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: closed (31 May 2022) | Viewed by 15141
Special Issue Editors
2. BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
3. Higher School of Engineering and Technology, International University of La Rioja (UNIR), Logroño, Spain
Interests: Internet of Things; edge computing; distributed ledger and blockchain technologies; embedded systems; indoor location systems; cloud computing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; blockchain; deep learning; satellite systems; robot vision; cognitive robotics; sensor fusion; data fusion; mobile robotics; wireless networks; robotics; security; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Interests: big data; Artificial Intelligence; IoT; Industry 4.0; energy efficiency
Special Issues, Collections and Topics 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
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Keywords
- Internet of Things
- Edge Computing
- Machine Learning
- Multi-Agent Reinforcement Learning
- Federated Machine Learning
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