• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4682-4705
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• 2020

The Unmanned Aerial Vehicles (UAV) networks consisting of low-cost UAVs are very vulnerable to smart jammers that can choose their jamming policies based on the ongoing communication policies accordingly. In this article, we propose a novel cloud and edge-aided mobile communication scheme for low-cost UAV network against smart jamming. The challenge of this problem is to design a communication scheme that not only meets the requirements of defending against smart jamming attack, but also can be deployed on low-cost UAV platforms. In addition, related studies neglect the problem of decision-making algorithm failure caused by intermittent ground-to-air communication. In this scheme, we use the policy network deployed on the cloud and edge servers to generate an emergency policy tables, and regularly update the generated policy table to the UAVs to solve the decision-making problem when communications are interrupted. In the operation of this communication scheme, UAVs need to offload massive computing tasks to the cloud or the edge servers. In order to prevent these computing tasks from being offloaded to a single computing resource, we deployed a lightweight game algorithm to ensure that the three types of computing resources, namely local, edge and cloud, can maximize their effectiveness. The simulation results show that our communication scheme has only a small decrease in the SINR of UAVs network in the case of momentary communication interruption, and the SINR performance of our algorithm is higher than that of the original Q-learning algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.4025-4041
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• 2020

Mobile edge computing (MEC) is capable of providing services to smart devices nearby through radio access networks and thus improving service experience of users. In this paper, an offloading strategy for the joint optimization of computing and communication resources in multi-user and multi-MEC overlapping scene was proposed. In addition, under the condition that wireless transmission resources and MEC computing resources were limited and task completion delay was within the maximum tolerance time, the optimization problem of minimizing energy consumption of all users was created, which was then further divided into two subproblems, i.e. offloading strategy and resource allocation. These two subproblems were then solved by the game theory and Lagrangian function to obtain the optimal task offloading strategy and resource allocation plan, and the Nash equilibrium of user offloading strategy games and convex optimization of resource allocation were proved. The simulation results showed that the proposed algorithm could effectively reduce the energy consumption of users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권7호
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• pp.3329-3350
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• 2017

Installing caching and computing facilities in mobile edge networks is a promising solution to cope with the challenging capacity and delay requirements imposed on future mobile communication systems. The problem of optimal facility placement in mobile edge networks has not been fully studied in the literature. This is a non-trivial problem because the mobile edge network has a unidirectional topology, making existing solutions inapplicable. This paper considers the problem of optimal placement of a fixed number of facilities in a mobile edge network with an arbitrary tree topology and an arbitrary demand distribution. A low-complexity sequential algorithm is proposed and proved to be convergent and optimal in some cases. The complexity of the algorithm is shown to be $O(H^2{\gamma})$, where H is the height of the tree and ${\gamma}$ is the number of facilities. Simulation results confirm that the proposed algorithm is effective in producing near-optimal solutions.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2019년도 제59차 동계학술대회논문집 27권1호
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• pp.107-108
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• 2019

모바일 엣지 컴퓨팅(Mobile Edge Computing, MEC)은 높은 컴퓨팅 성능을 요구하는 작업을 모바일 장치에서 가까운 MEC 서버로 오프로딩함으로써 모바일 서비스에 높은 계산 요구량을 효율적으로 제공할 수 있는 기술로 부상하였다. 본 논문에서는 실행 대기 시간과 장치 에너지 소비를 줄이기 위해 여러 가지의 독립적 작업을 통해 MEC 시스템에 대한 작업 오프로드 일정 및 전송 에너지 할당을 최적화하는 기법을 제안한다. 시뮬레이션 결과로 MEC 시스템에서 사용 가능한 무선 및 계산 리소스가 상대적으로 균형 잡혀있는 경우 작업 오프로딩 일정이 더 중요하다는 것을 확인했다.

• 인터넷정보학회논문지
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• 제24권2호
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• pp.1-10
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• 2023

The Multi-access Edge Computing (MEC) paradigm equips network edge telecommunication infrastructure with cloud computing resources. It seeks to transform the edge into an IT services platform for hosting resource-intensive and delay-stringent services for mobile users, thereby significantly enhancing perceived service quality of experience. However, erratic user mobility impedes seamless service continuity as well as satisfying delay-stringent service requirements, especially as users roam farther away from the serving MEC resource, which deteriorates quality of experience. This work proposes a deep reinforcement learning based service mobility management approach for ensuring seamless migration of service instances along user mobility. The proposed approach focuses on the problem of selecting the optimal MEC resource to host services for high mobility users, thereby reducing service migration rejection rate and enhancing service availability. Efficacy of the proposed approach is confirmed through simulation experiments, where results show that on average, the proposed scheme reduces service delay by 8%, task computing time by 36%, and migration rejection rate by more than 90%, when comparing to a baseline scheme.

• 로봇학회논문지
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• 제17권2호
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• pp.118-123
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• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.211-232
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• 2024

In this paper, we consider the resource allocation and offloading decisions of device-to-device (D2D) cooperative UAV-assisted mobile edge computing (MEC) system, where the device with task request is served by unmanned aerial vehicle (UAV) equipped with MEC server and D2D device with idle resources. On the one hand, to ensure the fairness of time-delay sensitive devices, when UAV computing resources are relatively sufficient, an optimization model is established to minimize the maximum delay of device computing tasks. The original non-convex objective problem is decomposed into two subproblems, and the suboptimal solution of the optimization problem is obtained by alternate iteration of two subproblems. On the other hand, when the device only needs to complete the task within a tolerable delay, we consider the offloading priorities of task to minimize UAV computing resources. Then we build the model of joint offloading decision and power allocation optimization. Through theoretical analysis based on KKT conditions, we elicit the relationship between the amount of computing task data and the optimal resource allocation. The simulation results show that the D2D cooperation scheme proposed in this paper is effective in reducing the completion delay of computing tasks and saving UAV computing resources.

• Journal of Information Processing Systems
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• 제20권1호
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• pp.38-52
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• 2024

The 5G is the 5th generation mobile network that provides enhanced mobile broadband, ultra-reliable & low latency communications, and massive machine-type communications. New services can be provided through multi-access edge computing, network function virtualization, and network slicing, which are key technologies in 5G mobile communication. However, these new technologies provide new attack paths and threats. In this paper, we analyzed the overall threats of 5G mobile communication through a literature review. First, defines 5G mobile communication, analyzes its features and technology architecture, and summarizes possible security issues. Addition, it presents security threats from the perspective of user devices, radio access network, multi-access edge computing, and core networks that constitute 5G mobile communication. After that, security requirements for threat factors were derived through literature analysis. The purpose of this study is to conduct a fundamental analysis to examine and assess the overall threat factors associated with 5G mobile communication. Through this, it will be possible to protect the information and assets of individuals and organizations that use 5G mobile communication technology, respond to various threat situations, and increase the overall level of 5G security.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.