Intelligent Edge: When Machine Learning Meets Edge Computing

  in Special Issue   Posted on March 6, 2020

Information for the Special Issue

Submission Deadline: Sun 15 Mar 2020
Journal Impact Factor : 2.816
Journal Name : Computer Communications
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/computer-communications/call-for-papers/when-machine-learning-meets-edge-computing
Journal & Submission Website: https://www.journals.elsevier.com/computer-communications

Special Issue Call for Papers:

The explosion of the big data generated by ubiquitous edge devices motivates the emergence of a new computing paradigm: edge computing. It has attracted attention from both academia and industry in recent years. In edge computing, computations are deployed mainly at the local network edge rather than at remote central computing infrastructures, thereby considerably reducing latency and possibly improving computation efficiency. This computing model has been applied in many areas such as mobile access networks, Internet of Things (IoT), and microservices, enabling novel applications that drastically change our daily lives. As a second trend, a new era of Artificial Intelligence (AI) research has delivered novel machine learning techniques that have been utilized in applications such as healthcare, industry, environment engineering, transportation, smart home and building automation, all of which heavily rely on technologies that can be deployed at the network’s edge. Therefore, intuitively, marrying machine learning techniques with edge computing has high potential to further boost the proliferation of truly intelligent edges.

In light of the above observations, in this special issue, we look for original work on intelligent edge computing, addressing the particular challenges of this field. On one hand, conventional machine learning techniques usually entail powerful computing infrastructures (e.g., cloud computing platforms), while the entities at the edge may have only limited resources for computations and communications. This suggests that machine learning algorithms or, at least, the implementations of machine learning algorithms, should be revisited for edge computing, which represents a considerable risk and challenge at once. On the other hand, the adapted deployments of machine learning algorithms at the edge empower the “smartification” across different layers, e.g., from network communications to applications. This in turn allows new applications of machine learning and artificial intelligence, opening up new opportunities. The goal of this special issue is to offer a venue for researchers from both academia and industry to present their solutions for re-designing machine learning algorithms compatible to edge computing, and for building intelligent edge by machine learning techniques, possibly revealing new, compelling use cases.

Relevant topics include, but are not limited to:

l System architectures of intelligent edge computing

l Modeling, analysis and measurement of intelligent edge computing

l Machine learning algorithms and systems for edge computing

l Machine learning-assisted networking and communication protocols for or using edge computing

l Intelligent mobile edge computing

l Architectures, techniques and applications of intelligent edge cloud

l Resource management for intelligent edge computing

l Security and privacy of intelligent edge computing

l Data management and analytics of intelligent edge computing

l Intelligent edge-cloud collaborations

l Programming models and toolkits for intelligent edge computing

l Distributed machine learning algorithms for edge computing

l Smart applications of edge computing

Important Dates

Manuscript Due: March 15, 2020

First Notification: June 30, 2020

Revised version: August 15, 2020

Final notification: September 15, 2020

Publication Date: The 1st quarter of 2021 (tentative)

Original, high quality contributions that are not yet published and that are not currently under review by any other journal or peer-reviewed conference are sought. Prospective authors should prepare their manuscript in accordance with the Computer Communications format described at https://www.journals.elsevier.com/computer-communications. To ensure that all manuscripts are correctly identified for inclusion into this Special Issue, the authors have to select “Special Issue: IntEdge: When ML meets EC” when they reach the “Article Type” step in the submission process.

 

Guest Editors

Feng Li

School of Computer Science and Technology

Shandong University

Qingdao, Shandong, 266237, P. R. China

Email: fli@sdu.edu.cn

Holger Karl

Department of Computer Science

Paderborn University

Paderborn, 33098, Germany

Email: hkarl@mail.uni-paderborn.de

Jiguo Yu

School of Computer Science and Technology

Qilu University of Technology (Shandong Academy of Sciences)

Jinan, Shandong, 250353, P. R. China

Email: jiguoyu@sina.com

Artur Hecker

Munich Research Center

Huawei Technologies Duesseldorf GmbH

Munich, 80992, Germany

Email: Artur.Hecker@huawei.com

Xiuzhen Cheng (Lead Guest Editor)

Department of Computer Science

The George Washington University

Washington DC 20052, USA

Email: cheng@gwu.edu

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