Convolutional Neural Networks and Edge Computing Application

Dear Colleagues,

Edge computing has emerged as a new computing paradigm in the last few years due to the explosion of big data generated by millions of edge devices that exchange information without the need for central coordination. In the edge computing paradigm, both computation and data storage and management are pushed mainly at the edge of the network, on the end devices where the data are usually produced and the processing of data is often required.

This brings several advantages: a reduced transmission latency, an improved computation efficiency, lower network congestion, the ability to be much more scalable than a central system, and greater reliability from failures. Additionally, the privacy and the security of the data are preserved since the data do not need to traverse the network to reach a central server.

However, this new paradigm also poses new challenges, such as the handling of a massive amount of data. The massive diffusion of artificial intelligence technologies, such as convolutional neural networks, can certainly help to address the new challenges posed by edge computing and can further boost the advantages of this new paradigm.

This Special Issue seeks original, previously unpublished works addressing the issues and challenges related to the design, implementation, deployment, operation, and evaluation of new solutions based on the integration of the edge computing paradigm with convolutional neural networks.

Dr. Claudio Gennaro
Dr. Claudio Vairo
Guest Editors

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