Special Issue "Artificial Intelligence on the Edge"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 28 February 2022.

Special Issue Editors

Dr. Lorenzo Carnevale
E-Mail Website
Guest Editor
Department of Mathematics, Computer Science, Physics and Hearth Sciences (MIFT), University of Messina, 98166 Messina, Italy
Interests: Artificial Intelligence; Cloud Computing; Edge Computing; Big Data
Special Issues and Collections in MDPI journals
Dr. Massimo Villari
E-Mail Website
Guest Editor
Universita degli Studi di Messina, Messina, Italy
Interests: Osmotic Computing; Cloud Computing; Fog-Edge Computing; IoT; IT Security

Special Issue Information

Dear Colleagues,

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 quadrillion 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
Dr. Massimo Villari
Guest Editors


  1. https://www.cisco.com/c/en/us/solutions/executive-perspectives/annual-internet-report/index.html

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


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

Published Papers (1 paper)

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Combine-Net: An Improved Filter Pruning Algorithm
Information 2021, 12(7), 264; https://doi.org/10.3390/info12070264 - 29 Jun 2021
Viewed by 542
The powerful performance of deep learning is evident to all. With the deepening of research, neural networks have become more complex and not easily generalized to resource-constrained devices. The emergence of a series of model compression algorithms makes artificial intelligence on edge possible. [...] Read more.
The powerful performance of deep learning is evident to all. With the deepening of research, neural networks have become more complex and not easily generalized to resource-constrained devices. The emergence of a series of model compression algorithms makes artificial intelligence on edge possible. Among them, structured model pruning is widely utilized because of its versatility. Structured pruning prunes the neural network itself and discards some relatively unimportant structures to compress the model’s size. However, in the previous pruning work, problems such as evaluation errors of networks, empirical determination of pruning rate, and low retraining efficiency remain. Therefore, we propose an accurate, objective, and efficient pruning algorithm—Combine-Net, introducing Adaptive BN to eliminate evaluation errors, the Kneedle algorithm to determine the pruning rate objectively, and knowledge distillation to improve the efficiency of retraining. Results show that, without precision loss, Combine-Net achieves 95% parameter compression and 83% computation compression on VGG16 on CIFAR10, 71% of parameter compression and 41% computation compression on ResNet50 on CIFAR100. Experiments on different datasets and models have proved that Combine-Net can efficiently compress the neural network’s parameters and computation. Full article
(This article belongs to the Special Issue Artificial Intelligence on the Edge)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Context-Preserving AI-Based Edge Systems: A Survey and Outlook
Authors: Catherine Inibhunu
Affiliation: Faculty of Business and Information Technology
Abstract: The potential to derive knowledge from vast data sources and settings can have significant impact on multiple applications from smart healthcare, smart buildings, smart energy consumption to smart cities whereby data driven recommendations on provision of services is enabled. This can be facilitated through deployment of end to end data services that effectively allows collection, transmission, processing, streaming, and knowledge discovery through integrated machine learning (ML) algorithms to provision of data driven services. However, the content and context of the data captured at the edge can be quite invasive from physiology about humans from wearables to sensitive building details including their location. To this respect, services that seek to harness such data must incorporate means for which the privacy of data sources and context is preserved. Preserving data privacy at the edge is an area still in infancy. In this paper, a review of recent methods proposed for preserving data privacy is presented. This includes a discussion on how the existing methods have been applied, highlighting the current limitations and pointing out the principles that need to be integrated within ML services at the edge in order to ensure the privacy of data sources and context are well preserved while still providing data driven recommendations in provision of critical services.

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