• The Journal of Information Technology and Database
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• v.8 no.2
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• pp.41-60
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• 2001

Despite the growth in Internet users, demand for multi-medial, large data files and resulting explosive growth in data traffic, there has been lack of investment in Middle-Mile, interconnection of various networks, resulting in bottleneck effect, which is acerbating. One strategy to overcome such network bottleneck is Content Delivery Network (CDN). CDN does not achieve efficient delivery of large file data through physical improvement/increase in network capacity, but by delivering large file contents, the cause of bottlenecks, from distributed servers. Since it is impracticable to physically improve networks capacity to accommodate the growth in internet traffic, CON, by strong CPs contents at cache servers deployed at major ISPs networks, is able to deliver requested contents to the requesting Web clients without the loss of data and long latency.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.101-104
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• 2007

Efficient Query processing and optimization are critical for reducing network traffic and decreasing latency of query when accessing and manipulating sensor data of large-scale sensor networks. Currently it has been studied in sensor database projects. These works have mainly focused on in-network query processing for sensor networks and assumes homogeneous sensor networks, where each sensor network has same hardware and software configuration. In this paper, we present a framework for efficient query processing over heterogeneous sensor networks. Our proposed framework introduces query processing paradigm considering two heterogeneous characteristics of sensor networks: (1) data dissemination approach such as push, pull, and hybrid; (2) query processing capability of sensor networks if they may support in-network aggregation, spatial, periodic and conditional operators. Additionally, we propose multi-query optimization strategies supporting cross-translation between data acquisition query and data stream query to minimize total cost of multiple queries. It has been implemented in WSN middleware, COSMOS, developed by ETRI.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.6
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• pp.152-160
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• 1996

This paper focuses on development and evaluation of a performance management algorithm for IEEE 802.4 token bus networks to serve large-scale integrated manufacturing systems. Such factory automation networks have to satisfy delay constraints imposed on time-critical messages while maintaining as much network capacity as possible for non-time-critical messages. This paper presents a network performance manager that adjusts queue capacity as well as timers by using a set of fuzzy rules and fuzzy inference mechanism. The efficacy of the performance management has been demonstrated by a series of simulation experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.8
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• pp.709-716
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• 2005

As a way to build more efficient and intelligent container cranes for todays hub ports, communication networks are used to interconnect numerous sensors, actuators, controllers, and operator switches and consoles that are spatially distributed over a crane. Various signals such as sensor values and operator's commands are digitized and broadcast on the network instead of using separate wiring cables. This not only makes the design and manufacturing of a crane more efficient, but also easier implementation of intelligent control algorithms. This paper presents the performance evaluation of CAN(Controller Area Network), TTP(Time Triggered Protocol) and Byteflight that can be used for cranes. Through discrete event simulation, several important quantitative performance factors such as the probability of a transmission failure, average system delay (data latency) and maximum system delay have been evaluated.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.794-799
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• 2012

The international standard, ISO-11783, was designed for the communication within an agriculture machinery. Even if it is based on the CAN (Control Area Network) protocol, its extended features which include point-to-point communication and large data transmission support show different network performance from the standard CAN. This paper proposes a dynamic priority allocation method to improve the real-time performance of ISO-11783. Computer simulation shows reduction of the deadline-missed cases and community latency via proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.6B
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• pp.563-572
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• 2003

When the Mobile IP is applied for mobility management protocol in the next-generation mobile communication networks, it can cause a serious performance degradation called micro-mobility problem. In this paper, we propose ANMM(Access Network Mobility Management) to efficiently support micro-mobility in the IP-based wireless access network. The ANMM scheme can reduce the handoff latency and signaling overhead resulting in performance enhancement by managing the mobility of mobile nodes within access network.

• Electronics and Telecommunications Trends
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• v.36 no.5
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• pp.92-102
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• 2021

Owing to various restrictions in-field application, the low-speed, low-power-based industrial Internet-of-Things (IoT) network built in extremely harsh industrial environment sites requires multi-hop, channel hopping, and low-latency transmission characteristics. In the past, wired networks were used in industrial facilities; however, network technologies based on the Industrial IoT Network standard standardized for industrial applications, such as WirelessIO link, WirelessHART, SmartMesh, and eStar Link satisfy industrial requirements. Recently, the use of industrial IoT networks in industrial facilities has rapidly expanded. This paper covers the developments in industrial IoT network technologies and summarizes the major industrial IoT standard technologies that meets the requirements of industrial sites.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.517-519
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• 2022

In 5G network environment, proactive mobility management is essential as 5G mobile networks provide new services with ultra-low latency through dense deployment of small cells. The importance of a system that actively controls device handover is emerging and it is essential to predict mobile trajectory during handover. Sequence-to-sequence model is a kind of deep learning model where it converts sequences from one domain to sequences in another domain, and mainly used in natural language processing. In this paper, we developed a system for predicting mobile trajectory in a wireless network environment using sequence-to-sequence model. Handover speed can be increased by utilize our sequence-to-sequence model in actual mobile network environment.

• ETRI Journal
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• v.45 no.6
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• pp.1065-1078
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• 2023

In vehicular networks, diverse safety information can be shared among vehicles through internet connections. In vehicle-to-internet communications, vehicles on the road are wirelessly connected to different cloud networks, thereby accelerating safety information exchange. Onboard sensors acquire traffic-related information, and reliable intermediate nodes and network services, such as navigational facilities, allow to transmit safety information to distant target vehicles and stations. Using vehicle-to-network communications, we minimize delays and achieve high accuracy through consistent connectivity links. Our proposed approach uses intermediate nodes with two-hop separation to forward information. Target vehicle detection and routing of safety information are performed using machine learning algorithms. Compared with existing vehicle-to-internet solutions, our approach provides substantial improvements by reducing latency, packet drop, and overhead.

• 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.