• Journal of Information Processing Systems
• /
• v.20 no.2
• /
• pp.226-238
• /
• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.

• Journal of KIISE:Computer Systems and Theory
• /
• v.26 no.7
• /
• pp.827-838
• /
• 1999

멀티미디어 통신에서 통신 성능에 관련된 요구 사항들은 QoS 매개 변수들로서 서술된다. QoS매개 변수들에서 중요한 매개 변수 중의 하나가 전송 신뢰성이다. QoS 매개 변수로서의 신뢰성은 오류 감지, 보고 그리고 정정 기법으로 정의된다. 하지만 기존의 오류 제어 기법들은 멀티미디어 데이타의 통합된 관점을 고려하지 않았다. 그래서 우리는 MEC(multimedia-oriented error control)라고 명명된 오류 제어 기법을 제안하였다. 1 본 논문에서는 MEC기법의 성능을 조사 하였다. 성능평가 결과는 MEC기법이 기존의 오류 제어 기법보다도 낮은 전송지연(lower delay)과 호손율(blocking probability) 을 갖는다는 것을 볼 수 있었다. 결국 제안된 MEC기법은 수송 프로토콜에게 유연성과 높은 성능을 갖도록 해준다.Abstract Communication performance requirements are described as QoS parameters in multimedia communication. One of the important QoS parameters is the reliability of the transfer. As a QoS parameter, the reliability defines error detection, report and correction mechanisms. Conventional error control mechanisms, however, do not consider the integrated viewpoint of multimedia data. So we have proposed the MEC(multimedia-oriented error control). 1 In this paper, we have investigated the performance evaluation of the MEC. The results show that the MEC mechanism provides lower transfer delay and blocking probability than those of the conventional error recovery mechanism. Therefore, the proposed MEC mechanism makes the transport protocol have the flexibility and high performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.6
• /
• pp.61-67
• /
• 2023

The rise of 5G and the proliferation of smart devices have underscored the significance of multi-access edge computing (MEC). Amidst this trend, interest in effectively processing computation-intensive and latency-sensitive applications has increased. This study investigated a novel task offloading strategy considering the probabilistic MEC environment to address these challenges. Initially, we considered the frequency of dynamic task requests and the unstable conditions of wireless channels to propose a method for minimizing vehicle power consumption and latency. Subsequently, our research delved into a deep reinforcement learning (DRL) based offloading technique, offering a way to achieve equilibrium between local computation and offloading transmission power. We analyzed the power consumption and queuing latency of vehicles using the deep deterministic policy gradient (DDPG) and deep Q-network (DQN) techniques. Finally, we derived and validated the optimal performance enhancement strategy in a vehicle based MEC environment.

• The Journal of Korea Robotics Society
• /
• v.7 no.1
• /
• pp.1-8
• /
• 2012

A humanoid robot hand with one thumb and two fingers has been developed. Each finger has the specially designed compact joints, called "MEC Joint", which convert the rotation of a motor to the swing motion of a pendulum. The robot hand with the MEC Joints is compact and relatively light but strong enough to grasp objects in the same manner as human being does in daily activities. In this paper the kinematic model and the torque characteristics of the MEC Joint are presented and compared with the results of the dynamic simulation and the dynamometer test. The dynamic behavior of the thumb and two fingers with MEC Joints are also presented by computer simulation.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.3
• /
• pp.427-432
• /
• 2023

In this study, MEC (: Multi-access Edge Computing) proposes a cloud service network configuration for various tests of autonomous vehicles to which V2X (: Vehicle to Everything) is applied in Wave, LTE, and 5G networks and MEC App (: Application) applied V2X service function test verification of two domains (operator (KT, SKT, LG U+), network type (Wave, LTE (including 3G), 5G)) in a specific region. In 4G networks of domestic operators (SKT, KT, LG U+ and Wave), MEC summarized the improvement effects through V2X function blocks and traffic offloading for the purpose of bringing independent network functions. And with a high level of QoS value in the V2X VNF of the 5G network, the traffic steering function scenario was demonstrated on the destination-specific traffic path.

• The Journal of Korea Robotics Society
• /
• v.17 no.2
• /
• pp.118-123
• /
• 2022

We implemented a real-time cloud robotics application by offloading robot navigation engine over to 5G Mobile Edge Computing (MEC) sever. We also ran a fleet management system (FMS) in the server and controlled the movements of multiple robots at the same time. The mobile robots under the test were connected to the server through 5G SA network. Public 5G network, which is already commercialized, has been temporarily modified to support this validation by the network operator. Robot engines are containerized based on micro-service architecture and have been deployed using Kubernetes - a container orchestration tool. We successfully demonstrated that mobile robots are able to avoid obstacles in real-time when the engines are remotely running in 5G MEC server. Test results are compared with 5G Public Cloud and 4G (LTE) Public Cloud as well.

• Journal of Information Processing Systems
• /
• v.18 no.3
• /
• pp.389-401
• /
• 2022

The business of Internet of Vehicles (IoV) is growing rapidly, and the large amount of data exchange has caused problems of large mobile network communication delay and large energy loss. A strategy for resource allocation of IoV communication based on mobile edge computing (MEC) is thus proposed. First, a model of the cloud-side collaborative cache and resource allocation system for the IoV is designed. Vehicles can offload tasks to MEC servers or neighboring vehicles for communication. Then, the communication model and the calculation model of IoV system are comprehensively analyzed. The optimization objective of minimizing delay and energy consumption is constructed. Finally, the on-board computing task is coded, and the optimization problem is transformed into a knapsack problem. The optimal resource allocation strategy is obtained through genetic algorithm. The simulation results based on the MATLAB platform show that: The proposed strategy offloads tasks to the MEC server or neighboring vehicles, making full use of system resources. In different situations, the energy consumption does not exceed 300 J and 180 J, with an average delay of 210 ms, effectively reducing system overhead and improving response speed.

• Investigative Magnetic Resonance Imaging
• /
• v.20 no.1
• /
• pp.66-70
• /
• 2016

Introduction: Distant metastases of mucoepidermoid carcinoma (MEC) are reported with the most common sites being the soft tissue of skin, lung, liver, and bone. We report here a very rare case of MEC with multiple metastases to the kidney, adrenal gland, skull and gluteus maximus muscle. Case report: A 63-year-old male patient presented with left-sided headache. Radiologic evaluations including CT and MRI showed ill-defined soft tissue lesion involving the left infratemporal fossa and left sphenoid sinus, and multiple enlarged lymph nodes in neck and mediastinum. PET-CT demonstrated multiple hypermetabolic lesions in and around the left kidney, left adrenal gland, right ischium, right gluteus maximus and skull base. These lesions were confirmed as MEC with multiple metastases through biopsy. Discussion: Only one case of metastasis to the skull has been previously reported, and moreover, there has not been a case of metastatic MEC to the kidney, adrenal gland and gluteus maximus muscle so far in the medical literature. It is important to acknowledge the possibility of every unusual MEC metastases, since the presence of metastasis has statistically significant influence on the survival of MEC.

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

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