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Edge Intelligence for Next Generation Cloud-Edge-IoT Systems
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Edge Intelligence for Next Generation Cloud-Edge-IoT Systems

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

Deadline for manuscript submissions: closed (26 August 2022) | Viewed by 4754

Special Issue Editors

Dr. Stefan Nastic

E-Mail Website
Guest Editor
Distributed Systems Group, Tu Wien, 1040 Vienna, Austria
Interests: serverless computing; edge-cloud continuum; reliability engineering; AI/ML
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoning Ding

E-Mail Website
Guest Editor
Field of Study Computer Science, University of Chinese Academy of Sciences, Beijing 100049, China
Interests: cloud infrastructure; large-scale cluster management
Prof. Dr. Marjan Gusev

E-Mail Website
Guest Editor
Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia
Interests: edge computing; IoT; e-Health; dew computing

Special Issue Information

Dear Colleagues,

Cloud computing has seen an unprecedented boom during the last decade. The next generation of clouds are already expanding beyond traditional data centers into the far edges of the network. This is completely transforming how we perceive the notion of computing infrastructures and other digital resources such as storage and network. Additionally, novel types of resources e.g., Edge-based, sensors are becoming an integral part of this novel computational fabric. Therefore, we are witnessing a paradigm shift, in which digital resources are becoming truly ubiquitous and first-class citizens available across the entire Cloud-Edge-IoT (CEI) continuum. This is quickly disrupting our understanding of many systems properties, which are traditionally known as cloud-native flagship properties. Examples include: elasticity, resource scheduling, Service Level Agreements (SLAs); application execution models (e.g., serverless computing) and design architectures (e.g., microservice architectures). Enabling edge intelgence will play a decisive role to  being able to efficiently and cost effectively operate such a CEI computational fabric. Whatismore, edge intelligence is a crucial precondition for building novel CEI applications, which need to process vast amounts of data intelligently, near data sources.

Dr. Stefan Nastic
Dr. Xiaoning Ding
Prof. Dr. Marjan Gusev
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

  • edge intelligence
  • edge computing
  • machine learning
  • Internet of Things
  • hybrid cloud computing

Published Papers (2 papers)

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Research

33 pages, 11879 KiB  
Article
Practical Latency Analysis of a Bluetooth 5 Decentralized IoT Opportunistic Edge Computing System for Low-Cost SBCs
by Ángel Niebla-Montero, Iván Froiz-Míguez, Paula Fraga-Lamas and Tiago M. Fernández-Caramés
Sensors 2022, 22(21), 8360; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218360 - 31 Oct 2022
Cited by 3 | Viewed by 1552
Abstract
IoT devices can be deployed almost anywhere, but they usually need to be connected to other IoT devices, either through the Internet or local area networks. For such communications, many IoT devices make use of wireless communications, whose coverage is key: if no [...] Read more.
IoT devices can be deployed almost anywhere, but they usually need to be connected to other IoT devices, either through the Internet or local area networks. For such communications, many IoT devices make use of wireless communications, whose coverage is key: if no coverage is available, an IoT device becomes isolated. This can happen both indoors (e.g., large buildings, industrial warehouses) or outdoors (e.g., rural areas, cities). To tackle such an issue, opportunistic networks can be useful, since they use gateways to provide services to IoT devices when they are in range (i.e., IoT devices take the opportunity of having a nearby gateway to exchange data or to use a computing service). Moreover, opportunistic networks can provide Edge Computing capabilities, thus creating Opportunistic Edge Computing (OEC) systems, which deploy smart gateways able to perform certain tasks faster than a remote Cloud. This article presents a novel decentralized OEC system based on Bluetooth 5 IoT nodes whose latency is evaluated to determine the feasibility of using it in practical applications. The obtained results indicate that, for the selected scenario, the average end-to-end latency is relatively low (736 ms), but it is impacted by factors such as the location of the bootstrap node, the smart gateway hardware or the use of high-security mechanisms. Full article
(This article belongs to the Special Issue Edge Intelligence for Next Generation Cloud-Edge-IoT Systems)
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19 pages, 3547 KiB  
Article
Osmotic Cloud-Edge Intelligence for IoT-Based Cyber-Physical Systems
by Giuseppe Loseto, Floriano Scioscia, Michele Ruta, Filippo Gramegna, Saverio Ieva, Corrado Fasciano, Ivano Bilenchi and Davide Loconte
Sensors 2022, 22(6), 2166; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062166 - 10 Mar 2022
Cited by 4 | Viewed by 2444
Abstract
Artificial Intelligence (AI) in Cyber-Physical Systems allows machine learning inference on acquired data with ever greater accuracy, thanks to models trained with massive amounts of information generated by Internet of Things devices. Edge Intelligence is increasingly adopted to execute inference on data at [...] Read more.
Artificial Intelligence (AI) in Cyber-Physical Systems allows machine learning inference on acquired data with ever greater accuracy, thanks to models trained with massive amounts of information generated by Internet of Things devices. Edge Intelligence is increasingly adopted to execute inference on data at the border of local networks, exploiting models trained in the Cloud. However, the training tasks on Edge nodes are not supported yet with flexible dynamic migration between Edge and Cloud. This paper proposes a Cloud-Edge AI microservice architecture, based on Osmotic Computing principles. Notable features include: (i) containerized architecture enabling training and inference on the Edge, Cloud, or both, exploiting computational resources opportunistically to reach the best prediction accuracy; and (ii) microservice encapsulation of each architectural module, allowing a direct mapping with Commercial-Off-The-Shelf (COTS) components. Grounding on the proposed architecture: (i) a prototype has been realized with commodity hardware leveraging open-source software technologies; and (ii) it has been then used in a small-scale intelligent manufacturing case study, carrying out experiments. The obtained results validate the feasibility and key benefits of the approach. Full article
(This article belongs to the Special Issue Edge Intelligence for Next Generation Cloud-Edge-IoT Systems)
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