRT protocol that provides Forward Error Correction and Fast Retransmission. The proposed system shows how to deploy software components that are developed in consideration of the nature of 5G and MEC to achieve sub-1 second latency for 4K real-time video streaming. In the last of this paper, we analyze the most significant factor in the entire video transmission process to achieve the lowest possible latency.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.137-144
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• 2023

Cyberattack technology is evolving to an unpredictable degree, and it is a situation that can happen 'at any time' rather than 'someday'. Infrastructure that is becoming hyper-connected and global due to cloud computing and the Internet of Things is an environment where cyberattacks can be more damaging than ever, and cyberattacks are still ongoing. Even if damage occurs due to external influences such as cyberattacks or natural disasters, intelligent self-recovery must evolve from a cyber resilience perspective to minimize downtime of cyber assets (OS, WEB, WAS, DB). In this paper, we propose an intelligent self-recovery technology to ensure sustainable cyber resilience when cyber assets fail to function properly due to a cyberattack. The original and updated history of cyber assets is managed in real-time using timeslot design and snapshot backup technology. It is necessary to secure technology that can automatically detect damage situations in conjunction with a commercialized file integrity monitoring program and minimize downtime of cyber assets by analyzing the correlation of backup data to damaged files on an intelligent basis to self-recover to an optimal state. In the future, we plan to research a pilot system that applies the unique functions of self-recovery technology and an operating model that can learn and analyze self-recovery strategies appropriate for cyber assets in damaged states.

• Journal of Korea Water Resources Association
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• v.57 no.9
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• pp.615-626
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• 2024

To accurately and efficiently monitor soil moisture (SM) across South Korea, this study developed a SM estimation model that integrates the cloud computing platform Google Earth Engine (GEE) and Automated Machine Learning (AutoML). Various spatial information was utilized based on Terra MODIS (Moderate Resolution Imaging Spectroradiometer) and the global precipitation observation satellite GPM (Global Precipitation Measurement) to test optimal input data combinations. The results indicated that GPM-based accumulated dry-days, 5-day antecedent average precipitation, NDVI (Normalized Difference Vegetation Index), the sum of LST (Land Surface Temperature) acquired during nighttime and daytime, soil properties (sand and clay content, bulk density), terrain data (elevation and slope), and seasonal classification had high feature importance. After setting the objective function (Determination of coefficient, R² ; Root Mean Square Error, RMSE; Mean Absolute Percent Error, MAPE) using AutoML for the combination of the aforementioned data, a comparative evaluation of machine learning techniques was conducted. The results revealed that tree-based models exhibited high performance, with Random Forest demonstrating the best performance (R² : 0.72, RMSE: 2.70 vol%, MAPE: 0.14).