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Future Internet
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Edge and Fog Computing for the Internet of Things
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Edge and Fog Computing for the Internet of Things

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 7701

Special Issue Editor

Dr. Alessandro Pozzebon

E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, 35131 Padova, Italy
Interests: Internet of Things; low power wide area networks; augmented and virtual sensing techniques; embedded ML; distributed measurement systems; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few years, embedded systems have experienced a rapid growth in the computation capabilities of microcontrollers and microprocessors. This aspect, combined with the availability of Internet of Things (IoT)-devoted data transmission technologies, is being extensively exploited to move several data processing tasks from the cloud towards the edge of the networks. Many low-cost hardware platforms enable the implementation of artificial intelligence (AI) algorithms, both at the edge and spread throughout the network, while data transmission technologies, such as LoRaWAN or IoT cellular networks (e.g., NB-IoT or LTE-M), facilitate data exchange among IoT nodes, as well as the distribution of the computation load, according to the fog computing paradigm.

The aim of the Special Issue is to invite either original research contributions or detailed surveys related to edge or fog computing solutions and techniques within the IoT framework, including, but not limited to, the following topics:

  • edge computing techniques for the IoT;
  • fog computing techniques for the IoT;
  • IoT network technologies for distributed computing;
  • data transmission protocols for edge and fog computing;
  • edge and fog computing solutions for smart cities;
  • edge and fog computing solutions for smart industries;
  • edge and fog computing solutions for smart healthcare;
  • distributed monitoring solutions relying of edge and fog computing;
  • embedded machine learning techniques;
  • artificial intelligence algorithms on the edge and on the fog.

Dr. Alessandro Pozzebon
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.

Keywords

  • embedded systems
  •  Internet of Things (IoT)
  •  Artificial Intelligence (AI)
  •  LoRaWAN
  •  IoT cellular networks
  •  fog computing
  •  edge computing
  •  distributed computing
  •  data transmission protocols
  •  smart cities
  •  smart industries
  •  smart healthcare
  •  embedded machine learning

Published Papers (5 papers)

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Editorial

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2 pages, 129 KiB  
Editorial
Edge and Fog Computing for the Internet of Things
by Alessandro Pozzebon
Future Internet 2024, 16(3), 101; https://doi.org/10.3390/fi16030101 - 16 Mar 2024
Viewed by 857
Abstract
Over the last years few years, the number of interconnected devices within the context of Internet of Things (IoT) has rapidly grown; some statistics state that the total number of IoT-connected devices in 2023 has reached the groundbreaking number of 17 billion [...] [...] Read more.
Over the last years few years, the number of interconnected devices within the context of Internet of Things (IoT) has rapidly grown; some statistics state that the total number of IoT-connected devices in 2023 has reached the groundbreaking number of 17 billion [...] Full article
(This article belongs to the Special Issue Edge and Fog Computing for the Internet of Things)

Research

Jump to: Editorial

30 pages, 894 KiB  
Article
Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum
by Filippo Poltronieri, Cesare Stefanelli, Mauro Tortonesi and Mattia Zaccarini
Future Internet 2023, 15(11), 359; https://doi.org/10.3390/fi15110359 - 31 Oct 2023
Cited by 2 | Viewed by 1463
Abstract
Modern computing environments, thanks to the advent of enabling technologies such as Multi-access Edge Computing (MEC), effectively represent a Cloud Continuum, a capillary network of computing resources that extend from the Edge of the network to the Cloud, which enables a dynamic and [...] Read more.
Modern computing environments, thanks to the advent of enabling technologies such as Multi-access Edge Computing (MEC), effectively represent a Cloud Continuum, a capillary network of computing resources that extend from the Edge of the network to the Cloud, which enables a dynamic and adaptive service fabric. Efficiently coordinating resource allocation, exploitation, and management in the Cloud Continuum represents quite a challenge, which has stimulated researchers to investigate innovative solutions based on smart techniques such as Reinforcement Learning and Computational Intelligence. In this paper, we make a comparison of different optimization algorithms and a first investigation of how they can perform in this kind of scenario. Specifically, this comparison included the Deep Q-Network, Proximal Policy Optimization, Genetic Algorithms, Particle Swarm Optimization, Quantum-inspired Particle Swarm Optimization, Multi-Swarm Particle Optimization, and the Grey-Wolf Optimizer. We demonstrate how all approaches can solve the service management problem with similar performance—with a different sample efficiency—if a high number of samples can be evaluated for training and optimization. Finally, we show that, if the scenario conditions change, Deep-Reinforcement-Learning-based approaches can exploit the experience built during training to adapt service allocation according to the modified conditions. Full article
(This article belongs to the Special Issue Edge and Fog Computing for the Internet of Things)
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12 pages, 284 KiB  
Article
Challenges of Network Forensic Investigation in Fog and Edge Computing
by Daniel Spiekermann and Jörg Keller
Future Internet 2023, 15(10), 342; https://doi.org/10.3390/fi15100342 - 18 Oct 2023
Cited by 1 | Viewed by 1815
Abstract
While network forensics has matured over the decades and even made progress in the last 10 years when deployed in virtual networks, network forensics in fog and edge computing is still not progressed to that level despite the now widespread use of these [...] Read more.
While network forensics has matured over the decades and even made progress in the last 10 years when deployed in virtual networks, network forensics in fog and edge computing is still not progressed to that level despite the now widespread use of these paradigms. By using an approach similar to software testing, i.e., a mixture of systematic and experience, we analyze obstacles specific to forensics in fog and edge computing such as spatial dispersion and possibly incomplete recordings, and derive how far these obstacles can be overcome by adapting processes and techniques from other branches of network forensics, and how new solutions could look otherwise. In addition, we present a discussion of open problems of network forensics in fog and edge environments and discusses the challenges for an investigator. Full article
(This article belongs to the Special Issue Edge and Fog Computing for the Internet of Things)
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18 pages, 683 KiB  
Article
Intelligent Video Streaming at Network Edge: An Attention-Based Multiagent Reinforcement Learning Solution
by Xiangdong Tang, Fei Chen and Yunlong He
Future Internet 2023, 15(7), 234; https://doi.org/10.3390/fi15070234 - 03 Jul 2023
Cited by 1 | Viewed by 1285
Abstract
Video viewing is currently the primary form of entertainment for modern people due to the rapid development of mobile devices and 5G networks. The combination of pervasive edge devices and adaptive bitrate streaming technologies can lessen the effects of network changes, boosting user [...] Read more.
Video viewing is currently the primary form of entertainment for modern people due to the rapid development of mobile devices and 5G networks. The combination of pervasive edge devices and adaptive bitrate streaming technologies can lessen the effects of network changes, boosting user quality of experience (QoE). Even while edge servers can offer near-end services to local users, it is challenging to accommodate a high number of mobile users in a dynamic environment due to their restricted capacity to maximize user long-term QoE. We are motivated to integrate user allocation and bitrate adaptation into one optimization objective and propose a multiagent reinforcement learning method combined with an attention mechanism to solve the problem of multiedge servers cooperatively serving users. Through comparative experiments, we demonstrate the superiority of our proposed solution in various network configurations. To tackle the edge user allocation problem, we proposed a method called attention-based multiagent reinforcement learning (AMARL), which optimized the problem in two directions, i.e., maximizing the QoE of users and minimizing the number of leased edge servers. The performance of AMARL is proved by experiments. Full article
(This article belongs to the Special Issue Edge and Fog Computing for the Internet of Things)
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29 pages, 2033 KiB  
Article
Anomaly Detection for Hydraulic Power Units—A Case Study
by Paweł Fic, Adam Czornik and Piotr Rosikowski
Future Internet 2023, 15(6), 206; https://doi.org/10.3390/fi15060206 - 02 Jun 2023
Cited by 1 | Viewed by 1548
Abstract
This article aims to present the real-world implementation of an anomaly detection system of a hydraulic power unit. Implementation involved the Internet of Things approach. A detailed description of the system architecture is provided. The complete path from sensors through PLC and the [...] Read more.
This article aims to present the real-world implementation of an anomaly detection system of a hydraulic power unit. Implementation involved the Internet of Things approach. A detailed description of the system architecture is provided. The complete path from sensors through PLC and the edge computer to the cloud is presented. Some technical information about hydraulic power units is also given. This article involves the description of several model-at-scale deployment techniques. In addition, the approach to the synthesis of anomaly and novelty detection models was described. Anomaly detection of data acquired from the hydraulic power unit was carried out using two approaches, statistical and black-box, involving the One Class SVM model. The costs of cloud resources and services that were generated in the project are presented. Since the article describes a commercial implementation, the results have been presented as far as the formal and business conditions allow. Full article
(This article belongs to the Special Issue Edge and Fog Computing for the Internet of Things)
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