Mobile Edge Communication An overview of MEC in 5G

Priya, Bhanu; Sri, R. Leena; Nimmagadda, Amulya; Garudkar, Krishnai

doi:10.1109/icaccs.2019.8728355

Cited by 7 publications

(1 citation statement)

References 10 publications

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“…ese three kinds of networks transfer data alternately between the user terminal and the application server to meet the various Internet needs of users. However, with the emergence of various new types of services, such as AR/VR, connected cars, and so on, this traditional network structure is gradually overburdened [18][19][20][21]. erefore, the emergence of MEC, that is, network services "sink" to the wireless access network side closer to users, brings about three benefits: (1) e transmission delay perceived by users is significantly reduced; (2) network congestion is controlled remarkably; and (3) more network information and network congestion control functions can be opened to developers.…”

Section: Introductionmentioning

confidence: 99%

On Improving the Robustness of MEC with Big Data Analysis for Mobile Video Communication

Zhao

Zeng

Liu

et al. 2021

Security and Communication Networks

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Mobile video communication and Internet of Things are playing a more and more important role in our daily life. Mobile Edge Computing (MEC), as the essential network architecture for the Internet, can significantly improve the quality of video streaming applications. The mobile devices transferring video flow are often exposed to hostile environment, where they would be damaged by different attackers. Accordingly, Mobile Edge Computing Network is often vulnerable under disruptions, against either natural disasters or human intentional attacks. Therefore, research on secure hub location in MEC, which could obviously enhance the robustness of the network, is highly invaluable. At present, most of the attacks encountered by edge nodes in MEC in the IoT are random attacks or random failures. According to network science, scale-free networks are more robust than the other types of network under the random failures. In this paper, an optimization algorithm is proposed to reorganize the structure of the network according to the amount of information transmitted between edge nodes. BA networks are more robust under random attacks, while WS networks behave better under human intentional attacks. Therefore, we change the structure of the network accordingly, when the attack type is different. Besides, in the MEC networks for mobile video communication, the capacity of each device and the size of the video data influence the structure significantly. The algorithm sufficiently takes the capability of edge nodes and the amount of the information between them into consideration. In robustness test, we set the number of network nodes to be 200 and 500 and increase the attack scale from 0% to 100% to observe the behaviours of the size of the giant component and the robustness calculated for each attack method. Evaluation results show that the proposed algorithm can significantly improve the robustness of the MEC networks and has good potential to be applied in real-world MEC systems.

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Section: Introductionmentioning

confidence: 99%

On Improving the Robustness of MEC with Big Data Analysis for Mobile Video Communication

Zhao

Zeng

Liu

et al. 2021

Security and Communication Networks

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Fuzzy-based Misbehavior Detection for Internet of Things in Multi-access Edge Computing Environment

Mansour

2023

Journal of King Saud University - Computer and Information Scie

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Orchestration of MEC Computation Jobs and Energy Consumption Challenges in 5G and Beyond

2022

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Mobile Edge Computing (MEC) technology philosophy inspires the next generation mobile networks to provide cloud computing capabilities in addition to a diverse range of Information Technology (IT) services with ultra-low latency and higher bandwidth at the edge. One of the most common challenges of 5G-MEC is the management and orchestration across all networks and infrastructure resources as well as end-to-end quality of experience. The decentralized architecture of MEC with independent and noncollaborative servers results in the situation of having underutilized servers with wasted energy. Moreover, the consequences of having highly utilized servers with highly consumed energy are not only the incapability to accommodate all the load of the computing jobs and the dramatic increase in the total OPEX cost, but it also creates some environmental problems. Orchestrating servers' workload and control offloading the computation jobs is one of the technical advantages of MEC since it satisfies the increasing requirements of modern mobile applications while optimizing the energy consumption and cost. In this work, we consider cluster-based energy-aware offloading framework. The proposed work consists of dual-tier domain divided into clusters of Edge Servers ES s . We have presented the results of our simulation as a proof of our concept that the formulated adaptive strategy to minimize the optimization problem calculation per cluster reduces the energy consumption and enhances the quality of experience while achieving the conservation of the related computing and storage resources cost.

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Mobile Edge Communication An overview of MEC in 5G

Cited by 7 publications

References 10 publications

On Improving the Robustness of MEC with Big Data Analysis for Mobile Video Communication

On Improving the Robustness of MEC with Big Data Analysis for Mobile Video Communication

Fuzzy-based Misbehavior Detection for Internet of Things in Multi-access Edge Computing Environment

Orchestration of MEC Computation Jobs and Energy Consumption Challenges in 5G and Beyond

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