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Elsevier ComCom Special Issue 2020 : Intelligent Edge: When Machine Learning Meets Edge Computing


When N/A
Where N/A
Submission Deadline TBD

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 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:

1. System architectures of intelligent edge computing
2. Modeling, analysis and measurement of intelligent edge computing
3. Machine learning algorithms and systems for edge computing
4. Machine learning-assisted networking and communication protocols for or using edge computing
5. Intelligent mobile edge computing
6. Architectures, techniques and applications of intelligent edge cloud
7. Resource management for intelligent edge computing
8. Security and privacy of intelligent edge computing
9. Data management and analytics of Intelligent edge computing
10. Intelligent edge-cloud collaborations
11. Programming models and toolkits for Intelligent edge computing
12. Distributed machine learning algorithms for edge computing
13. Smart applications of edge computing


Manuscript Due: January 15th, 2020
First Notification: April 31st, 2020
Revised version: June 15th, 2020
Final notification: July 15th, 2020
Publication Date: The 1st quarter of 2021 (tentative)

Guest Editors:

Feng Li
School of Computer Science and Technology
Shandong University
Qingdao, Shandong, 266237, P. R. China

Holger Karl
Department of Computer Science
Paderborn University
Paderborn, 33098, Germany

Jiguo Yu
School of Computer Science and Technology
Qilu University of Technology (Shandong Academy of Sciences)
Jinan, Shandong, 250353, P. R. China

Artur Hecker
Huawei Technologies-Munich Research Center
Munich, Germany

Xiuzhen Cheng
Department of Computer Science
The George Washington University
Washington DC 20052, USA

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