• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.345-352
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• 2022

Recent LiDAR(Light Detection And Ranging) sensor is used for scanning object around in real-time. This sensor can detect movement of the object and how it has changed. As the production cost of the sensors has been decreased, LiDAR begins to be used for various industries such as facility guard, smart city and self-driving car. However, LiDAR has a large input data size due to its real-time scanning process. So another way for processing a large amount of data are needed in LiDAR system because it can cause a bottleneck. This paper proposes edge computing to compress massive point cloud for processing quickly. Since laser's reflection range of LiDAR sensor is limited, multiple LiDAR should be used to scan a large area. In this reason multiple LiDAR sensor's data should be processed at once to detect or recognize object in real-time. Edge computer compress point cloud efficiently to accelerate data processing and decompress every data in the main cloud in real-time. In this way user can control LiDAR sensor in the main system without any bottleneck. The system we suggest solves the bottleneck which was problem on the cloud based method by applying edge computing service.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.176-190
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• 2019

The prevention of pedestrian accidents in crosswalks and intersections is very important. The C-ITS services provide a warning service for preventing accidents between cars and pedestrians. In the current pedestrian collision prevention warning service according to the C-ITS standard, however, it is difficult to provide real-time service because it detects pedestrians from a video-analysis server in the control center and sends service messages through the ITS system. This paper proposes a pedestrian collision-prevention warning system that detects pedestrians in the local field using an edge camera and sends a warning message directly to the driver through a roadside unit. An evaluation showed that the proposed system could deliver the pedestrian collision prevention-warning message to the driver satisfying the delay time within the 300 ms required by the C-ITS standard, even in the worst case.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.7
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• pp.167-176
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• 2019

Recently, as the types of services that can be enjoyed in mobile telecommunication networks such as social networks and video streaming are increasing, mobile users(MUs) can access mobile contents easily by consuming mobile data. However, under a mobile telecommunication environment, MUs have to pay a high data fee to a network service provider(SP) in order to enjoy contents. The 'data sponsor' technique, introduced as a way to solve this problem, has attracted attention as a breakthrough method for enhancing contents accessibility of MUs. In this paper, we propose an algorithm that determines the optimal discount rate through the Stackelberg game in the data sponsor environment. We also propose an algorithm to design edge caching, which caches highly popular content for MUs on edge server, through many-to-many matching game. Simulation results clearly indicate that the profit for CP's content consumption is improved by about 6~11%, and the profit of CP according to the ratio of edge caching is improved by about 12% than the other existing schemes under data sponsor environment.

• Journal of IKEEE
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• v.28 no.3
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• pp.458-464
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• 2024

This study proposes a fault-tolerant caching system to address the issue of caching content imbalance caused by the dynamic departure and participation of cache servers in a heterogeneous cache server network, and validates it in both real and virtual environments. With the increase of large-scale media content requiring various types and resolutions, the necessity of cache servers as key components to reduce response time to user requests and alleviate network load has been growing. In particular, research on heterogeneous cache server networks utilizing edge computing and low-power devices has been actively conducted recently. However, in such environments, the irregular departure and participation of cache servers can occur frequently, leading to content imbalance among the cache servers deployed in the network, which can degrade the performance of the cache server network. The fault-tolerant caching algorithm proposed in this study ensures stable service quality by maintaining balance among media contents even when cache servers depart. Experimental results confirmed that the proposed algorithm effectively maintains content distribution despite the departure of cache servers. Additionally, we built a network composed of seven heterogeneous cache servers to verify the practicality of the proposed caching system and demonstrated its performance and scalability through a large-scale cache server network in a virtual environment.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.470-476
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• 2020

In this paper, we present the design of modular replaceable AI server for image deep learning that separates the server from the Edge Device so as to drive the AI block and the method of data transmission and reception. The modular replaceable AI server for image deep learning can reduce the dependency between social robots and edge devices where the robot's platform will be operated to improve drive stability. When a user requests a function from an AI server for interaction with a social robot, modular functions can be used to return only the results. Modular functions in AI servers can be easily maintained and changed by each module by the server manager. Compared to existing server systems, modular replaceable AI servers produce more efficient performance in terms of server maintenance and scale differences in the programs performed. Through this, more diverse image deep learning can be included in robot scenarios that allow human-robot interaction, and more efficient performance can be achieved when applied to AI servers for image deep learning in addition to robot platforms.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.11
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• pp.291-296
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• 2021

The development of the Internet of Things(IoT) requires large computational resources for tasks from numerous devices. Mobile Edge Computing(MEC) has attracted a lot of attention in the IoT environment because it provides computational resources geographically close to the devices. Task offloading to MEC servers is efficient for devices with limited battery life and computational capability. In this paper, we assumed an industrial IoT environment requiring high reliability. The complexity of optimization problem in industrial IoT environment with many devices and multiple MEC servers is very high. To solve this problem, the problem is divided into two. After selecting the MEC server considering the queue status of the MEC server, we propose an offloading decision algorithm that optimizes reliability and energy consumption using genetic algorithm. Through experiments, we analyze the performance of the proposed algorithm in terms of energy consumption and reliability.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.57-58
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• 2016

ICT의 보급, 확대는 데이터 센터의 중요성을 높이고 보다 성능이 좋으며 체적 당 소비전력이 큰 서버를 수용할 수 있는 데이터 센터의 수요를 창출하고 있다. 현재 데이터 센터는 데이터 센터 활용 시에 구성요소들에 대한 상당한 대역폭을 필요로 하나 현 데이터센터에 적용된 토폴로지는 고성능 IP 스위치/라우터를 사용하더라도 네트워크 엣지 계층에서는 기본 활용도의 50%의 bandwidth밖에 지원하지 못한다. 따라서 이러한 문제를 해결하기 위해 OMNeT++을 이용하여 데이터 센터 토폴로지 중 하나인 Fat-tree를 모델링하고 데이터 센터 제반 환경을 구축, latency, power consumption, heat dissipation 등의 기준지표를 성능평가 하였다.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.259-266
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• 2021

Edge Computing enables real-time big data processing by performing computing close to the physical location of the user or data source. However, in an edge computing environment, various situations that affect big data processing performance may occur depending on temporary service requirements or changes of physical resources in the field. In this paper, we proposed a BigCrawler system that dynamically configures the computing module and storage module according to the big data collection status and computing resource usage status in the edge computing environment. And the feature of big data processing workload according to the arrangement of computing module and storage module were analyzed.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1090-1100
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• 2021

Recently, as indoor activities increase due to the spread of infectious diseases, the metaverse is attracting attention. Metaverse refers to content in which the virtual world and the real world are closely related, and its representative platform technology is VR(Virtual Reality). However, since VR hardware is difficult to access in terms of cost, the concept of streaming-based cloud VR has emerged. This study proposes a server configuration and deployment method in an edge network when metaverse content involving multiple users operates based on cloud VR. The proposed algorithm deploys the edge server in consideration of the network and computing resources and client location for cloud VR, which requires a high level of computing resources while at the same time is very sensitive to latency. Based on simulation, it is confirmed that the proposed algorithm can effectively reduce the total network traffic load regardless of the number of applications or the number of users through comparison with the existing deployment method.

• Annual Conference of KIPS
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• 2021.11a
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• pp.100-103
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• 2021

5G 의 발전과 함께 차량과 IT 통신 기술을 융합한 어플리케이션들이 급증하면서 멀티 액세스 엣지 컴퓨팅(MEC)이 차세대 기술로 등장했다. 낮은 지연시간 안에 계산 집약적인 서비스들을 제공하기 위해 단독적인 MECS 서버(MECS)에서의 수행이 아닌 다수의 MECS 에서 동시에 연산을 수행할 수 있도록 태스크를 파티셔닝하는 기법이 주목받고 있다. 특히 차량이 다수의 MECS 로 태스크를 파티셔닝하여 오프로딩하는 기법과 하나의 MECS 로 오프로딩한 후 다른 MECS 들로 파티셔닝하여 마이그레이션하는 기법들이 연구되고 있다. 본 논문에서는 오프로딩과 마이그레이션을 이용한 파티셔닝 기법들을 서비스 지연시간과 차량의 에너지 소비량 측면에서 성능을 비교 분석을 하였다.