• The Journal of the Korea Contents Association
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• v.10 no.9
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• pp.45-56
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• 2010

This paper reports a new mechanism for congestion controls. The proposed congestion detection algorithm can be provided with delay and unnecessary energy consumption. Conventional congestion control methods decide congestion by queue occupancy or mean packet arrival rate of MAC layer only, however, our method can perform precise detection by considering queue occupancy and mean packet arrival rate. In addition, the congestion avoiding method according to congestion degree and scheduling method using priority for real time packets are proposed. Finally, simulation results show that proposed congestion detection and control methods outperforms conventional scheduling schemes for wireless sensor network.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.133-142
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• 2011

When TCP operates in multi-hop wireless networks, it suffers from severe performance degradation due to the different characteristics of wireless networks and wired networks. This is because TCP reacts to wireless packet losses by unnecessarily decreasing its sending rate assuming the losses as congestion losses. Although several loss differentiation algorithms (LDAs) have been proposed to avoid such performance degradation, their detection accuracies are not high as much as we expect. In addition the schemes have a tendency to sacrifice the detection accuracy of congestion losses while they improve the detection accuracy of wireless losses. In this paper, we suggest a new sender-based loss differentiation scheme which enhances the detection accuracy of wireless losses while minimizing the sacrifice of the detection accuracy of congestion losses. Our scheme estimates the rate of queue usage which is highly correlated with the congestion in the network path between a TCP sender and a receiver, and it distinguishes congestion losses from wireless losses by comparing the estimated queue usage with a certain threshold. In the extensive experiments based on a network simulator, QualNet, we measure and compare each detection accuracy of wireless losses and congestion losses, and evaluate the performance enhancement in each scheme. The results show that our scheme has the highest accuracy among the LDAs and it improves the most highly TCP performance in multi-hop wireless networks.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.332-333
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• 2023

본 논문은 사람과 짐의 물체 감지를 위한 YOLO 모델을 활용하여 특정 공간 내 혼잡도를 측정하는 시스템을 제안한다. YOLO를 학습시켜 기차 내에서 사람 및 짐과 같은 객체를 탐지하는 모델을 만든다. 그리고 이 모델을 이용하여 기차 내에서 객체를 탐지하고, 객체의 위치와 개수 정보를 얻는다. 이렇게 얻은 정보를 기반으로, 혼잡도를 측정하기 위해 다양한 지표를 활용한다. 이를 인터페이스에 시각적으로 보여준다. 결과적으로, 제안된 시스템은 승객들의 안전과 편의를 보장하며, 특정 공간의 혼잡도 파악에 유용한 도구로 사용될 수 있다.

• Journal of Advanced Navigation Technology
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• v.14 no.3
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• pp.343-350
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• 2010

Wireless Sensor Network is an event-driven system that a large number of micro sensor nodes are collected, giving and Wing information. Congestion can take place easily since a great number of sensor nodes are sensing data and transferring them to sink nodes when an event occurs, comparing to the existing wired network. Current internet transport protocol is TCP/UDP, however, this is not proper for wireless sensor network event-driven ESRT, STCP and CODA are being studied for reliable data transport in the wireless sensor network. Congestion are being detected local buffer and channel loading with these techniques. Broadcasting is mainly used and can avoid congestion when one happens. The way that combining local buffer and channel loading information and detecting congestion is being studied in this paper. When congestion occurs, buffering state is divided into three in order to lessen the broadcasting sending the message on congestion control to the node having frequent channel loading. Thus they have advantage of decreasing network's loading.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.1-10
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• 2018

Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. Recently, some studies have applied information technology to a livestock management system to minimize the damage resulting from such anomalies. Nonetheless, detecting each pig in a crowed pigsty is still challenging problem. In this paper, we propose a new Kinect camera and deep learning-based monitoring system for the detection of the individual pigs. The proposed system is characterized as follows. 1) The background subtraction method and depth-threshold are used to detect only standing-pigs in the Kinect-depth image. 2) The standing-pigs are detected by using YOLO (You Only Look Once) which is the fastest and most accurate model in deep learning algorithms. Our experimental results show that this method is effective for detecting individual pigs in real time in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (average 99.40% detection accuracies).

• The KIPS Transactions:PartC
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• v.16C no.2
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• pp.237-244
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• 2009

The congestion in wireless sensor network increases the ratio of data loss and causes the delay of data. The existing congestion protocols for wireless sensor network reduces the amount of transmission by control the sampling frequency of the sensor nodes related to the congestion when the congestion has occurred and was detected. However, the control method of sampling frequency is not applicable on the situation which is sensitive to the temporal data loss. In the paper, we propose a new congestion control, ACT - Adaptive Congestion conTrol. The ACT monitors the network traffic with the queue usage and detects the congestion based on the multi level threshold of queue usage. Given network congestion, the ACT increases the efficiency of network by adaptive flow control method which adjusts the frequency of packet transmission and guarantees the fairness of packet transmission between nodes. Furthermore, ACT increases the quality of data by using the variable compression method. Through experiment, we show that ACT increases the network efficiency and guarantees the fairness to sensor nodes compared with existing method.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.379-381
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• 2021

비디오 모니터링은 자율주행차뿐만 아니라 농장 내 병든 동물 탐지 등과 같은 스마트팜 분야에서도 사람을 대신하여 24시간 연속 모니터링할 수 있는 중요한 응용 분야이다. 본 논문에서는 비디오 모니터링의 계산양을 줄이면서도 혼잡한 돈방에서 빠르게 움직이는 돼지들을 정확히 탐지하기 위해 CNN 기반 객체 탐지기의 정확도를 고려한 방법을 제안한다. 즉, 연속되는 비디오 영상에서 key frame을 먼저 추출한 후, 비디오의 특성인 움직임 정보가 포함된 영상에서 GMM을 이용하여 움직인 돼지와 움직이지 않은 돼지의 위치를 구분하고, 최종적으로 YOLOv4를 적용하여 움직인 돼지와 움직이지 않은 돼지를 탐지한다. 돈사에서 촬영된 비디오 데이터로 실험한 결과, 제안 방법은 효과적으로 움직인 돼지를 탐지할 수 있음을 확인하였다.

• Journal of Advanced Navigation Technology
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• v.18 no.5
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• pp.461-467
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• 2014

Active queue management (AQM) algorithms run on routers and detect incipient congestion by typically monitoring the instantaneous or average queue size. When the average queue size exceeds a certain threshold, AQM algorithms infer congestion on the link and notify the end systems to back off by proactively dropping some of the packets arriving at a router or marking the packets to reduce transmission rate at the sender. Among the existing AQM algorithms, random early detection (RED) is well known as the representative queue-based management scheme by randomizing packet dropping. To reduce the number of timeouts in TCP and queuing delay, maintain high link utilization, and remove bursty traffic biases, the RED considers an average queue size as a degree of congestions. However, RED do not well in the specified networks conditions due to the fixed parameters($P_{max}$ and $TH_{min}$) of RED. This paper addresses a extended RED to be adapted in various networks conditions. By sensing network state, $P_{max}$ and $TH_{min}$ can be automatically changed to proper value and then RED do well in various networks conditions.

• Journal of KIISE:Information Networking
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• v.35 no.6
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• pp.474-481
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• 2008

Scanning worm increases network traffic load and result in severe network congestion because it is a self-replicating worm and send copies of itself to a number of hosts through the Internet. So an early detection system which can automatically detect scanning worms is needed to protect network from those attacks. Although many studies are conducted to detect scanning worms, most of them are focusing on the method using packet header information. The method using packet header information has long detection delay since it must examine the header information of all packets entering or leaving the network. Therefore we propose an algorithm to detect scanning worms using network traffic characteristics such as variance of traffic volume, differentiated traffic volume, mean of differentiated traffic volume, and product of mean traffic volume and mean of differentiated traffic volume. We verified the proposed algorithm by analyzing the normal traffic captured in the real network and the worm traffic generated by simulator. The proposed algorithm can detect CodeRed and Slammer which are not detected by existing algorithm. In addition, all worms were detected in early stage: Slammer was detected in 4 seconds and CodeRed and Witty were detected in 11 seconds.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10c
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• pp.378-380
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• 2000

재경로설정이란 MPLS 망에서 보호해야 할 트래픽이 노드나 링크가 에러이거나 혼잡이 발생했을 때 대체경로로 그 트래픽을 우회시켜 신뢰성 있는 서비스를 제공하는 기술이다. 본 논문에서 제안하는 기법은 QoS 및 최단경로를 고려하여 동적으로 대체(backup) 경로를 설정하는 방법이다. 노드 에러를 탐지했을 경우에는 에러를 탐지한 노드가 에러가 발생한 노드의 다음 노드까지 QoS 및 최단경로를 고려하여 대체 경로를 설정하고 링크 에러를 탐지했을 경우에는 두 노드 사이에 QoS 및 최단경로를 고려하여 대체 경로를 설정함으로써 신뢰성 있는 데이터 전송을 보장할 수 있다. 본 논문에서 제안하는 기법의 타당성을 검증하기 위하여 시뮬레이션을 통하여 기존에 제안된 기법들과 비교 분석한다.