We have published a special issue of MDPI Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 August 2021.

Link for manuscript submissions: www.mdpi.com/journal/information/special_issues/AI_on_the_edge.

Special Issue Information

As the demand for Internet of Things (IoT) solutions has grown over time, edge computing has gained momentum, because of the need to move computation close to data sources. Indeed, the rise of connected devices, which Gartner estimates number more than 20 quadrillions in 2020, is growing the need for computation in scenarios where prompt responses are crucial. On the other hand, the upcoming deployment of 5G networks will bring drastic performance improvements, traffic optimization, and new ultra-low-latency services in locations where cloud connectivity is too low, such as in oil platforms or cruise ships.

On the other hand, the increasing number of connected devices is also generating a huge number of raw data directly on the edge. For example, Cisco estimates that nearly 850 ZB will be generated by people, machines, and things at the network edge by 2021 [1]. That is where artificial intelligence (AI) steps in, leading data transformation in real-time extracted business value. Therefore, the migration of machine learning and deep learning techniques over to the edge enables a new field of research, where the intelligence is distributed over devices. In this context, TensorFlow has already released a tool that enables AI on the edge, but many challenges remain.

The benefits of AI on the edge are typically visible over several application fields, such as wearable technologies, smart homes, smart cities, 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 migration to the edge of artificial intelligence services. Topics of interest include, but are not limited to:

- Machine learning services on the edge;
- Deep learning services on the edge;
- The migration of AI-based services from the cloud into the edge;
- The optimization of real-time, AI-based solutions on the edge;
- Edge-centric distributed intelligent services;
- Edge-centric collaborative intelligent services;
- Edge-centric federated intelligent services;
- The security of data distribution over AI-based edge systems;
- Trust and privacy management in AI-based edge systems;
- The quality of services and energy efficiency for AI-based edge systems;
- AI for the IoT;
- AI for microcontroller and microprocessor.

Dr. Lorenzo Carnevale, Università degli Studi di Messina, Messina, ItalyDr. Massimo Villari, Università degli Studi di Messina, Messina, ItalyGuest Editors

Keywords

- Artificial intelligence
- Machine learning
- Deep learning
- Edge computing
- Internet of Things