• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.160-162
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• 2005

IPsec Tunnel Mode를 이용하여 보안 네트워크를 구축 시, 네트워크 구성이 중복된 경우에는 중복되지 않도록 재구성해야 하는 문제가 있다. 본 논문에서는 IPSec Tunnel Mode 통신을 하고자 하는 두 네트워크가 중복된 경우, IPSec 네트워크 별칭 기법을 통하여, 이전 네트워크의 구성을 변경하지 않고 통신할 수 있는 방안을 제시한다.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.315-318
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• 2005

Lots of researches have been focusing on ubiquitous computing which means wherever, whenever, whatever the required information must be accessible. In ubiquitous computing environment, ubiquitous sensor network (USN) is the basis technology for gathering and transferring the required information. However sensor network characteristically has more severe vulnerability than the existing networks do. The paper presents operation of secure protocols for delivering information in secure in ubiquitous computing environment and show improvement of the secure transferring protocol.

• Journal of Information Processing Systems
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• v.8 no.1
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• pp.1-20
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• 2012

The most important criterion for achieving the maximum performance in a wireless mesh network (WMN) is to limit the interference within the network. For this purpose, especially in a multi-radio network, the best option is to use non-overlapping channels among different radios within the same interference range. Previous works that have considered non-overlapping channels in IEEE 802.11a as the basis for performance optimization, have considered the link quality across all channels to be uniform. In this paper, we present a measurement-based study of link quality across all channels in an IEEE 802.11a-based indoor WMN test bed. Our results show that the generalized assumption of uniform performance across all channels does not hold good in practice for an indoor environment and signal quality depends on the geometry around the mesh routers.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.317-320
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• 2009

At the present day, the use of containers crisscrossing seven seas and intercontinental transport has significantly increased and bringing the change on the shape of the world economy which we cannot be neglected. Additionally, with the recent technological advances in wireless sensor network (WSN) technologies, has providing an economically feasible monitoring solution to diverse application that allow us to envision the intelligent containers represent the next evolutionary development step in order to increase the efficiency, productivity, utilities, security and safe of containerized cargo shipping. This paper we present a comprehensive containerized cargo monitoring system which has adaptively embedded WSN technology into cargo logistic technology. We share the basic requirement for an autonomous logistic network that could provide optimum performance and a suite of algorithms for self-organization and bi-directional communication of a scalable large number of sensor node apply on container regardless inland and maritime transportation.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.16-24
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• 2024

With an increase in the relevance of next-generation integrated networking environments, the need to effectively utilize advanced networking techniques also increases. Specifically, integrating Software-Defined Networking (SDN) with Multi-access Edge Computing (MEC) is critical for enhancing network flexibility and addressing challenges such as security vulnerabilities and complex network management. SDN enhances operational flexibility by separating the control and data planes, introducing management complexities. This paper proposes a reinforcement learning-based network path optimization strategy within SDN environments to maximize performance, minimize latency, and optimize resource usage in MEC settings. The proposed Enhanced Proximal Policy Optimization (PPO)-based scheme effectively selects optimal routing paths in dynamic conditions, reducing average delay times to about 60 ms and lowering energy consumption. As the proposed method outperforms conventional schemes, it poses significant practical applications.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.11a
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• pp.320-320
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• 2004

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.17-23
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• 2022

Network slicing is a promising paradigm and significant evolution for adjusting the heterogeneous services based on different requirements by placing dynamic virtual network functions (VNF) forwarding graph (VNFFG) and orchestrating service function chaining (SFC) based on criticalities of Quality of Service (QoS) classes. In system architecture, software-defined networks (SDN), network functions virtualization (NFV), and edge computing are used to provide resourceful data view, configurable virtual resources, and control interfaces for developing the modified deep reinforcement learning agent (MDRL-A). In this paper, task requests, tolerable delays, and required resources are differentiated for input state observations to identify the non-critical/critical classes, since each user equipment can execute different QoS application services. We design intelligent slicing for handing the cross-domain resource with MDRL-A in solving network problems and eliminating resource usage. The agent interacts with controllers and orchestrators to manage the flow rule installation and physical resource allocation in NFV infrastructure (NFVI) with the proposed formulation of completion time and criticality criteria. Simulation is conducted in SDN/NFV environment and capturing the QoS performances between conventional and MDRL-A approaches.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.443-456
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• 2022

An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.

• Journal of KIISE:Information Networking
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• v.34 no.5
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• pp.379-388
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• 2007

With proliferation of computer and wireless network technology, it is common to access Internet through wireless network using mobile device. Ratio of using streaming media out of many applications through Internet is increasing not only in wired network but also in wireless network. Streaming media is much bigger than other contents and requires more network bandwidth and more computing resources. However mobile devices hate relatively poor computing resource and low network bandwidth. If media streaming service is provided for mobile devices without any consideration about network bandwidth and computing power, it is hard for the client to get high qualify service. Since mobile device is supported with very limited energy from the battery, media streaming should be adjusted to varying energy state of mobile device in realtime to ensure complete playback of streaming media. In this paper, we propose DFRC to provide high qualify service to mobile client through wireless network by controlling the number of frames transmitted to client based on computing resource and energy state of mobile device.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.1-9
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• 2006

As an effective solution to resolve complex computing problems and to handle geographically dispersed data sets, grid computing has been noticed. Grid computing separates an application to several parts and executes on heterogeneous computing platforms simultaneously. The most important problem in grid computing environments is to manage grid resources and to schedule grid resources. This paper proposes a grid transaction network model that is applicable for resource management and scheduling in grid computing environment and presents a grid resource bidding algorithm for grid users and grid resource providers. Using DEVSJAVA modeling and simulation, this paper evaluates usefulness and efficiency of the proposed model.