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Advanced Computing Paradigms for Internet of Things: Fog, Edge, and Cloud Technologies

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

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 6602

Special Issue Editors


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Guest Editor
Learning, Data and Robotics Laboratory, ESIEA Graduate Engineering School, 75005 Paris, France
Interests: Internet of Things; cloud computing; AI and data science
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Nokia, Paris-Saclay, 91620 Nozay, France
Interests: big data; internet of things; middleware and virtualization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of intelligent computational technologies, the Internet of Things (IoT), as a pervasive paradigm connecting the real world with the cyber world through heterogeneous devices, has shown great advances in many fields, such as wireless telecommunications, digital health, and industrial automation. However, challenges have also emerged in data management, processing, and storage, presenting a need to develop advanced computing paradigms to meet the ever-increasing number of data resources in IoT development.

Traditional cloud computing has shown its limits of latency and link congestion when transferring large amounts of data. Therefore, fog and edge computing concepts have been proposed for the collection, analysis, and processing of data closer to sensor devices. However, there are still series of challenging problems in the integration, efficiency, and security of cloud/fog/edge computing in the Internet of Things.

This Special Issue focuses on advanced research regarding advanced cloud/fog/edge computing technologies in the Internet of Things. Researchers are welcome to present original research on the latest findings related to current trends and challenges in the cloud/fog/edge computing architectures, protocols, designs, and frameworks for future IoT. Topics of interest include, but are not limited to, the following:

  • Cloud/fog/edge computing architectures and protocols for IoT;
  • Resource allocation in cloud/fog/edge computing for IoT;
  • Green cloud/fog/edge computing;
  • Cloud/fog/edge computing in connected vehicles, intelligent manufacturing, and smart cities;
  • Communication technologies in cloud/fog/edge computing for IoT;
  • The integration of fog/edge computing with cloud computing for IoT;
  • The modeling and performance analysis of cloud/fog/edge computing for IoT;
  • Computing and network convergence (CNC) paradigms/issues.

Dr. Ehsan Ahvar
Prof. Dr. Gyumyoung Lee
Dr. Shohreh Ahvar
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.

Published Papers (2 papers)

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Research

20 pages, 1442 KiB  
Article
Simulation Tools for Fog Computing: A Comparative Analysis
by Muhammad Fahimullah, Guillaume Philippe, Shohreh Ahvar and Maria Trocan
Sensors 2023, 23(7), 3492; https://doi.org/10.3390/s23073492 - 27 Mar 2023
Cited by 3 | Viewed by 3834
Abstract
Fog Computing (FC) was introduced to offer resources closer to the users. Researchers propose different solutions to make FC mature and use simulators for evaluating their solutions at early stages. In this paper, we compare different FC simulators based on their technical and [...] Read more.
Fog Computing (FC) was introduced to offer resources closer to the users. Researchers propose different solutions to make FC mature and use simulators for evaluating their solutions at early stages. In this paper, we compare different FC simulators based on their technical and non-technical characteristics. In addition, a practical comparison is conducted to compare the three main FC simulators based on their performance such as execution time, CPU, and memory usage for running different applications. The analysis can be helpful for researchers to select the appropriate simulator and platform to evaluate their solutions on different use cases. Furthermore, open issues and challenges for FC simulators are discussed that require attention and need to be addressed in the future. Full article
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29 pages, 6099 KiB  
Article
Machine Learning Analytic-Based Two-Staged Data Management Framework for Internet of Things
by Omar Farooq, Parminder Singh, Mustapha Hedabou, Wadii Boulila and Bilel Benjdira
Sensors 2023, 23(5), 2427; https://doi.org/10.3390/s23052427 - 22 Feb 2023
Cited by 5 | Viewed by 2102
Abstract
In applications of the Internet of Things (IoT), where many devices are connected for a specific purpose, data is continuously collected, communicated, processed, and stored between the nodes. However, all connected nodes have strict constraints, such as battery usage, communication throughput, processing power, [...] Read more.
In applications of the Internet of Things (IoT), where many devices are connected for a specific purpose, data is continuously collected, communicated, processed, and stored between the nodes. However, all connected nodes have strict constraints, such as battery usage, communication throughput, processing power, processing business, and storage limitations. The high number of constraints and nodes makes the standard methods to regulate them useless. Hence, using machine learning approaches to manage them better is attractive. In this study, a new framework for data management of IoT applications is designed and implemented. The framework is called MLADCF (Machine Learning Analytics-based Data Classification Framework). It is a two-stage framework that combines a regression model and a Hybrid Resource Constrained KNN (HRCKNN). It learns from the analytics of real scenarios of the IoT application. The description of the Framework parameters, the training procedure, and the application in real scenarios are detailed. MLADCF has shown proven efficiency by testing on four different datasets compared to existing approaches. Moreover, it reduced the global energy consumption of the network, leading to an extended battery life of the connected nodes. Full article
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