Dear Colleague,
As Guest Editors, we cordially invite you to submit a manuscript for consideration and possible publication in a Special Issue on "Artificial Intelligence on the Edge" to be published in the journal Information ( http://www.mdpi.com/journal/information | ISSN 2078-2489 ), an EI, Scopus et al. indexed, open-access journal.
We invite you to contribute a feature paper to be published with a 100% discount!
Kindly remind that the Special Issue deadline is February 28th, 2022, but papers can be published immediately after acceptance on an ongoing basis even before the deadline.
Thank you very much for your consideration. We look forward to hearing from you soon
All best,
Lorenzo Carnevale and Massimo Villari
Guest Editors
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This Special Issue on “Artificial Intelligence on the Edge” aims to lead the data transformation in real-time extracted business value. Therefore, the migration of Machine Learning and Deep Learning techniques over the Edge enables a new field of research where intelligence is distributed over devices. In this context, TensorFlow has already released a tool that enables AI on the Edge, but a lot of challenges are still open.
The benefits of AI on the Edge are typically visible over several application fields, such as wearables technologies, Smart Home, Smart City, Industry 4.0, agriculture, autonomous driving, video surveillance, social and industrial robotics, etc.
This special issue aims to promote high-quality research on all the aspects related to the training, inference, and migrations of Artificial Intelligence service into the Edge. Topics of interest include, but are not limited to:
* Machine Learning services on the Edge
* Deep Learning services on the Edge
* Migration of AI-based services from Cloud into Edge
* Optimization of real-time AI-based solution on the Edge
* Edge-centric distributed intelligent services
* Edge-centric collaborative intelligent services
* Edge-centric federated intelligent services
* Security of data distribution over AI-based Edge systems
* Trust and privacy management in AI-based Edge systems
* Quality of Services and energy efficiency for AI-based Edge systems
* AI for IoT
* AI for microcontroller and microprocessor
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Eng. Lorenzo Carnevale, PhD
Assistant Professor
fcrlab.unime.it<http://fcrlab.unime.it/>
University of Messina,
Messina, Italy
mobile: 0039 351 911 3346
website: lorenzocarnevale.com<http://www.lorenzocarnevale.com/>