posted by organizer: flisdu || 782 views || tracked by 2 users: [display]

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

FacebookTwitterLinkedInGoogle

Link: https://www.journals.elsevier.com/computer-communications/call-for-papers/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

Schedule:

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
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
Huawei Technologies-Munich Research Center
Munich, Germany
Email: Artur.Hecker@huawei.com

Xiuzhen Cheng
Department of Computer Science
The George Washington University
Washington DC 20052, USA
Email: cheng@gwu.edu

Related Resources

MNLP 2020   4th IEEE Conference on Machine Learning and Natural Language Processing
WSPML 2020   2020 2nd International Workshop on Signal Processing and Machine Learning (WSPML 2020)
AICA 2020   O'Reilly AI Conference San Jose
IEEE CiSt 2020   6th IEEE Congress on Information Science and Technology
MAAIDL 2020   Springer Book 'Malware Analysis using Artificial Intelligence and Deep Learning'
IDEAL 2020   IDEAL 2020 : Intelligent Data Engineering and Automated Learning
JIST-KG 2020   Joint International Conference on Knowledge Graphs
ICDM 2020   20th IEEE International Conference on Data Mining
IDA 2020   The 18th International Symposium on Intelligent Data Analysis (IDA 2020)
ICMLC--ACM, Ei and Scopus 2020   ACM--2020 12th International Conference on Machine Learning and Computing (ICMLC 2020)--SCOPUS, Ei Compendex