• Plant Journal
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• v.17 no.3
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• pp.34-36
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• 2021

The interest in renewable energy-based distributed power generation systems is increasing due to the recognitions of the breakthrough of existing centralized power generation, energy conversion, and environmental problems. In this study, the optimal capacity was selected by simulating a distributed power generation system based on PV and WT using lead acid batteries as the energy storage system. CHP was adopted as the existing power source, and the optimal capacity of the system was derived through MOGA according to the operating modes(full load/part load) of the existing power source. In addition, it was confirmed that the battery life differs when the battery charging method is changed at the same battery capacity. Therefore, for economical and stable power supply and demand, the capacity selection of the distributed generation system considering the battery charging method should be performed.

• KNOM Review
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• v.22 no.1
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• pp.30-41
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• 2019

MQTT protocol is one of open Publish / Subscribe systems for IoT information transmission. In this paper, we are proposing an algorithm to assign a subscriber, which dynamically participate in MQTT clustering system, to an appropriate broker. In MQTT systems with a centralized broker, there are losses of connectivity and messages between subscribers and brokers. In this paper, we addressed this issue for developing scalable open IoT systems and consider clustering MQTT brokers on the SDN infrastructure. In particular, this paper focuses on the problem of allocating subscribers to brokers in accordance with sharing brokers' topics to reduce brokers' load and communication cost in SDN based MQTT cluster. The Experimental results show that the proposed algorithm will reduce the load and the cost as compared to existing methods.

• Journal of Communications and Networks
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• v.11 no.1
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• pp.26-35
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• 2009

In this paper, we investigate how to do resource allocation to guarantee a minimum user data rate at low signaling overhead in multi-cell orthogonal frequency division multiple access (OFDMA) wireless systems. We devise dynamic resource allocation (DRA) algorithms that can minimize the QoS violation ratio (i.e., the ratio of the number of users who fail to get the requested data rate to the total number of users in the overall network). We assume an OFDMA system that allows dynamic control of frequency reuse factor (FRF) of each sub-carrier. The proposed DRA algorithms determine the FRFs of the sub-carriers and allocate them to the users adaptively based on inter-cell interference and load distribution. In order to reduce the signaling overhead, we adopt a hierarchical resource allocation architecture which divides the resource allocation decision into the inter-cell coordinator (ICC) and the base station (BS) levels. We limit the information available at the ICC only to the load of each cell, that is, the total number of sub-carriers required for supporting the data rate requirement of all the users. We then present the DRA with limited coordination (DRA-LC) algorithm where the ICC performs load-adaptive inter-cell resource allocation with the limited information while the BS performs intra-cell resource allocation with full information about its own cell. For performance comparison, we design a centralized algorithm called DRA with full coordination (DRA-FC). Simulation results reveal that the DRA-LC algorithm can perform close to the DRA-FC algorithm at very low signaling overhead. In addition, it turns out to improve the QoS performance of the cell-boundary users, and achieve a better fairness among neighboring cells under non-uniform load distribution.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.83-88
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• 2022

Internet of Things (hereinafter referred to as IoT) related technologies are continuously developing in line with the recent development of information and communication technologies. IoT system sends and receives unique data through network based on various sensors. Data generated by IoT systems can be defined as big data in that they occur in real time, and that the amount is proportional to the amount of sensors installed. Until now, IoT systems have applied data storage, processing and computation through centralized processing methods. However, existing centralized processing servers can be under load due to bottlenecks if the deployment grows in size and a large amount of sensors are used. Therefore, in this paper, we propose a distributed processing system for applying a data importance-based algorithm aimed at the high availability of the system to efficiently handle real-time sensor data arising in IoT environments.

• KNOM Review
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• v.22 no.2
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• pp.22-28
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• 2019

A Science DeMilitarized Zone (DMZ) is an optimized network technology tailored to research data nature. The Science DMZ guarantees end-to-end network performance by forming a closed research network without redundant networking and security devices for the authorized researchers. Data Transfer Node (DTN) is an essential component for the high performance and security of the Science DMZ, since only transfer functions of research data are allowed to the DTN without any security- and performance-threatening functions such as commercial internet service. Current Science DMZ requires per-user DTN server installation which turns out a scalability limitation of the networks in terms of management overhead, entry barrier of the user, and networks-wise CAPEX. In order to relax the aforementioned scalability issues, this paper suggests a centralized DTN design where end users in a group can share the centralized DTN. We evaluate the effectiveness of the suggested sharable DTN design by comparing CAPEX against to that of current design with respect to the diverse network load and the state-of-the-art computing machine.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.507-509
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• 2018

Recently, the research for IoT technologies has been established actively, however the structure of centralized network has been pointed out as the vulnerable points. To solve these problems such as system load and security vulnerability, the research to introduce block chain technology is needed. In this paper, we propose the network domain for convergence of block chain and IoT platform, and describe the advantages from the convergence and various and applicable fields.

• Korean Management Science Review
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• v.10 no.1
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• pp.107-125
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• 1993

Modern telecommunication switching systems are SPC(Stored Program Control) machines handling voice, data and other kinds of traffic, in an environment which tends to be fully digital switching and transmission. The throughput of such systems is determined by the real time capacity of its centralized or distributed control processors and by the traffic capacity of the switching network. Designers must verify the traffic and call processing capacity of the switching system and check its performance under traffic load before it is put into service. Verification of traffic and call processing capacity of switching systems is one of the problems treated by teletraffic studies ; teletraffic studies are based on stochastic process, queueing theory, simulations and other quantitative methods of decision making. This study suggests the general methodology to evaluate the throughput and performance of the ISDN switching system. TDX-10 ISDN switching system are employed to give illustrative examples of the methodologies discussed in this study.

• Journal of Korean Institute of Industrial Engineers
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• v.13 no.2
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• pp.17-34
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• 1987

Modern telecommunication switching systems are SPC (Stored Program Control) machines handling voice, data and other kinds of traffic, in an environment which tends to be fully digital switching and transmission. The throughput of such systems is determined by the real time capacity of its centralized or distributed control processors and by the traffic capacity of the switching network. Designers must verify the traffic and call processing capacity of the switching system and check its performance under traffic load before it is put into service. Verification of traffic and call processing capacity of switching systems is one of the problems treated by teletraffic studies; teletraffic studies are based on stochastic process, queueing theory, simulations and other quantitative methods of decision making. This paper reviews the general methodologies to evaluate the throughput and performance of the digital switching system. TDX-10, which is a fully digital switching system under development in ETRI, is employed to give illustrative examples of the methodologies discussed in this paper.

• Journal of Communications and Networks
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• v.18 no.2
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• pp.135-149
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• 2016

Cloud radio access network (C-RAN) refers to the virtualization of base station functionalities by means of cloud computing. This results in a novel cellular architecture in which low-cost wireless access points, known as radio units or remote radio heads, are centrally managed by a reconfigurable centralized "cloud", or central, unit. C-RAN allows operators to reduce the capital and operating expenses needed to deploy and maintain dense heterogeneous networks. This critical advantage, along with spectral efficiency, statistical multiplexing and load balancing gains, make C-RAN well positioned to be one of the key technologies in the development of 5G systems. In this paper, a succinct overview is presented regarding the state of the art on the research on C-RAN with emphasis on fronthaul compression, baseband processing, medium access control, resource allocation, system-level considerations and standardization efforts.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.161-168
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• 2024

As the size of big data models grows, distributed training is emerging as an essential element for large-scale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.