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Mobile Edge Computing for 5G and Future Internet
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Mobile Edge Computing for 5G and Future Internet

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 5675

Special Issue Editors

Prof. Dr. Jihoon Lee

E-Mail Website
Guest Editor
Department of Smart Information and Telecommunication Engineering, Sangmyung University, Cheonan-si, Republic of Korea
Interests: wireless networks; future internet; mobile-oriented information-centric networking; virtual reality (VR) streaming; mobile edge computing (MEC); network security; secure M2M; software-defined networking
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Daeyoub Kim

E-Mail Website
Guest Editor
Dept. of Information Security, Suwon University, Hwaseong-si, Korea
Interests: secure edge computing; future internet security; ML-based security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wonjong Noh

E-Mail Website
Guest Editor
School of Software, Hallym University, Chuncheon-si, Gangwon-do, Korea
Interests: network capacity; network optimization; stochastic QoS guarantee; machine learning; information theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, the service and communication paradigms are rapidly changing from simple applications to various intelligent multimedia applications such as immersive VR/AR contents as well as from cellular and WiFi based networks to heterogeneous networks including all-IP, device-to-device, and Internet of Things (IoTs). With such big changes, interest in 5G and edge computing is increasing. Edge computing is a technology that reduces transmission delays and bandwidth constraints by placing resources such as computing, memory, bandwidth, and applications on a network close to the user. That is, edge computing, can process data on user devices such as smartphones, and can support mobile computing and IoT technologies by providing distributed processing performance as a distributed open architecture. This Special issue “Mobile Edge Computing for 5G and Future Internet” aims (i) the recent developments in distributed control and schems for edge computing in 5G environment, and (ii) the analysis of critical issues and proposals for the interworking with future internet technologies.

Prof. Dr. Jihoon Lee
Prof. Dr. Daeyoub Kim
Prof. Dr. Wonjong Noh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent edge computing
  • cooperated mobility management
  • distributed content migration
  • secure edge computing
  • vehicular edge computing
  • network slicing
  • mobility-aware edge computing

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Published Papers (2 papers)

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19 pages, 1975 KiB  
Article
D2D-Assisted Multi-User Cooperative Partial Offloading in MEC Based on Deep Reinforcement Learning
by Xin Guan, Tiejun Lv, Zhipeng Lin, Pingmu Huang and Jie Zeng
Sensors 2022, 22(18), 7004; https://doi.org/10.3390/s22187004 - 15 Sep 2022
Cited by 10 | Viewed by 2259
Abstract
Mobile edge computing (MEC) and device-to-device (D2D) communication can alleviate the resource constraints of mobile devices and reduce communication latency. In this paper, we construct a D2D-MEC framework and study the multi-user cooperative partial offloading and computing resource allocation. We maximize the number [...] Read more.
Mobile edge computing (MEC) and device-to-device (D2D) communication can alleviate the resource constraints of mobile devices and reduce communication latency. In this paper, we construct a D2D-MEC framework and study the multi-user cooperative partial offloading and computing resource allocation. We maximize the number of devices under the maximum delay constraints of the application and the limited computing resources. In the considered system, each user can offload its tasks to an edge server and a nearby D2D device. We first formulate the optimization problem as an NP-hard problem and then decouple it into two subproblems. The convex optimization method is used to solve the first subproblem, and the second subproblem is defined as a Markov decision process (MDP). A deep reinforcement learning algorithm based on a deep Q network (DQN) is developed to maximize the amount of tasks that the system can compute. Extensive simulation results demonstrate the effectiveness and superiority of the proposed scheme. Full article
(This article belongs to the Special Issue Mobile Edge Computing for 5G and Future Internet)
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20 pages, 795 KiB  
Article
Optimal Task Allocation Algorithm Based on Queueing Theory for Future Internet Application in Mobile Edge Computing Platform
by Yukiko Katayama and Takuji Tachibana
Sensors 2022, 22(13), 4825; https://doi.org/10.3390/s22134825 - 25 Jun 2022
Cited by 7 | Viewed by 2514
Abstract
For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC server, [...] Read more.
For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC server, and a cloud server. For this platform, we first calculate the delay between sending a task and receiving a response for the dedicated MEC server, shared MEC server, and cloud server by considering the processing time and transmission delay. Here, the transmission delay for the shared MEC server is derived using queueing theory. Then, we formulate an optimization problem for task allocation to minimize the total latency for all tasks. By solving this optimization problem, tasks can be allocated to the MEC servers and cloud server appropriately. In addition, we propose a heuristic algorithm to obtain the approximate optimal solution in a shorter time. This heuristic algorithm consists of four algorithms: a main algorithm and three additional algorithms. In this algorithm, tasks are divided into two groups, and task allocation is executed for each group. We compare the performance of our proposed heuristic algorithm with the solution obtained by three other methods and investigate the effectiveness of our algorithm. Numerical examples are used to demonstrate the effectiveness of our proposed heuristic algorithm. From some results, we observe that our proposed heuristic algorithm can perform task allocation in a short time and can effectively reduce the total latency in a short time. We conclude that our proposed heuristic algorithm is effective for task allocation in a MEC platform with multiple types of MEC servers. Full article
(This article belongs to the Special Issue Mobile Edge Computing for 5G and Future Internet)
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