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Future Internet
Special Issues
Fog and Mobile Edge Computing
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Fog and Mobile Edge Computing

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Network Virtualization and Edge/Fog Computing".

Deadline for manuscript submissions: closed (20 March 2021) | Viewed by 5574

Special Issue Editor

Dr. Nabil El Ioini

E-Mail Website
Guest Editor
Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani, 3 39100 Bolzano, Italy
Interests: software certification; software security; blockchain and software testing; cloud security; IoT; edge computing

Special Issue Information

Dear Colleagues,

Fog and Mobile Edge Computing (MEC) have been defined by many as an extension of the cloud, where resources are placed at the edge of the network and therefore, close to the end user. In the past few years, the terms Fog and MEC have attracted more interest both from academia and industry due to the huge role they could play in improving different scenarios where the traditional cloud fails, especially in terms of quality of service. Many industries see the benefits of these technologies as follows: i) they are key enablers for IoT infrastructures, by providing the needed processing and storage capacities often constrained in IoT devices; ii) they allow cloud resources to be closer to data sources in order to reduce latency and increase performance; and iii) they increase security and privacy by placing security checks closer to the data sources or even on companies’ premises. The distributed nature of Fog and MEC allow companies to deploy dedicated and context-aware services, while maintaining a high level of scalability, interoperability, and efficient (de-)allocation of resources.

The aim of this Special Issue is to collect the most recent studies focusing on Fog and MEC. Our goal is to bring together researchers and experts from different IT communities, including Fog computing, Mobile Edge Computing, Cloud Computing, IoT, Big Data, etc., to share ideas, present use cases, validate results, identify challenges, and define the future directions of Fog and MEC. The potential topics for the Special Issue include, but are not limited to:

  • Fog and MEC adoption strategies;
  • Fog and MEC architectures evolution, scaling, adaptation;
  • Fog and MEC delivery models;
  • Fog and MEC Security;
  • Application distribution and portability in Fog and MEC;
  • Availability and resiliency in Fog and MEC;
  • Fog and MEC virtualization and containers;
  • Infrastructure automation for Fog and MEC.

Dr. Nabil El Ioini
Guest Editor

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. Future Internet is an international peer-reviewed open access monthly 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 1600 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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

23 pages, 4398 KiB  
Article
Joint Offloading and Energy Harvesting Design in Multiple Time Blocks for FDMA Based Wireless Powered MEC
by Zhiyan Yu, Gaochao Xu, Yang Li, Peng Liu and Long Li
Future Internet 2021, 13(3), 70; https://doi.org/10.3390/fi13030070 - 12 Mar 2021
Cited by 5 | Viewed by 2387
Abstract
The combination of mobile edge computing (MEC) and wireless power transfer (WPT) is recognized as a promising technology to solve the problem of limited battery capacities and insufficient computation capabilities of mobile devices. This technology can transfer energy to users by radio frequency [...] Read more.
The combination of mobile edge computing (MEC) and wireless power transfer (WPT) is recognized as a promising technology to solve the problem of limited battery capacities and insufficient computation capabilities of mobile devices. This technology can transfer energy to users by radio frequency (RF) in wireless powered mobile edge computing. The user converts the harvested energy, stores it in the battery, and utilizes the harvested energy to execute corresponding local computing and offloading tasks. This paper adopts the Frequency Division Multiple Access (FDMA) technique to achieve task offloading from multiple mobile devices to the MEC server simultaneously. Our objective is to study multiuser dynamic joint optimization of computation and wireless resource allocation under multiple time blocks to solve the problem of maximizing residual energy. To this end, we formalize it as a nonconvex problem that jointly optimizes the number of offloaded bits, energy harvesting time, and transmission bandwidth. We adopt convex optimization technology, combine with Karush–Kuhn–Tucker (KKT) conditions, and finally transform the problem into a univariate constrained convex optimization problem. Furthermore, to solve the problem, we propose a combined method of Bisection method and sequential unconstrained minimization based on Reformulation-Linearization Technique (RLT). Numerical results demonstrate that the performance of our joint optimization method outperforms other benchmark schemes for the residual energy maximization problem. Besides, the algorithm can maximize the residual energy, reduce the computation complexity, and improve computation efficiency. Full article
(This article belongs to the Special Issue Fog and Mobile Edge Computing)
►▼ Show Figures

Figure 1

Figure 1
<p>System architecture.</p> Full article ">Figure 2
<p>The time allocation.</p> Full article ">Figure 3
<p>Residual Energy vs. Access Point (AP) Transmission Power.</p> Full article ">Figure 4
<p>Residual Energy vs. Task Size.</p> Full article ">Figure 5
<p>Residual Energy vs. Time Block.</p> Full article ">Figure 6
<p>Performance vs. AP Transmission Power. (<b>a</b>) Residual Energy; (<b>b</b>) Time Cost.</p> Full article ">Figure 7
<p>Performance vs. Task Size. (<b>a</b>) Residual Energy; (<b>b</b>) Time Cost.</p> Full article ">Figure 8
<p>Performance vs. AP Time Block. (<b>a</b>) Residual Energy; (<b>b</b>) Time Cost.</p> Full article ">
18 pages, 581 KiB  
Article
Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC–WPT System
by Long Li, Gaochao Xu, Peng Liu, Yang Li and Jiaqi Ge
Future Internet 2020, 12(12), 233; https://doi.org/10.3390/fi12120233 - 20 Dec 2020
Cited by 11 | Viewed by 2555
Abstract
With the rapid popularity of mobile devices (MDs), mobile edge computing (MEC) networks and wireless power transmission (WPT) will receive more attention. Naturally, by integrating these two technologies, the inherent energy consumption during task execution can be effectively reduced, and the collected energy [...] Read more.
With the rapid popularity of mobile devices (MDs), mobile edge computing (MEC) networks and wireless power transmission (WPT) will receive more attention. Naturally, by integrating these two technologies, the inherent energy consumption during task execution can be effectively reduced, and the collected energy can be provided to charge the MD. In this article, our research focuses on extending the battery time of MDs by maximizing the harvested energy and minimizing the consumed energy in the MEC–WPT system, which is formulated as a residual energy maximization problem and also a non-convex optimization problem. On the basis of study on maximizing the residual energy under multi-users and multi-time blocks, we propose an effective jointly optimization method (i.e., jointly optimize the energy harvesting time, task-offloading time, task-offloading size and the MDs’ CPU frequency), which combines the convex optimization method and the augmented Lagrangian to solve the residual energy maximum problem. We leverage Time Division Multiple Access (TMDA) mode to coordinate computation offloading. Simulation results show that our scheme has better performance than the benchmark schemes on maximizing residual energy. In particular, our proposed scheme is outstanding in the failure rate of multiple MDs and can adapt to the task size to minimize the failure rate. Full article
(This article belongs to the Special Issue Fog and Mobile Edge Computing)
►▼ Show Figures

Figure 1

Figure 1
<p>System model.</p> Full article ">Figure 2
<p>Time-division multiplexing model for energy harvesting and task offloading.</p> Full article ">Figure 3
<p>Access point (AP) transmission power vs. residual energy.</p> Full article ">Figure 4
<p>Task size vs. residual energy.</p> Full article ">Figure 5
<p>Time block vs. residual energy.</p> Full article ">Figure 6
<p>Task size vs. failure ratio.</p> Full article ">
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