• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4C
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• pp.308-315
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• 2002

Guaranteed frame rate (GFR) service has been defied to provide minimum cell rate (MCR) guarantees for virtual connections (VCs) carrying Internet traffic in ATM networks and allow them to fairly share residual bandwidth. The simplest switch implementation mechanism to support the GFR service in ATM networks consists of the frame-based generic cell rate algorithm (F-GCRA) frame classifier and the early packet discard (EPD)-like buffer acceptance algorithm in a single FIFO buffer. This mechanism is simple, but has foiled to guarantee the same bandwidth as an MCR to a VC that has reserved a relatively large MCR. This paper applies the packet spacing scheme to TCP traffic to alleviate its burstness, so as to guarantee a larger MCR to a VC. In addition, the random early detection (RED) scheme is added to the buffer acceptance algorithm in order to improve fairness in use of residual bandwidth. Simulation results show that the applied two schemes improve a quality of service (QoS) in the GFR service for the TCP traffic.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.568-573
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• 2019

In the diagnosis of pulmonary diseases, auscultation technique is simpler than the other methods, and lung sounds can be used for predicting the types of pulmonary diseases as well as identifying patients with pulmonary diseases. Therefore, in this paper, we identify patients with pulmonary diseases and classify lung sounds according to their sound characteristics using various convolutional neural networks, and compare the classification performance of each neural network method. First, lung sounds over affected areas of the chest with pulmonary diseases are collected by using a single-channel lung sound recording device, and spectral features are extracted from the collected sounds in time domain and applied to each neural network. As classification methods, we use general, parallel, and residual convolutional neural network, and compare lung sound classification performance of each neural network through experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2631-2649
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• 2021

Device-to-Device (D2D) communication is one of the enabling technologies for 5G networks that support proximity-based service (ProSe) for wireless network communications. This paper proposes a power control algorithm based on the Nash equilibrium and game theory to eliminate the interference between the cellular user device and D2D links. This leadsto reliable connectivity with minimal power consumption in wireless communication. The power control in D2D is modeled as a non-cooperative game. Each device is allowed to independently select and transmit its power to maximize (or minimize) user utility. The aim is to guide user devices to converge with the Nash equilibrium by establishing connectivity with network resources. The proposed algorithm with pricing factors is used for power consumption and reduces overall interference of D2Ds communication. The proposed algorithm is evaluated in terms of the energy efficiency of the average power consumption, the number of D2D communication, and the number of iterations. Besides, the algorithm has a relatively fast convergence with the Nash Equilibrium rate. It guarantees that the user devices can achieve their required Quality of Service (QoS) by adjusting the residual cost coefficient and residual energy factor. Simulation results show that the power control shows a significant reduction in power consumption that has been achieved by approximately 20% compared with algorithms in [11].

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.6
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• pp.21-28
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• 2016

This paper presents a cooperative transmission scheme for underwater acoustic sensor networks to improve packet transmission rate and reduce energy consumption. Source node transmits duplicated information relayed by distributed antennas called a virtual antenna array. Destination node combines that information to reduce packet error rate. The suggested cooperative scheme enhances the reliability by providing high diversity gains through intermediate relay nodes to overcome the distinct characteristics of the underwater channel, such as high transmission loss, propagation delay, and ambient noises. It is suggested that the algorithm select destinations and potential relays from a set of neighboring nodes that utilize distance cost, the residual energy of each node and local measurement of the channel conditions into calculation. Simulation results show that the proposed scheme reduces average energy consumption, response time, and increases packet delivery ratio compared with the SPF(Shortest Path First) and non-cooperative scheme using OPNET Moduler.

• The KIPS Transactions:PartC
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• v.14C no.2
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• pp.185-190
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• 2007

The efficient node energy utilization is one of important performance factors in wireless sensor networks because sensor nodes operate with limited battery power. To extend the lifetime of the wireless sensor networks, maintaining balanced power consumption between sensor nodes is more important than reducing each energy consumption of the sensor node in the network. In this paper, we proposed a cluster formation algorithm to extend the lifetime of the networks and to maintain a balanced energy consumption of nodes. To obtain it, we add a tiny slot in a round frame, which enables to exchange the residual energy messages between the base station (BS). cluster heads, and nodes. The performance of the proposed protocol has been examined and evaluated with the NS 2 simulator. As a result of simulation, we have confirmed that our proposed algorithm show the better performance in terms of lifetime than LEACH. Consequently, our proposed protocol can effectively extend the network lifetime without other critical overhead and performance degradation.

• The KIPS Transactions:PartC
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• v.17C no.3
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• pp.259-270
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• 2010

In this paper, a Location Information-based Gradient Routing (LIGR) algorithm is proposed for setting up routing path based on physical location information of sensor nodes in wireless ad-hoc networks. LIGR algorithm reduces the unnecessary data transmission time, route search time, and propagation delay time of packet by determining the transmission direction and search range through the gradient from the source node to sink node using the physical location information. In addition, the low battery nodes are supposed to have the second or third priority in case of forwarding node selection, which reduces the possibility of selecting the low battery nodes. As a result, the low battery node functions as host node rather than router in the wireless sensor networks. The LIGR protocol performed better than the Logical Grid Routing (LGR) protocol in the average receiving rate, delay time, the average residual energy, and the network processing ratio.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4883-4901
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• 2016

Energy efficiency in Wireless Sensor Networks (WSNs) is very appealing research area due to serious constrains on resources like storage, processing, and communication power of the sensor nodes. Due to limited capabilities of sensing nodes, such networks are composed of a large number of nodes. The higher number of nodes increases the overall performance in data collection from environment and transmission of packets among nodes. In such networks the nodes sense data and ultimately forward the information to a Base Station (BS). The main issues in WSNs revolve around energy consumption and delay in relaying of data. A lot of research work has been published in this area of achieving energy efficiency in the network. Various techniques have been proposed to divide such networks; like grid division of network, group based division, clustering, making logical layers of network, variable size clusters or groups and so on. In this paper a new technique of group based WSNs is proposed by using some features from recent published protocols i.e. "Energy-Efficient Multi-level and Distance Aware Clustering (EEMDC)" and "Energy-Efficient Multi-level and Distance Aware Clustering (EEUC)". The proposed work is not only energy-efficient but also minimizes the delay in relaying of data from the sensor nodes to BS. Simulation results show, that it outperforms LEACH protocol by 38%, EEMDC by 10% and EEUC by 13%.

• Journal of Information Technology Services
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• v.8 no.3
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• pp.171-184
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• 2009

Since the development of the Distributed Hash Tables (DHTs), the distributed lookup services are one of the hot topics in the networking area. The main reason of this popularity is the simplicity of the lookup structure. However, the simple key based search mechanism makes the so called "keyword" based search difficult if not impossible. Thus, the applicability of the DHTs is limited to certain areas. In this paper. we find that DHTs can be used as the ubiquitous sensor network (USN) metadata lookup service across a large number of sensor networks. The popularity of the Ubiquitous Sensor Network has motivated the development of the USN middleware services for the sensor networks. One of the key functionalities of the USN middleware service is the lookup of the USN metadata, by which users get various information about the sensor network such as the type of the sensor networks and/or nodes, the residual of the batteries, the type of the sensor nodes. Traditional distributed hash table based lookup systems are good for one sensor network. However, as the number of sensor network increases, the need to integrate the lookup services of many autonomous sensor networks so that they can provide the users an integrated view of the entire sensor network. In this paper, we provide a hybrid lookup model, in which the autonomous lookup services are combined together and provide seamless services across the boundary of a single lookup services. We show that the hybrid model can provide far better lookup performance than a single lookup system.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.925-928
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• 2010

Wireless sensor networks are composed of a large number of sensor nodes with limited energy resources. One critical issue in wireless sensor networks is how to gather sensed information in an energy efficient way since the energy is limited. The clustering algorithm is a technique used to reduce energy consumption. It can improve the scalability and lifetime of wireless sensor network. In this paper, we introduce a clustering protocol with mode selection (CPMS) for wireless sensor networks. Our scheme improves the performance of BCDCP (Base Station Controlled Dynamic Clustering Protocol) and BIDRP (Base Station Initiated Dynamic Routing Protocol) routing protocol. In CPMS, the base station constructs clusters and makes the head node with highest residual energy send data to base station. Furthermore, we can save the energy of head nodes using modes selection method. The simulation results show that CPMS achieves longer lifetime and more data messages transmissions than current important clustering protocol in wireless sensor networks.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.