• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.89-99
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• 2017

Many works have attempted to solve the scalability, the availability, and the low-latency problems of peer-to-peer (P2P) live video streaming; yet, the problems still remain. While tree-based systems are vulnerable to churn, the mesh-based systems suffer from high delay and overhead. The DHT-aided chunk-driven overlay (DCO) [1] tried to tackle these problems by using the distributed hash table (DHT), which structures into a mesh-based overlay to efficiently share the video segment. However, DCO fully depends on the capacity of the users' device which is small and unstable, i.e., the users' device may leave and join the network anytime, and the video server's bandwidth can be insufficient when the number of users joining the network suddenly increases. Therefore, cloud assist is introduced to overcome those problems. Cloud assist can be used to enhance the availability, the low-latency, and the scalability of the system. In this paper, the DHT is used to maintain the location of the streaming segments in a distributed manner, and the cloud server is used to assist other peers when the bandwidth which required for sharing the video segment is insufficient. The simulation results show that by using the threshold and cloud assist, the availability and the low-latency of the video segments, and the scalability of the network are greatly improved.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.251-263
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• 2019

Recently the huge demand for Internet media streaming has dramatically increased the cost of the CDN (Content Delivery Network) and the need for a means to reduce it is increasing day by day. In this situation, a P2P-assisted DASH technology has recently emerged, which uses P2P (Peer-to-Peer) communications based on WebRTC (Web Real-Time Communication) standards to reduce the CDN cost. This paper proposes an algorithm that can significantly improve CDN cost savings in this technology by selecting peers based on the transport history. Also we implemented this algorithm in an experimental system and, after setting experimental conditions that emulate the actual mobile network environment, we measured the performance of the experimental system. As a result, we demonstrated that the proposed algorithm can achieve higher CDN cost savings compared to the conventional algorithm where peers are selected at random.

• Journal of Internet of Things and Convergence
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• v.7 no.2
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• pp.41-45
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• 2021

This paper proposes a video content delivery scheme for vehicles. Today, we spend a lot of time commuting to work in vehicles such as trains and cars. In addition, the number of users who enjoy video content such as YouTube and Netflix in order to appease the boredom in the vehicle is increasing rapidly. Video content requires a larger amount of data usage than text-based content. Hence, the user's mobile communication data usage increases rapidly along with the cost. The proposed video content delivery scheme downloads a lot of video content in advance when the vehicle is in a free Wi-Fi area. In this way, it is possible to play video content in a vehicle at a low cost. It is expected that the proposed scheme can be applied to the Internet of Things(IoT) for moving objects.

• Smart Media Journal
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• v.9 no.4
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• pp.9-16
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• 2020

Recently, various studies are being conducted to integrate Image Segmentation into smart factory industries and autonomous driving fields. In particular, Image Segmentation systems using deep learning algorithms have been researched and developed enough to learn from large volumes of data with higher accuracy. In order to use image segmentation in the autonomous driving sector, sufficient amount of learning is needed with large amounts of data and the streaming environment that processes drivers' data in real time is important for the accuracy of safe operation through highways and child protection zones. Therefore, we proposed a novel DFCN algorithm that enhanced existing FCN algorithms that could be applied to various road environments, demonstrated that the performance of the DFCN algorithm improved 1.3% in terms of "loss" value compared to the previous FCN algorithms. Moreover, the proposed DFCN algorithm was applied to the existing U-Net algorithm to maintain the information of frequencies in the image to produce better results, resulting in a better performance than the classical FCN algorithm in the autonomous environment.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.135-143
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• 2022

As the world has implemented social distancing and telecommuting due to the spread of COVID-19, real-time streaming sessions based on routing protocols have increased dependence on the Internet due to the activation of video and voice-related content services and cloud computing. BGP is the most widely used routing protocol, and although many studies continue to improve security, there is a lack of visual analysis to determine the real-time nature of analysis and the mis-detection of algorithms. In this paper, we analyze BGP data, which are powdered as normal and abnormal, on a real-world basis, using an anomaly detection algorithm that combines statistical and post-processing statistical techniques with Rule-based techniques. In addition, we present an interactive spatio-temporal analysis plan as an intuitive visualization plan and analysis result of the algorithm with a map and Sankey Chart-based visualization technique.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.5
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• pp.33-41
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• 2009

In this paper, we propose a scheme for voice data retransmission in LR-WPAN to support the required QoS even in the severe channel error environments. In IEEE 802.15.4a, as the user data rate is supported up to 850Kbps, the voice streaming data can be transferred more easily. In this research, we study the beacon-enabled mode in IEEE 802.15.4 LR-WPAN standard with 250Kbps data rate. In the proposed scheme, special slots are dynamically assigned for retransmission of the packet that fails during a voice service, and in the severe channel error environments a time diversity is acquired. Analytical results show that the proposed scheme is more robust and achieves a much higher throughput than the previous protocol in LR-WPAN.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1697-1705
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• 2022

The safety of marine construction workers and marine pollution problems are occurring due to large-scale offshore construction. In particular, underwater construction work in the sea has a higher risk than other work, so it is necessary to apply an unmanned alternative system that considers the safety of the workers. In this paper, the ROV system for offshore construction has been developed for underwater unmanned work. A monitoring system was developed for position control through the control of underwater propellants, pneumatic gripper, and monitoring of underwater work. As a result of the performance evaluation, the underwater movement speed of the ROV was evaluated to be 0.89 m/s, and it was confirmed that the maximum load of the pneumatic gripper was 80 kg. In addition, the network bandwidth required for underwater ROV control and underwater video streaming was evaluated to be more than 300Mbps, wired communication at 92.7 ~ 95.0Mbit/s at 205m, and wireless communication at 78.3 ~ 84.8Mbit/s.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.11
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• pp.319-328
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• 2023

Unlike cloud computing, edge computing technology analyzes and judges data close to devices and users, providing advantages such as real-time service, sensitive data protection, and reduced network traffic. EdgeX Foundry, a representative open source of edge computing platforms, is an open source-based edge middleware platform that provides services between various devices and IT systems in the real world. EdgeX Foundry provides a service for handling camera devices, along with a service for handling existing sensed data, which only supports simple streaming and camera device management and does not store or process image data obtained from the device inside EdgeX. This paper presents a technique that can store and process image data inside EdgeX by applying some of the services provided by EdgeX Foundry. Based on the proposed technique, a service pipeline for object detection services used core in the field of autonomous driving was created for experiments and performance evaluation, and then compared and analyzed with existing methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.210-216
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• 2014

In this paper, it was proposed to develop front and rear image monitoring system for vehicle that help a driver to cope with urgent situation about a dangerous element. When parking a vehicle, the risk factors to be formed by the dead zone can be resolved by using anterior and posterior cameras of the vehicle. In embedded system environment, a SoC(System on Chip) and two high-resolution CMOS (Complementary metal-oxide-semiconductor) image sensors were used to transfer two high-resolution image data through he TCP/ IP-based network. To transfer image data through he TCP/ IP-based network, the images received by two cameras were compressed by using H.264 and they were transmitted with wireless method(Wi-Fi) by using real-time transport protocol (Real-time Transport Protocol). Transmission loss, transmission delay and transmission limit were solved in wireless (Wi-Fi) environment and the bit-rate of two image data compressed by H.264 was adjusted. And the system for the optimal transmission in wireless (Wi-Fi) environment was materialized and experimented.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.115-121
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• 2021

This paper used big data and artificial intelligence technology to predict the rapidly increasing internet traffic. There have been various studies on traffic prediction in the past, but they have not been able to reflect the increasing factors that induce huge Internet traffic such as smartphones and streaming in recent years. In addition, event-like factors such as the release of large-capacity popular games or the provision of new contents by OTT (Over the Top) operators are more difficult to predict in advance. Due to these characteristics, it was impossible for an ISP (Internet Service Provider) to reflect real-time service quality management or traffic forecasts in the network business environment with the existing method. Therefore, in this study, in order to solve this problem, an Internet traffic collection system was constructed that searches, discriminates and collects traffic data in real time, separate from the existing NMS. Through this, the flexibility and elasticity to automatically register the data of the collection target are secured, and real-time network quality monitoring is possible. In addition, a large amount of traffic data collected from the system was analyzed by machine learning (AI) to predict future traffic of OTT operators. Through this, more scientific and systematic prediction was possible, and in addition, it was possible to optimize the interworking between ISP operators and to secure the quality of large-scale OTT services.