Call for Papers

The recent revival of artificial intelligence (AI) is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile terminals and Internet of Things (IoT) devices, it is expected that a majority of intelligent applications will be deployed at the edge networks. Therefore, providing intelligent and sustainable edge computing services will be a hot topic in IoT and 5G services as sustainability of edge systems becomes a necessity with the rapid constant growth of edge devices/sensors.

Edge learning is needed to support edge computing services. However, it faces great challenges when considering the latency-sensitive requirements, network bandwidth limitation, computing power limitation, as well as limited data at each edge device. AI-based technologies, such as statistical learning, feedforward neural networks, and deep recurrent neural networks, among others, are expected to construct the intelligent edge for improving Quality of Services (QoS) and Quality of Experience (QoE). These learning-based methods are implemented for sophisticated decision making, network management, resource optimization, and in-depth knowledge discovery in complex environments. The intelligence built by using edge learning techniques can help promote better decision making and contribute to building greener and more sustainable systems.

To address several major issues regarding the sustainability of edge computing services, this special issue highlights edge learning techniques to provide intelligent and greener edge computing services. Learning-based approaches are required to obtain more clear and in-depth knowledge of the behavior of edge networks. Submissions are expected to address how to build greener and more sustainable edge systems through learning-based approaches.

The scope of this Special Issue (SI) is to present and highlight the advances and the latest intelligent technologies, implementations and applications in the field of learning-based edge computing services, so as to build greener and more sustainable edge systems.

The topics of interest include, but are not limited to:

• Innovative architectures, frameworks, and models for learning-based edge computing.
• Theory, standards, protocols, and strategies for learning-based edge computing services in IoT.
•  Machine learning, AI and other innovative optimization approaches for learning-based edge computing services.
• Intelligent and interactive IoT services and applications assisted by machine learning and edge computing.
• Intelligent decision-making systems for learning-based edge computing services.
• Smart task caching at edges by joint optimization of computation, caching and communication.
• Security and privacy assisted by learning-based edge computing in IoT.
• Learning-based testbeds, simulations, experiments and evaluation for edge computing.

Submission Guidelines

Manuscripts should conform to the standard format as indicated in the Information for Authors section of the Paper Submission Guidelines.

All manuscripts to be considered for publication must be submitted by the deadline through Manuscript Central. Select "January 2021/Learning-based Edge Computing Services" from the drop-down menu of Topic/Series titles.

Important Dates

Manuscript Submission Deadline: 5 April 2020
First Revisions/Reject Notification: 1 July 2020
Revision Submission Deadline: 15 August 2020
Notification of Acceptance/Reject: 15 September 2020
Final Manuscript Due: 15 October 2020
Publication Date: January 2021

Guest Editors

Min Chen
Huazhong University of Science and Technology, China

Haiyang Wang
University of Minnesota at Duluth, USA

Kai Hwang
The Chinese University of Hong Kong, China

Giancarlo Fortino
University of Calabria, Italy

Jeungeun Song
The University of British Columbia, Canada

Limei Peng
Kyungpook National University, South Korea

Joze Guna
University of Ljubljana, Slovenia