• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.47-52
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• 2023

Mobile Edge Computing(MEC) is considered as a promising technology to effectively support the explosively increasing traffic demands. It can provide low-latency services and reduce network traffic by caching contents at the edge of networks such as Base Station(BS). Although users may associate with the nearest BSs, it is more beneficial to associate users to the BS where the requested content is cached to reduce content download latency. Therefore, in this paper, we propose a mobility-aware joint caching and user association algorithm to imporve the cache hit ratio. In particular, the proposed algorithm performs caching and user association based on sojourn time and content preferences. Simulation results show that the proposed scheme improves the performance in terms of cache hit ratio and latency as compared with existing schemes.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.1
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• pp.11-19
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• 2021

The emerging Fifth Generation (5G) network technology brings us a new demand for low latency services. However, it may not be possible for long-distanced cloud computing servers to support users with satisfactory low latency services. For this reason, Multi-access Edge Computing (MEC) technology are gaining attraction since it is designed to provide low latency services to users by placing cloud computing resources to base-stations or mobile switching centers nearby users. Accordingly, it is necessary to verify the deployed containers on the MECs are reliable enough to provide low latency services empirically. For the purpose, we develop a testing tool to verify the reliability as well as network resources status of running MECs by deploying containers on the MECs in a Kubernetes environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.523-535
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• 2017

Local intelligent services aim at controlling local services such as cooling or lightening services in a certain local area, using Internet-of-Things (IoT) sensor data in the area. As the IoT paradigm has evolved, local intelligent services have gained increasing attention. However, most of the local intelligent service mechanism proposed so far do not directly take the users' presence and service preference information into account for controlling local services. This study proposes an individual presence-and-preference-based local service system (IPP-LISS). We present a intelligent service control algorithm and implement a prototype system of IPP-LISS. Typically, the intelligence part of IPP-LISS including the prediction models, is generated on remote server in the cloud because of their compute-intense aspect. However, this can cause huge data traffic between IoT devices and servers in the cloud. The emerging mobile edge computing technology will be a promising solution of this challenge of IPP-LISS. In this paper, we implement IPP-LISS in the cloud, and then, based on the implementation result, we discuss applying the mobile edge computing technology to the IPP-LISS application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.1-6
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• 2022

Recently, IoT sensors are often used to produce stream data locally and they are provided for edge computing applications. Mass-produced data are stored in the mobile device's database for real-time processing and then synchronized with the server when needed. Many mobile databases are developed to support those applications. They are CloudScape, DB2 Everyplace, ASA, PointBase Mobile, etc, and the most widely used database is SQLite3 on Linux. In this paper, we focused on the performance required for synchronization with the server. The search performance required to retrieve SQLite3 was compared and analyzed according to the type of each Linux file system in which the database is stored. Thus, performance differences were checked for each file system according to various search query types, and criteria for applying the more appropriate Linux file system according to the index use environment and table scan environment were prepared and presented.

• ETRI Journal
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• v.42 no.5
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• pp.643-657
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• 2020

Sixth generation will exploit satellite, aerial, and terrestrial platforms jointly to improve radio access capability and unlock the support of on-demand edge cloud services in three-dimensional (3D) space, by incorporating mobile edge computing (MEC) functionalities on aerial platforms and low-orbit satellites. This will extend the MEC support to devices and network elements in the sky and forge a space-borne MEC, enabling intelligent, personalized, and distributed on-demand services. End users will experience the impression of being surrounded by a distributed computer, fulfilling their requests with apparently zero latency. In this paper, we consider an architecture that provides communication, computation, and caching (C3) services on demand, anytime, and everywhere in 3D space, integrating conventional ground (terrestrial) base stations and flying (non-terrestrial) nodes. Given the complexity of the overall network, the C3 resources and management of aerial devices need to be jointly orchestrated via artificial intelligence-based algorithms, exploiting virtualized network functions dynamically deployed in a distributed manner across terrestrial and non-terrestrial nodes.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.437-444
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• 2022

Recently, research on mobile edge services has been conducted to handle computationally intensive and latency-sensitive tasks occurring in wireless networks. However, MEC, which is fixed on the ground, cannot flexibly cope with situations where task processing requests increase sharply, such as commuting time. To solve this problem, a technology that provides edge services using UAVs (Unmanned Aerial Vehicles) has emerged. Unlike ground MEC servers, UAVs have limited battery capacity, so it is necessary to optimize energy efficiency through load balancing between UAV MEC servers. Therefore, in this paper, we propose a load balancing technique with consideration of the energy state of UAVs and the mobility of vehicles. The proposed technique is composed of task offloading scheme using genetic algorithm and task migration scheme using Q-learning. To evaluate the performance of the proposed technique, experiments were conducted with varying mobility speed and number of vehicles, and performance was analyzed in terms of load variance, energy consumption, communication overhead, and delay constraint satisfaction rate.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.85-87
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• 2018

Motivating by two promising technique of 5G, namely D2D and Edge computing, and the above mentioned problem of the current joint studies, We believe that more study is needed on the benefits of joining these two techniques in a single framework by more precisely taking into account the energy needed to computation, sending data, receiving data and as a result achieving more realistic energy efficiency in 5G cellular networks.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1161-1162
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• 2023

모바일 로봇의 자율주행을 위하여 인터넷이 제약된 환경에서도 가능한 Edge computing 에서의 Object Detection 이 필수적이다. 본 논문에서는 이를 위해 Orin 보드에서 YOLOv7 과 Complex_YOLOv4 를 구현하였다. 직접 취득한 데이터를 통해 YOLOv7 을 구현한 결과 0.56 의 mAP 로 프레임당 133ms 가 소요되었다. Kitti Dataset 을 통해 Complex_YOLOv4 를 구현한 결과 0.88 의 mAP 로 프레임당 236ms 가 소요되었다. Comple_YOLOv4 가 YOLOv7 보다 더 많은 데이터를 예측하기에 시간은 더 소요되지만 높은 정확성을 가지는 것을 확인할 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1571-1576
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• 2018

Recently, the attractive changes of mobile device are a improvement of the computing performance, dramatic improvement of storage capacity, constant connection to the internet, and sophisticated development of display technology. As a result, database applications utilizing mobile devices are emerging. These applications include databases for mobile servers, databases for edge computing, and fog computing. Therefore, it is important to pay attention to the current mobile database and pay attention to whether it has suitable performance for the applications. In this paper, the most common mobile database, SQLite3 is selected and experimented to test and understand the update performance and characteristics. The results of experiment are compared with the one of Oracle database at the same condition to evaluate the experiment. As a result, Insert Performance of SQLite3 has a lot of points to be improved and Update performance is very good. Especially, the performance of Range Query is excellent.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.45-47
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• 2022

Mobile edge computing (MEC), which enables mobile terminals to offload computational tasks to a server located at the user's edge, is considered an effective way to reduce the heavy computational burden and achieve efficient computational offloading. In this paper, we study a multi-user MEC system in which multiple user devices (UEs) can offload computation to the MEC server via a wireless channel. To solve the resource allocation and task offloading problem, we take the total cost of latency and energy consumption of all UEs as our optimization objective. To minimize the total cost of the considered MEC system, we propose an DRL-based method to solve the resource allocation problem in wireless MEC. Specifically, we propose a Asynchronous Advantage Actor-Critic (A3C)-based scheme. Asynchronous Advantage Actor-Critic (A3C) is applied to this framework and compared with DQN, and Double Q-Learning simulation results show that this scheme significantly reduces the total cost compared to other resource allocation schemes