• Journal of Korea Multimedia Society
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• v.11 no.5
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• pp.685-691
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• 2008

Recently with the rapid evolution of the mobile computing and communication technologies, mobile VOD service becomes increasingly important for wireless mobile users. The VOD service is being widely used in various areas of application, such as education, entertainment and business, because it provides users convenience in easily having access to video information at any time in any places. However, in reality, the mobile system has many difficulties in providing the smooth VOD service owing to frequent transfers and cutoffs of clients. The importance of a technique to transmit broadcasting is being stressed as a method for providing stabler mobile VOD service to a large number of clients. This paper is aimed at showing how to reduce demands for server bandwidth and delay of earlier service through performance analysis by suggesting an effective VOD broadcasting transmission technique through channel division in the mobile atmosphere. Many researches have been made about regular broadcasting techniques in particular. This study divides the methods used for assigning channels which have been decided by the size of segments into a group of regular channels and assistant channels using wireless gap-fillers to provide effective VOD services to a large number of clients at the mobile environment using small bandwidth resources. The regular channels transfer regular streams, while assistant channels repeatedly transfer the first segment to reduce early service delay time to receive regular streams. In this way, the study suggests a technique to reduce server bandwidth demand and early service delay time. Through the proposed technique, the server bandwidth demand could be reduced by more than 30 percent and the study continuously shows reduced early service delay time through conducting performance analysis.

• Journal of Internet Computing and Services
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• v.7 no.1
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• pp.143-150
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• 2006

The quickest path problem deals with the transmission of a message of size ${\sigma}$ from a source to a destination with the minimum end to end delay over a network with bandwidth and delay constraints on the links. We consider two basic modes and four variations for the message delivery at the nodes reflecting the mechanisms such as circuit switching. Internet protocol, and their combinations, For each of first five modes, we present O($m^2+mnlogn$) algorithm to compute the quickest path for a given message size ${\sigma}$, For the last mode, the quickest path can be computed in O(m+nlogn) time.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.145-150
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• 2014

This paper presents an effective vision based method for LED traffic light detection at the daytime. First, the proposed method calculates horizontal coordinates to set region of interest (ROI) on input sequence images. Second, the proposed uses color segmentation method to extract region of green and red traffic light. Next, to classify traffic light and another noise, shape filter and haar-like feature value are used. Finally, temporal delay filter with weight is applied to remove blinking effect of LED traffic light, and state and weight of traffic light detection are used to classify types of traffic light. For simulations, the proposed method is implemented through Intel Core CPU with 2.80 GHz and 4 GB RAM, and tested on the urban and rural road video. Average detection rate of traffic light is 94.50 % and average recognition rate of traffic type is 90.24 %. Average computing time of the proposed method is 11 ms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.2960-2976
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• 2018

The limited storage and computing capacity hinder the development of cognitive radio ad hoc networks (CRAHNs). To solve the problem, a new paradigm of cloud-based CRAHN has been proposed, in which a CRAHN will make use of the computation and storage resources of the cloud. This paper envisions an integrated CRAHN-cloud network architecture. In this architecture, some cognitive radio users (CUs) who satisfy the required metrics could perform as mobile gateway candidates to connect other ordinary CUs with the cloud. These mobile gateway candidates are dynamically clustered according to different related metrics. Cluster head and time-to-live value are determined in each cluster. In this paper, the gateway advertisement and discovery issues are first addressed to propose a hybrid gateway discovery mechanism. After that, a QoS-based gateway selection algorithm is proposed for each CU to select the optimal gateway. Simulations are carried out to evaluate the performance of the overall scheme, which incorporates the proposed clustering and gateway selection algorithms. The results show that the proposed scheme can achieve about 11% higher average throughput, 10% lower end-to-end delay, and 8% lower packet drop fractions compared with the existing scheme.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.93-95
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• 2014

In this paper, we proposed efficient forwarding path computing method using Context-Awareness Mobility Prediction Model. Context-Awareness Mobility Prediction Model is storing and classifying node's previous velocity and direction according to time in the hierarchical cluster structure. To overcome environment which node-to-node connection is broken off easily, the proposed algorithm calculate the connectivity formed matrix structure by comparing predicted velocity and direction, and use masking operation for selecting relay moving to destination. The proposed algorithm identified to show short delay by utilizing forwarding path which is continue node-to-node connection in the unstable situation.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.237-252
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• 2017

In recent years, data centers have become the core infrastructure to deal with big data processing. For these big data applications, network transmission has become one of the most important factors affecting the performance. In order to improve network utilization and reduce job completion time, in this paper, by real-time monitoring from the application layer, we propose job-aware priority scheduling. Our approach takes the correlations of flows in the same job into account, and flows in the same job are assigned the same priority. Therefore, we expect that flows in the same job finish their transmissions at about the same time, avoiding lagging flows. To achieve load balancing, two approaches (Flow-based and Spray) using ECMP (Equal-Cost multi-path routing) are presented. We implemented our scheme using NS-2 simulator. In our evaluations, we emulate real network environment by setting background traffic, scheduling delay and link failures. The experimental results show that our approach can enhance the Hadoop job execution efficiency of the shuffle stage, significantly reduce the network transmission time of the highest priority job.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.1-7
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• 2022

Recently, as the use of applications such as big data programs and machine learning programs that are driven while generating large amounts of data in the program itself becomes common, the existing main memory alone lacks memory, making it difficult to execute the program quickly. In particular, the need to derive results more quickly has emerged in a situation where it is necessary to analyze whether the entire sequence is genetically altered due to the outbreak of the coronavirus. As a result of measuring performance by applying large-capacity data to a computing system equipped with a self-developed memory pool MOCA host adapter instead of processing large-capacity data from an existing SSD, performance improved by 16% compared to the existing SSD system. In addition, in various other benchmark tests, IO performance was 92.8%, 80.6%, and 32.8% faster than SSD in computing systems equipped with memory pool MOCA host adapters such as SortSampleBam, ApplyBQSR, and GatherBamFiles by task of workflow. When analyzing large amounts of data, such as electrical dielectric pipeline analysis, it is judged that the measurement delay occurring at runtime can be reduced in the computing system equipped with the memory pool MOCA host adapter developed in this research.

• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.13-24
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• 2013

IEEE 802.15.4 network and WiFi network are installed to overlap each other and configured to use adjacent frequency bands in which case the communication service required by applications can not be guaranteed because of randomly increased frame transmission delay and frequent frame transmission failures at nodes in IEEE 802.15.4 network. In this paper, transmission delay model at IEEE 802.15.4 nodes and an experimental system to evaluate the interference from WiFi traffic are described, then elements for the evaluation of interference are measured with the analysis of their characteristics. A sequential method of using medium access layer and physical layer elements of IEEE 802.15.4 protocols is proposed to decide interference from WiFi network. With the proposed method, if an evaluation function having frame transmission failures and transmission delay as variables returns a value greater than a threshold, intensive measurements of wireless channel power are carried out subsequently and the final decision of interference is made by the calculated average channel power. Experimental results of the method show that the decision time is reduced with increased frequency of decision in comparison to an other similar method.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.727-732
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• 2015

These days, many researchers are working on Future Internet and a new networking paradigm called Software Defined Networking draws a great attention. In this paper, we redefine Software Defined Networking as Service Defined Networking which means that packets are categorized according to types of services. By using Service Defined Networking, we are not only dealing with the way to utilize the network resources efficiently but we also propose an algorithm to minimize the waiting time for packets to be delivered. This proposed algorithm can solve the delay problem, one of the most significant problems caused by network congestion. Also, since we are adopting Service Defined Networking, network resource utilization can be improved compared to the existing network.