• 인터넷정보학회논문지
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• 제23권2호
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• pp.1-7
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• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• International journal of advanced smart convergence
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• 제11권2호
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• pp.22-29
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• 2022

In this paper, for the purpose of designing an real-time unmanned monitoring system, the YOLOv5s (small) object detection model was applied on the NVIDIA TX2^TM AI (Artificial Intelligence) edge computing platform in order to design the fundamental function of an unmanned monitoring system that can detect objects in real time. YOLOv5s was applied to the our real-time unmanned monitoring system based on the performance evaluation of object detection algorithms (for example, R-CNN, SSD, RetinaNet, and YOLOv5). In addition, the performance of the four YOLOv5 models (small, medium, large, and xlarge) was compared and evaluated. Furthermore, based on these results, the YOLOv5s model suitable for the design purpose of this paper was ported to the NVIDIA TX2^TM AI edge computing system and it was confirmed that it operates normally. The real-time unmanned monitoring system designed as a result of the research can be applied to various application fields such as an security or monitoring system. Future research is to apply NMS (Non-Maximum Suppression) modification, model reconstruction, and parallel processing programming techniques using CUDA (Compute Unified Device Architecture) for the improvement of object detection speed and performance.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2022년도 제66차 하계학술대회논문집 30권2호
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• pp.625-627
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• 2022

FEC (Fog/Edge Computing) 환경에서 지연시간 최소화는 로컬과 에지 서버 간의 효율적인 협력을 보장하기 위한 최적의 계산 오프로딩 방법 결정을 통해 실현될 수 있다. 본 논문은 임의의 응용 서비스 실행모듈에 대한 부분 오프로딩 기반의 로컬(모바일 장치)과 에지 서버 간의 협업 경로를 추출하는 방법을 제안한다. 제안 방법은 다중 분기구조를 포함하는 응용 서비스 실행모듈에 대한 부분 오프로딩 기반의 최적 협업 실행경로 추출 방법을 제안한다. 제안 방법은 각 부분 모듈들의 실행위치에 따라 변화되는 지연시간 측정 및 분석에 적용가능하다.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.7-15
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• 2024

6G network technology represents the next generation of communications, supporting high-speed connectivity, ultra-low latency, and integration with cutting-edge technologies, such as the Internet of Things (IoT), virtual reality, and autonomous vehicles. These advancements promise to drive transformative changes in digital society. However, as technology progresses, the demand for efficient data transmission and energy management between smart devices and network equipment also intensifies. A significant challenge within 6G networks is the optimization of interactions between satellites and smart devices. This study addresses this issue by introducing a new game theory-based technique aimed at minimizing system-wide energy consumption and latency. The proposed technique reduces the processing load on smart devices and optimizes the offloading decision ratio to effectively utilize the resources of Low-Earth Orbit (LEO) satellites. Simulation results demonstrate that the proposed technique achieves a 30% reduction in energy consumption and a 40% improvement in latency compared to existing methods, thereby significantly enhancing performance.

• 한국산업정보학회논문지
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• 제26권1호
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• pp.11-19
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• 2021

5G 네트워크의 등장으로 초저지연 서비스에 대한 요구가 제기되었다. 그러나, 사용자로부터 지리적으로 멀리 위치한 클라우드 센터의 컴퓨팅 서비스로는 이러한 요구를 만족할 수 없다. 이러한 요구에 따라 클라우드 컴퓨팅 서비스를 사용자 근처에 위치한 기지국 혹은 교환국에 전진 배치하여 저지연 서비스를 제공하는 Multi-access Edge Computing (MEC) 기술이 주목받고 있다. 우리는 구글의 Kubernetes를 기반으로 MEC를 위한 클라우드 컴퓨팅 환경을 구축하였다. 이때, 안정적인 동작 확인을 위해 많은 수의 컨테이너가 발생시키는 로드에 강건하게 견딜 수 있는지 실험적으로 확인할 필요가 있다. 이를 위하여 우리는 Kubernetes 환경에서 다양한 컨테이너를 생성하여 네트워크 자원과 컴퓨팅 자원의 안정도를 측정할 수 있는 도구를 개발하였다.

• 사물인터넷융복합논문지
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• 제9권1호
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• pp.125-129
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• 2023

최근 Internet of Things (IoT) 기반 Wireless Networked Control System (WNCS)에서 Sensor의 Status Update 및 Actuator로의 Actuation Update 분석을 위해 정보의 신선도를 측정하는 지표인 Age of Information (AoI)가 고려되고 있다. 또한 WNCS에 Edge Computing (EC)이 도입되면서 기존의 Cloud Computing 기반 아키텍처보다 낮은 AoI를 보장할 수 있다. 하지만 Controller가 관리하는 Sensor의 수가 증가하면서 Controller에 부하가 증가하여 AoI 요구사항을 만족시키지 못하는 문제점이 발생하게 되었다. 본 연구에서는 이러한 문제를 해결하기 위해 Actuator의 컴퓨팅 능력을 활용하여 Sensor의 Status Update를 해당 지역의 Actuator가 가용할 때 직접적으로 전송하여 Actuator가 직접 Actuation Update를 수행함으로써 AoI 요구사항을 만족시키고자 한다. 이를 위해 본 연구에서는 AoI 분석을 위한 분석 모델을 제시하였고 시뮬레이션을 통해 제안하는 방법이 기존 방법 대비 AoI를 줄일 수 있음을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1147-1161
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• 2023

The Internet of Things (IoT) requires a new processing model that will allow scalability in cloud computing while reducing time delay caused by data transmission within a network. Such a model can be achieved by using resources that are closer to the user, i.e., by relying on edge computing (EC). The amount of IoT data also grows with an increase in the number of IoT devices. However, building such a flexible model within a heterogeneous environment is difficult in terms of resources. Moreover, the increasing demand for IoT services necessitates shortening time delay and response time by achieving effective load balancing. IoT devices are expected to generate huge amounts of data within a short amount of time. They will be dynamically deployed, and IoT services will be provided to EC devices or cloud servers to minimize resource costs while meeting the latency and quality of service (QoS) constraints of IoT applications when IoT devices are at the endpoint. EC is an emerging solution to the data processing problem in IoT. In this study, we improve the load balancing process and distribute resources fairly to tasks, which, in turn, will improve QoS in cloud and reduce processing time, and consequently, response time.

• Journal of Information Processing Systems
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• 제16권4호
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• pp.896-914
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• 2020

With the rapid growth of network traffic, a large number of connected devices, and higher application services, the traditional network is facing several challenges. In addition to improving the current network architecture and hardware specifications, effective resource management means the development trend of 5G. Although many existing potential technologies have been proposed to solve the some of 5G challenges, such as multiple-input multiple-output (MIMO), software-defined networking (SDN), network functions virtualization (NFV), edge computing, millimeter-wave, etc., research studies in 5G continue to enrich its function and move toward B5G mobile networks. In this paper, focusing on the resource allocation issues of 5G core networks and radio access networks, we address the latest technological developments and discuss the current challenges for resource management in 5G.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.25-31
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• 2023

Fog computing diversifies cloud computing by using edge devices to provide computing, data storage, communication, management, and control services. As it has a decentralised infrastructure that is capable of amalgamating with cloud computing as well as providing real-time data analysis, it is an emerging method of using multidisciplinary domains for a variety of applications; such as the IoT, Big Data, and smart cities. This present study provides an overview of the security and privacy concerns of fog computing. It also examines its fundamentals and architecture as well as the current trends, challenges, and potential methods of overcoming issues in fog computing.

• IEIE Transactions on Smart Processing and Computing
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• 제3권6호
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• pp.345-352
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• 2014

Edge detection considers the important technical details of digital image processing. Many edge detection operators already perform edge detection in digital color imaging. In this study, the edge of many real color images that represent the type of digital image was detected using a new operator in the least square approximation method, which is a type of numerical method. The Linear Fitting algorithm is computationally more expensive compared to the Canny, LoG, Sobel, Prewitt, HIS, Fuzzy, Parametric, Synthetic and Vector methods, and Robert' operators. The results showed that the new method can detect an edge in a digital color image with high efficiency compared to standard methods used for edge detection. In addition, the suggested operator is very useful for detecting the edge in a digital color image.