• Proceedings of the Korean Information Science Society Conference
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• 2003.10c
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• pp.277-279
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• 2003

Ad­hoc 네트워크 환경은 자유롭고 예측할 수 없는 통신 노드들의 이동으로 인해 기본적인 TCP 혼잡 제어가 상당히 나쁜 영향을 미치게 된다. 기본적인 기존의 TCP에 대한 에러 감지(detection) 및 복구(recovery)는 새로운 환경에 맞추어 개선되어야 한다. 이 논문에서는 잦은 경로변경 현상을 네트워크의 피드백 없이도 패킷의 순서가 바뀌어 전달됨을 이용하여 알아내고, 이를 처리하여 성능을 향상시킬 수 있는 기법을 제안한다. 또한 오랜 시간의 네트워크 단절에 따른 성능감소문제를 수신자(Receiver)가 검사하여 해결할 수 있는 기법을 제안한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.65-75
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• 2005

Measurement of network traffic have shown that the self-similarity is a ubiquitous phenomenon spanning across diverse network environments. In previous work, we have explored the feasibility of exploiting the long-range correlation structure in a self-similar traffic for the congestion control. We have advanced the framework of the multiple time scale congestion control and showed its effectiveness at enhancing performance for the rate-based feedback control. Our contribution is threefold. First, we define a modular extension of the TCP-a function called with a simple interface-that applies to various flavours of the TCP-e.g., Tahoe, Reno, Vegas and show that it significantly improves performance. Second, we show that a multiple time scale TCP endows the underlying feedback control with proactivity by bridging the uncertainty gap associated with reactive controls which is exacerbated by the high delay-bandwidth product in broadband wide area networks. Third, we investigate the influence of the three traffic control dimensions-tracking ability, connection duration, and fairness-on performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.1
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• pp.75-80
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• 2008

Measurement of network traffic have shown that the self-similarity is a ubiquitous phenomenon spanning across diverse network environments. In previous work, we have explored the feasibility of exploiting the long-range correlation structure in a self-similar traffic for the congestion control. We have advanced the framework of the multiple time scale congestion control and showed its effectiveness at enhancing performance for the rate-based feedback control. Our contribution is threefold. First, we define a modular extension of the TCP-a function called with a simple interface-that applies to various flavours of the TCP-e.g., Tahoe, Reno, Vegas and show that it significantly improves performance. Second, we show that a multiple time scale TCP endows the underlying feedback control with proactivity by bridging the uncertainty gap associated with reactive controls which is exacerbated by the high delay-bandwidth product in broadband wide area networks. Third, we investigate the influence of the three traffic control dimensions-tracking ability, connection duration, and fairness-on performance.

• Journal of Internet Computing and Services
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• v.8 no.1
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• pp.15-23
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• 2007

Wireless LAN (IEEE 802.11) uses traditional TCP for reliable data transmission, But it brings the unintentional packet loss which is not congestion loss caused by handoff, interference, and fading in wireless LAN. In wireless LAN, TCP experiences performance degradation because it consumes that the cause of packet loss is congestion, and it decrease the sending rate by activating congestion control algorithm. This paper analyzes that correlation of throughput and buffer size for wireless buffer tuning. We find MBT (Maximum Buffer Threshold) which does not increase the throughput through the analysis, For calculation of MBT, we experiment the throughput by using high volume music data which is creased by real-time performance of piano. The experiment results is shown that buffer tuing based on MBT shows 20.3%, 21.4%, and 45.4% throughput improvement under 5ms RTT, 10ms RTT, and 20ms RTT, respectively, comparing with the throughput of operation system default buffer size, In addition, we describe that The setting of TCP buffer size by exceeding MBT does not have an effect on the performance of TCP.

• The Journal of the Korea Contents Association
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• v.8 no.10
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• pp.76-87
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• 2008

Prediction of travel time on road network is one of crucial research issue in dynamic route guidance system. A new approach based on Rule-Based classification is proposed for predicting travel time. This approach departs from many existing prediction models in that it explicitly consider traffic patterns during day time as well as week day. We can predict travel time accurately by considering both traffic condition of time range in a day and traffic patterns of vehicles in a week. We compare the proposed method with the existing prediction models like Link-based, Micro-T* and Switching model. It is also revealed that proposed method can reduce MARE (mean absolute relative error) significantly, compared with the existing predictors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9B
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• pp.829-837
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• 2006

Due to the internet network congestion, packets may be dropped or delayed at routers. This phenomenon degrades the quality of streaming applications that require high QoS requirements. The proposed algorithm in this paper, called DBRC(Delay-Based Rate Control), tries to cause router queue occupancy to reach a steady state or equilibrium by throttling the transmission rate of the multimedia traffics when network delays tend to increase and also probing for more bandwidth when network delays tend to decrease. Simulation results show that the proposed algorithm provides smooth transmission rate, nearly constant delay and low packet loss rates, compared with TFRC(TCP Friendly Rate Control) that is one of dominant multimedia congestion control algorithms.

• The Journal of Information Systems
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• v.33 no.2
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• pp.125-142
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• 2024

Purpose The purpose of this study is to identify and analyze the key factors influencing congestion in the in-out transportation at port container terminals, and to design of a predictive model for in-out congestion based on these analysis. This study focused on architecting a deep learning-based predictive model. Design/methodology/approach This study was conducted through the following methodology. First, hypotheses were established and data were analyzed to examine the impact of vessel schedules and external truck schedules on in-out transportation. Next, explored time series forecasting models to a design the architecture for deep learning-based predictive model. Findings According to the empirical analysis results, this study confirmed that vessel schedules significantly affect in-out transportation. Specifically, the volume of transportation increases as the vessel arrival/departure time and the cargo cutoff time approach. Additionally, significant congestion patterns in transportation volume depending on the day of the week and the time of day were observed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.183-193
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• 2019

This study developed a shockwave detection and prediction of their extinction point method based on continuous wavelet transform using trajectory data from probe vehicles equipped with automotive sensors.. To analyze the effectiveness of the proposed method, this paper proposed two measures which are a distance error between the extinction points of the predictor and an time-location error of the extinction points. The proposed concept was proved using the micro simulation based experiment with three exogenous variables of traffic volume, lane-close duration, market penetration of probe vehicles. The analysis results show that the proposed method is capable of detecting the traffic shockwaves as well as predicting their extinction point, and also that the accuracy of the proposed method is highly dependent on the rate of the probe vehicles.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.348-359
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• 2017

Recently, various public transportation activation policies are being implemented in order to mitigate traffic congestion in metropolitan areas. Especially in the metropolitan area, the bus information system has been introduced to provide information on the current location of the bus and the estimated arrival time. However, it is difficult to predict the travel time due to repetitive traffic congestion in buses passing through complex urban areas due to repetitive traffic congestion and bus bunching. The previous bus travel time study has difficulties in providing information on route travel time of bus users and information on long-term travel time due to short-term travel time prediction based on the data-driven method. In this study, the path based long-term bus travel time prediction methodology is studied. For this purpose, the training data is composed of 2015 bus travel information and the 2016 data are composed of verification data. We analyze bus travel information and factors affecting bus travel time were classified into departure time, day of week, and weather factors. These factors were used into clusters with similar patterns using self organizing map. Based on the derived clusters, the reference table for bus travel time by day and departure time for sunny and rainy days were constructed. The accuracy of bus travel time derived from this study was verified using the verification data. It is expected that the prediction algorithm of this paper could overcome the limitation of the existing intuitive and empirical approach, and it is possible to improve bus user satisfaction and to establish flexible public transportation policy by improving prediction accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1505-1508
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• 2004

반응적 Ad hoc 라우팅인 AODV의 경로관리는 노드 이동, 노드 혼잡 등의 원인으로 지정시간 내에 응답을 하지 못할 경우 경로 단절을 감지한 뒤 경로복구 과정을 수행한다. 그러나 이러한 경로관리 방법은 능동적이지 못하여 단절 후 경로 재 설정까지 시간 지연이 발생하기 때문에 신속한 경로 복구에 어려움이 있다. 본 논문에서는 기존 AODV에 대한 추가적인 제어 패킷을 발생시키지 않고 경로를 구성하고 있는 이웃노드 Hello 패킷의 수신 시간차를 이용하여 경로 불안정을 예측, Expanding Ring Search 알고리즘을 활용한 효율적인 경로관리 기법을 제시한다.