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Applied Sciences
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Fog Computing Applications in the Internet of Things: Exploiting Computational...
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Fog Computing Applications in the Internet of Things: Exploiting Computational Intelligence

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (15 March 2020) | Viewed by 16421

Special Issue Editors

Dr. Michele Scarpiniti

E-Mail Website
Guest Editor
Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome - Via Eudossiana 18, 00184 Rome, Italy
Interests: audio signal processing and machine learning for signal processing, nonlinear adaptive filtering, blind signal processing, and fog computing
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Chiti

E-Mail Website
Guest Editor
Department of Information Engineering, University of Florence, Via S. Marta 3, 50139 Firenze, Italy
Interests: fog computing; internet of vehicles; mobile ad hoc and sensor networks; self-similar traffic modeling; radio resource management within 3/4G networks; turbo coding
Special Issues, Collections and Topics in MDPI journals
Dr. Manuel Roveri

E-Mail Website
Guest Editor
Dipartimento di Elettronica e Informazione, Politecnico di Milano – Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Interests: fog computing; intelligent embedded and cyber-physical systems; adaptive computational-intelligent techniques; machine learning
Dr. Daniele Tarchi

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Information Engineering, Università di Bologna, 40136 Bologna, Italy
Interests: wireless communications and networking; satellite communications; mobile edge computing; fog computing; optimization techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fog computing (FC) is an emerging multitier paradigm that integrates cloud computing towards the edge of the network. In particular, FC refers to a distributed computing infrastructure confined to a limited geographical area in which some applications/services exploit proximate end-devices located at the edge of the network. The mission of FC is to improve energy-efficiency as well as to reduce the amount of data that need to be transported to the cloud for massive data processing, analysis and storage. However, being FC in its infancy, several issues and relevant challenges are still open. Specifically, critical aspects involve latency-sensitive and energy-efficient applications, where the end devices might not be appropriate since decisions/reactions must usually be taken in a very short time and energy and power constraints constitute a limiting factor. Computational intelligence (CI) constitutes an umbrella of techniques that are extremely flexible and suitable for solving dynamic and complex real-world problems that can be successfully applied to FC.

This Special Issue is devoted to collecting papers on novel and efficient computational intelligence solutions for these low-latency and green applications, and novel communication/networking paradigms, in order to meet specific configurability, adaptability, flexibility and energy/spectrum-efficiency constraints.

This Special Issue solicits papers that include, but are not limited to the following topics:

  • Networked computing architectures and infrastructures for fog computing
  • Energy efficient solutions for fog computing
  • Joint optimization of distributed communication and computing resource management in fog computing
  • Standardization of fog computing architectures
  • Machine and deep learning for fog and edge computing
  • Fog-enabled social networks of IoT devices
  • Isolation, vulnerability and risk analysis for fog over IoT applications
  • Cognitive fault detection and diagnosis
  • Cyber physical fog-supported IoT systems
  • Fog-aided big data streaming
  • Vehicular fog computing
  • Applications/architectures for fog-IoT-supported industry 4.0
  • Description of ongoing research projects on fog-IoT topics
  • Field trials and demo

Dr. Michele Scarpiniti
Dr. Francesco Chiti
Dr. Manuel Roveri
Dr. Daniele Tarchi
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. Applied Sciences 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 2400 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

  • Fog Computing
  • Internet of Thing
  • Computational Intelligence
  • Industry 4.0
  • Internet of Energy
  • Smart home
  • Smart Cities

Published Papers (4 papers)

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Research

18 pages, 1974 KiB  
Article
Towards Adaptive Multipath Managing: A Lightweight Path Management Mechanism to Aid Multihomed Mobile Computing Devices
by Yuanlong Cao, Mario Collotta, Siyi Xu, Longjun Huang, Xueqiang Tao and Zhichao Zhou
Appl. Sci. 2020, 10(1), 380; https://doi.org/10.3390/app10010380 - 04 Jan 2020
Cited by 9 | Viewed by 2002
Abstract
With the large scale deployment of multihomed mobile computing devices in today’s Internet, the Multipath TCP (MPTCP) is being considered as a preferred data transmission technology in the future Internet due to its promising features of bandwidth aggregation and multipath transmission. However, MPTCP [...] Read more.
With the large scale deployment of multihomed mobile computing devices in today’s Internet, the Multipath TCP (MPTCP) is being considered as a preferred data transmission technology in the future Internet due to its promising features of bandwidth aggregation and multipath transmission. However, MPTCP is more likely to be vulnerable to the transmission quality differences of multiple paths, which cause a “hot-potato” out-of-order arrival of packets at the receiver side, and in the absence of a related approach to fix this issue, serious application level performance degradations will occur. In this paper, we proposes MPTCP-LM 3 , a Lightweight path Management Mechanism to aid Multihomed MPTCP based mobile computing devices towards efficient multipath data transmission. The goals of MPTCP-LM 3 are: (i) to offer MPTCP a promising path management mechanism, (ii) to reduce out-of-order data reception and protect against receiver buffer blocking, and (iii) to increase the throughput of mobile computing devices in a multihomed wireless environment. Simulations show that MPTCP-LM 3 outperforms the current MPTCP schemes in terms of performance and quality of service. Full article
(This article belongs to the Special Issue Fog Computing Applications in the Internet of Things: Exploiting Computational Intelligence)
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18 pages, 3150 KiB  
Article
Blockchain-Based Resource Allocation Model in Fog Computing
by Haoyu Wang, Lina Wang, Zhichao Zhou, Xueqiang Tao, Giovanni Pau and Fabio Arena
Appl. Sci. 2019, 9(24), 5538; https://doi.org/10.3390/app9245538 - 16 Dec 2019
Cited by 38 | Viewed by 6906
Abstract
Fog computing makes up for the shortcomings of cloud computing. It brings many advantages, but various peculiarities must be perceived, such as security, resource management, storage, and other features at the same time. This paper investigates the resource contribution model between the fog [...] Read more.
Fog computing makes up for the shortcomings of cloud computing. It brings many advantages, but various peculiarities must be perceived, such as security, resource management, storage, and other features at the same time. This paper investigates the resource contribution model between the fog node and cloud or users when fog computing introduces blockchain. The proposed model practices the reward and punishment mechanism of the blockchain to boost the fog nodes to contribute resources actively. The behavior of the fog node in contributing resources and the completion degree of the task also for contributing resources are packaged into blocks and stored in the blockchain system to form a transparent, open, and tamper-free service evaluation index. The differential game method is employed to model and solve the above process and address the interaction between the optimal resource contribution strategy of the fog node and the optimal benefit under the optimal resource contribution strategy. Indirectly, this service evaluation index also brings long-term economic benefits to fog service providers. Besides, taking advantage of the performance characteristics of the collective maintenance of blockchain and the ability to establish a credible consensus mechanism in an untrusted environment, fog computing nodes, under the proposed architecture, can have specific security protection capabilities. Full article
(This article belongs to the Special Issue Fog Computing Applications in the Internet of Things: Exploiting Computational Intelligence)
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14 pages, 429 KiB  
Article
SmartFog: Training the Fog for the Energy-Saving Analytics of Smart-Meter Data
by Michele Scarpiniti, Enzo Baccarelli, Alireza Momenzadeh and Aurelio Uncini
Appl. Sci. 2019, 9(19), 4193; https://doi.org/10.3390/app9194193 - 08 Oct 2019
Cited by 3 | Viewed by 2568
Abstract
In this paper, we characterize the main building blocks and numerically verify the classification accuracy and energy performance of SmartFog, a distributed and virtualized networked Fog technological platform for the support for Stacked Denoising Auto-Encoder (SDAE)-based anomaly detection in data flows generated by [...] Read more.
In this paper, we characterize the main building blocks and numerically verify the classification accuracy and energy performance of SmartFog, a distributed and virtualized networked Fog technological platform for the support for Stacked Denoising Auto-Encoder (SDAE)-based anomaly detection in data flows generated by Smart-Meters (SMs). In SmartFog, the various layers of an SDAE are pretrained at different Fog nodes, in order to distribute the overall computational efforts and, then, save energy. For this purpose, a new Adaptive Elitist Genetic Algorithm (AEGA) is “ad hoc” designed to find the optimized allocation of the SDAE layers to the Fog nodes. Interestingly, the proposed AEGA implements a (novel) mechanism that adaptively tunes the exploration and exploitation capabilities of the AEGA, in order to quickly escape the attraction basins of local minima of the underlying energy objective function and, then, speed up the convergence towards global minima. As a matter of fact, the main distinguishing feature of the resulting SmartFog paradigm is that it accomplishes the joint integration on a distributed Fog computing platform of the anomaly detection functionality and the minimization of the resulting energy consumption. The reported numerical tests support the effectiveness of the designed technological platform and point out that the attained performance improvements over some state-of-the-art competing solutions are around 5%, 68% and 30% in terms of detection accuracy, execution time and energy consumption, respectively. Full article
(This article belongs to the Special Issue Fog Computing Applications in the Internet of Things: Exploiting Computational Intelligence)
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48 pages, 2739 KiB  
Article
VirtFogSim: A Parallel Toolbox for Dynamic Energy-Delay Performance Testing and Optimization of 5G Mobile-Fog-Cloud Virtualized Platforms
by Michele Scarpiniti, Enzo Baccarelli and Alireza Momenzadeh
Appl. Sci. 2019, 9(6), 1160; https://doi.org/10.3390/app9061160 - 19 Mar 2019
Cited by 15 | Viewed by 4117
Abstract
It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization [...] Read more.
It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emerging Mobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is a MATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering; and (v) its MATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared. Full article
(This article belongs to the Special Issue Fog Computing Applications in the Internet of Things: Exploiting Computational Intelligence)
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