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Special Issue "Knowledge Transfer in IoT and Edge Computing"

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

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Dr. Ricardo S. Alonso
E-Mail Website
Guest Editor
BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
Interests: Internet of Things; Industry 4.0; edge computing; smart farming; software-defined networks; intelligent systems; wireless sensor networks; real-time locating systems
Special Issues and Collections in MDPI journals
Dr. Óscar García
E-Mail Website
Guest Editor
BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
Interests: big data; Artificial Intelligence; IoT; Industry 4.0; energy efficiency
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things is no longer a novel technology but is now widely used in many applications: smart cities, Industry 4.0, smart farming, healthcare or smart energy, among many others. In this sense, Edge Computing architectures allow freeing IoT devices from managing communication with the Cloud, reducing its energy consumption, and increasing its autonomy. Moreover, Edge nodes allow pre-processing data transmitted to the Cloud, reducing costs of transfer, processing, and storage in the Cloud. Furthermore, the possibility of running Machine Learning models at the Edge is very useful in those scenarios in which it is necessary to offer a low-latency response when detecting patterns or anomalies, even when communication with the cloud is interrupted. In this regard, the creation of models that run at the Edge brings with it different challenges. On one hand, training the models in the cloud involves sending all the data from the IoT layer to the Cloud, which implies a risk in terms of security and privacy. On the other hand, training the models on the Edge without considering the rest of the data obtained in other locations may waste some of the knowledge acquired. In this sense, it is necessary to develop new solutions that allow the creation and transfer of knowledge in Edge–IoT applications in an efficient and secure way.

For this purpose, this Special Issue will be conducted under, but not limited to, the following topics:

  • Innovative methods for transferring knowledge between Cloud and Edge;
  • Distributed and collaborative knowledge management;
  • Novel architectures, protocols, and algorithms in Edge–IoT scenarios;
  • Deep Learning and deep reinforcement learning at the Edge;
  • Multi-agent reinforcement learning;
  • Federated machine learning and federated reinforcement learning;
  • Osmotic computing in highly distributed and federated environments;
  • Machine learning in cloudlets and Fog computing architectures;
  • Knowledge transfer in Mobile Edge Computing architectures;
  • Intelligent algorithms to manage software-defined networks and network function virtualization in Edge–IoT scenarios;
  • Security and privacy frameworks for transferring data and models in Edge–IoT scenarios;
  • Innovative applications of Machine Learning in Edge–IoT scenarios: Industry 4.0, smart cities, healthcare, smart farming, smart energy, etc.

Dr. Ricardo S. Alonso
Dr. Javier Prieto
Dr. Óscar García
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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 papers will be 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 2200 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.


  • Internet of Things
  • Edge Computing
  • Machine Learning
  • Multi-Agent Reinforcement Learning
  • Federated Machine Learning

Published Papers (1 paper)

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BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces
Sensors 2021, 21(10), 3372; - 12 May 2021
Cited by 2 | Viewed by 561
Wearable technologies are becoming a profitable means of monitoring a person’s health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. [...] Read more.
Wearable technologies are becoming a profitable means of monitoring a person’s health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly. They can provide valuable information, especially in work environments, where they help reduce the rate of accidents and occupational diseases, which makes them powerful Personal Protective Equipment (PPE). The constant monitoring of the worker’s health can be done in real-time, through temperature, falls, noise, impacts, or heart rate meters, activating an audible and vibrating alarm when an anomaly is detected. The gathered information is transmitted to a server in charge of collecting and processing it. In the first place, this paper provides an exhaustive review of the state of the art on works related to electronics for human activity behavior. After that, a smart multisensory bracelet, combined with other devices, developed a control platform that can improve operators’ security in the working environment. Artificial Intelligence and the Internet of Things (AIoT) bring together the information to improve safety on construction sites, power stations, power lines, etc. Real-time and historic data is used to monitor operators’ health and a hybrid system between Gaussian Mixture Model and Human Activity Classification. That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised. Where the GMM gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the LSTM obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. This information was sent in real time through the platform that has been used to analyze and process the data in an alarm system. Full article
(This article belongs to the Special Issue Knowledge Transfer in IoT and Edge Computing)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.


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