• Journal of the Korea Society of Computer and Information
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• v.5 no.3
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• pp.126-131
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• 2000

Through many users employ TCP of which the performance has been proved in Internet, but many papers Proposed to improve TCP performance according to varying network architecture. In Particular, BWDP(bandwidth-delay Product) grew larger because of the increasing delay in satellite link and the network's speed-up. To consider these increased bandwidth-delay product, it is suggested that TCP options include Window Scale option. TimeStamp option, and PAWS. Because TCP window size should be commonly high in the network with these increased bandwidth-delay product, the multiple decrease and linear increase scheme of current TCP would cause underflow and instability within network. Then TCP performance is reduced as a result. Thus, to improve TCP congestion control algorithm in the network which has large sized window, this paper proposes the congestion control scheme that controls window size by using TimeStamp option.

• Journal of KIISE:Information Networking
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• v.36 no.5
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• pp.413-424
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• 2009

Wireless sensor network is based on an event driven system. Sensor nodes collect the events in surrounding environment and the sensing data are relayed into a sink node. In particular, when events are detected, the data sensing periods are likely to be shorter to get the more correct information. However, this operation causes the traffic congestion on the sensor nodes located in a routing path. Since the traffic congestion generates the data queue overflows in sensor nodes, the important information about events could be missed. In addition, since the battery energy of sensor nodes exhausts quickly for treating the traffic congestion, the entire lifetime of wireless sensor networks would be abbreviated. In this paper, a new congestion control method is proposed on the basis of genetic algorithm. To apply genetic algorithm, the data traffic rate of each sensor node is utilized as a chromosome structure. The fitness function of genetic algorithm is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets, the proposed method selects the optimal data forwarding sensor nodes for relieving the traffic congestion. In experiments, when compared with other methods to handle the traffic congestion, the proposed method shows the efficient data transmissions due to much less queue overflows and supports the fair data transmission between all sensor nodes as possible. This result not only enhances the reliability of data transmission but also distributes the energy consumptions across the network. It contributes directly to the extension of total lifetime of wireless sensor networks.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.526-533
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• 2008

We propose a wavelet neural network (WNN) control method for active queue management (AQM) in an end-to-end TCP network, which is trained by adaptive learning rates (ALRs). In the TCP network, AQM is important to regulate the queue length by passing or dropping the packets at the intermediate routers. RED, PI, and PID algorithms have been used for AQM. But these algorithms show weaknesses in the detection and control of congestion under dynamically changing network situations. In our method, the WNN controller using ALRs is designed to overcome these problems. It adaptively controls the dropping probability of the packets and is trained by gradient-descent algorithm. We apply Lyapunov theorem to verify the stability of the WNN controller using ALRs. Simulations are carried out to demonstrate the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.22-24
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• 2000

As a competition is introduced in the electricity supply industry, a congestion problem arises in the transmission network. The congestion causes the transmission cost to increase. One way to decrease the congestion cost is to control the transmission flow through the installation of FACTS(Flexible AC Transmission system). This paper deals with the optimal site of the FACTS for reducing the congestion cost using a shadow price which is one of the economic signals for the systems. Test results show that the site of the FACTS(UPFC) is optimal to minimize the congestion cost by the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.5
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• pp.220-225
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• 2000

As a competition is introduced in the electricity supply industry, a congestion problem arises in the transmission network. The congestion causes the transmission cost to increase. One way to decrease the congestion cost is to control the transmission flow through the installation of TCSC(Thyrister Controlled Series Capacitor). This paper deals with the optimal site of the TCSC for reducing the congestion cost using a shadow price which is one of the economic signals for the systems. Test results show that the site of the TCSC is optimal to minimize the congestion cost by the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.359-365
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• 2005

This paper presents an reduced-order near-optimal controller for the congestion control of Available Bit Rate (ABR) service in Asynchronous Transfer Mode (ATM) networks. We introduce the model, of a class of ABR traffic, that can be controlled using a Explicit Rate feedback for congestion control in ATM networks. Since there are great computational complexities in the class of optimal control problem for the ABR model, the near-optimal controller via reduced-order technique is applied to this model. It is implemented by the help of weakly coupling and singular perturbation theory, and we use bilinear transformation because of its computational convenience. Since the bilinear transformation can convert discrete Riccati equation into continuous Riccati equation, the design problems of optimal congestion control can be reduced. Using weakly coupling and singular perturbation theory, the computation time of Riccati equations can be saved, moreover the real-time congestion control for ATM networks can be possible.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.17-26
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• 2019

The Constrained Application Protocol (CoAP) is a specialized web transfer protocol proposed by the IETF for use in IoT environments. CoAP was designed as a lightweight machine-to-machine protocol for resource constrained environments. Due to the strength of low overhead, the number of CoAP devices is expected to rise rapidly. When CoAP runs over UDP for wireless sensor networks, CoAP needs to support congestion control mechanisms. Since the default CoAP defines a minimal mechanism for congestion control, several schemes to improve the mechanism have been proposed. To keep CoAP lightweight, the majority of the schemes have been focused mainly on how to measure RTT accurately and how to set RTO adaptively according to network conditions, but other approaches such as rate-based congestion control were proposed more recently. In this paper, we survey the literature on congestion control for CoAP and discuss the future research directions.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.325-327
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• 2005

In this paper, we propose the design of active queue management (AQM) control system using the self-recurrent wavelet neural network (SRWNN). By regulating the queue length close to reference value, AQM can control the congestions in TCP network. The SRWNN is designed to perform as a feedback controller for TCP dynamics. The parameters of network are tunes to minimize the difference between the queue length of TCP dynamic model and the output of SRWNN using gradient-descent method. We evaluate the performances of the proposed AQM approach through computer simulations.

• Proceedings of the Safety Management and Science Conference
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• 2002.05a
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• pp.121-127
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• 2002

In this paper, we propose traffic management support and evaluate the performance through simulation. We suggest traffic management routing protocol that can guarantee reliance according to not only reduction of the Network traffic congestion but also distribution of the network load that prevents data transmission. For performance evaluation, we analyzed the average data reception rate and network load, considering the node mobility. We found that in the mobile Ad Hoc networks, the traffic management service increased the average data reception rate and reduced the network traffic congestion and network load in Mobile Ad Hoc Networks.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.67-78
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• 2017

Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.