Emerging Trends and Challenges in Edge Computing
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".
Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 311
Special Issue Editor
Special Issue Information
Dear Colleagues,
With the rapid development of communication technologies and smart devices, there is a rapid increase in the number of connected users and the number of different applications and services which produce a high volume of data traffic. Traditional computing infrastructures rely on centralized cloud computing systems to collect and process such large data. However, given the distributed nature of the smart devices and the Internet-of-Things (IoT) applications as well as privacy and security concerns associated with the centralized cloud infrastructure, edge computing has emerged as an alternative technology for large data processing. On the other hand, machine learning and specially Deep Neural Network (DNN) models appear as popular and effective tools for processing such large amount of data and extracting useful information and predictive knowledge. Therefore, techniques to efficiently and reliably combine edge computing infrastructure and DNN models in such large environments are required. This special issue address challenges and trends related to edge computing and bringing DNN to the edge.
- Resource management in edge computing
- Edge computing for IoT
- Federated and Split learning for edge computing
- Distributed machine learning and edge computing
- Privacy and security issues for edge computing
- Machine learning model compression for IoT and edge devices
- Wireless issues in edge computing
Prof. Dr. Hana Khamfroush
Guest Editor
If you have any questions or need further information, please free to contact Special Issue Editor Larissa Zhang <[email protected]>.
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