A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art.
Quoc-Viet Pham,Fang Fang,Vu Nguyen Ha,Md. Jalil Piran,Mai Le,Long Bao Le,Won-Joo Hwang,Zhiguo Ding +7 more
TL;DR: In this article, the authors provide a comprehensive overview of mobile edge computing (MEC) and its potential use cases and applications, as well as discuss challenges and potential future directions for MEC research.
read more
Abstract: Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Industry 5.0: A survey on enabling technologies and potential applications
Praveen Kumar Reddy Maddikunta,Quoc-Viet Pham,B. Prabadevi,N. Deepa,Kapal Dev,Thippa Reddy Gadekallu,Rukhsana Ruby,Madhusanka Liyanage +7 more
TL;DR: This paper aims to provide a survey-based tutorial on potential applications and supporting technologies of Industry 5.0 from the perspective of different industry practitioners and researchers.
Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research
Chamitha de Alwis,Anshuman Kalla,Quoc-Viet Pham,Pardeep Kumar,Kapal Dev,Won-Joo Hwang,Madhusanka Liyanage +6 more
- 07 Apr 2021
TL;DR: In this paper, the authors provide a comprehensive survey of the current developments towards 6G and elaborate the requirements that are necessary to realize the 6G applications, and summarize lessons learned from state-of-the-art research and discuss technical challenges that would shed a new light on future research directions toward 6G.
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges
Dinh C. Nguyen,Ming Ding,Quoc-Viet Pham,Pubudu N. Pathirana,Long Bao Le,Aruna Seneviratne,Jun Li,Dusit Niyato,H. Vincent Poor +8 more
TL;DR: Several main issues in FLchain design are identified, including communication cost, resource allocation, incentive mechanism, security and privacy protection, and the applications of FLchain in popular MEC domains, such as edge data sharing, edge content caching and edge crowdsensing are investigated.
526
Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things
TL;DR: This article analyzes the main features of MEC in the context of 5G and IoT and presents several fundamental key technologies which enable MEC to be applied in 5Gs and IoT, such as cloud computing, software-defined networking/network function virtualization, information-centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading.
501
Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements, and Challenges
Praveen Kumar Reddy Maddikunta,Saqib Hakak,Mamoun Alazab,Sweta Bhattacharya,Thippa Reddy Gadekallu,Wazir Zada Khan,Quoc-Viet Pham +6 more
TL;DR: An attempt has been made to explore the types of sensors suitable for smart farming, potential requirements and challenges for operating UAVs in smart agriculture, and the future applications of using UAV's in smart farming.
477
References
A Smart Classroom Based on Deep Learning and Osmotic IoT Computing
Alberto Pacheco,Pablo Cano,Ever Flores,Edgar Trujillo,Pedro Marquez +4 more
- 01 Oct 2018
TL;DR: A comparative performance study and analysis was made by means of selecting a single deep learning model that was tried to be adapted to run over the cloud, a fog microserver and a mobile edge computing device for person recognition.
34
Joint Computation Offloading and Resource Allocation in C-RAN With MEC Based on Spectrum Efficiency
Zhang Jian,Wu Muqing,Zhao Min +2 more
TL;DR: A novel task-aware C-RAN with MEC structure is presented and the proposed SJOORA scheme can effectively increase the profit of network operator with relative lower complexity.
A Middleware for Mobile Edge Computing
TL;DR: This paper proposes middleware for running applications over heterogeneous environments, made of telecom networks and legacy data centers, and describes an integrated solution for developing and deploying modular applications in an automatic way.
31
Collaborative Computing for Advanced Tactile Internet Human-to-Robot (H2R) Communications in Integrated FiWi Multirobot Infrastructures
TL;DR: An adaptive resource allocation model is introduced and an analytical framework is developed to evaluate the task allocation delay, energy consumption, and task response time for noncollaborative and collaborative task execution scheme across integrated fiber-wireless multirobot networks.
31
•Journal Article
An energy efficient design for UAV communication with mobile edge computing
TL;DR: This paper considers a UAV communication system with mobile edge computing and jointly optimizing the UAV's trajectory and task assignment as well as CPU's computational speed under the set of resource constrains to solve the optimization problem approximately.
28