BDMLN 2024

The 4th International Workshop on Big Data and Machine Learning for Networking

July 29 - 31, 2024, Big Island, Hawaii, USA

Scope

The Internet of Everything (IoE) enables universal connectivity between people, process, data and things through wired or wireless networks. IoE includes People-to-People (P2P), People-to-Machine (P2M) and Machine-to-Machine (M2M) communications. Thus, it has been the backbone of smart-world systems. With an exceptional number of people and things being interconnected in IoE, the data sources and volumes exponentially increase and the process cooperating individual components becomes even sophisticated. This intensifies the heterogeneity, uncertainty and complexity of networking in IoE. Nonetheless, the fog/edge computing in IoE paves the way for big data and machine learning technology to tackle these issues and harness networking benefits. It is relatively novel to use big data and machine learning technology to mine and remember networking characteristics in IoE, and help develop proactive, reactive and predictive algorithms to improve network management, protocol design, service placement, threat detection, security assurance, etc.. The big data and machine learning technology is thus promising to support IoE with delivering successful process, which transmits legitimate data collected from proper things to target people in IoE to create great values across communities, academics, industries and governments. 


This workshop solicits papers in all aspects of big data and machine learning for networking. Topics of interest include, but are not limited to:

·      Data Mining and Big Data Analytics for IoT

·      Machine Learning for IoT

·      BD/ML for Energy Efficient System and Networking

·      BD/ML for Space/Air/Ground Networking

·      BD/ML for Blockchain-Enabled Networking

·      BD/ML for 5G Networking

·      BD/ML for M2M Networking

·      BD/ML for D2D Networking

·      BD/ML for Multimedia Networking

·      BD/ML for Fog/Edge/Cloud Networking

·      BD/ML for Data Center Networking

·      BD/ML for Storage Networking

·      BD/ML for Smart City Networking

·      BD/ML for Mobile Networking

·      BD/ML for Software Defined Networking

·      BD/ML for Network Management

·      BD/ML for Network Security, Privacy and Trust

·      BD/ML for Network Traffic Modeling and Prediction

·      BD/ML for Network Performance Assessment

·      BD/ML for Network Protocol Design and Optimization

·      BD/ML for Network Topology Design and Optimization

·      BD/ML for Network Architecture Design and Optimization

·      BD/ML for Network Scalability and Reliability

·      BD/ML for Network Service Placement

·      BD/ML for Network Virtualization and Automation


Instructions for Authors

Submitted manuscripts must be formatted in standard IEEE camera-ready format (double-column, 10-pt font) and must be submitted via EDAS (http://edas.info/) as PDF files (formatted for 8.5x11-inch paper). The manuscripts should be no longer than 6 pages. Submitted papers cannot have been previously published in or be under consideration for publication in another journal or conference. The workshop program committee reserves the right to not review papers that either exceed the length specification or have been submitted or published elsewhere. Submissions must include a title, abstract, keywords, author(s) and affiliation(s) with postal and e-mail address(es).


Review and Publication of Manuscripts

Submitted papers will be reviewed by the workshop program committee and judged on originality, technical correctness, relevance, and quality of presentation. An accepted paper must be presented at the ICCCN 2024 venue by one of the authors registered at the full registration rate. Each workshop registration covers up to two workshop papers by an author. Accepted and presented papers will be published in the ICCCN proceedings and submitted to IEEE Xplore as well as other Abstracting and Indexing (A&I) databases. IEEE reserves the right to exclude a paper from distribution after the conference, including IEEE Xplore® Digital Library if the paper is not presented by the author at the conference.


ICCCN 2024 Paper Submission Terms and Conditions


Important Dates


Workshop Chair


Technical Program Committee

1. Hongzhi Guo, University of Nebraska Lincoln

2. Yunfei Hou, California State University San Bernardino

3. Genya Ishigaki, San Jose State University

4. Honglu Jiang, Miami University

5. Liuwang Kang, University of Virginia

6. Kee Kim, University of Georgia

7. Kiho Lim, William Paterson University

8. Joydeep Mukherjee, Cal Poly San Luis Obispo

9. Han Wang, University of Kansas

10. Qun Wang, San Francisco State University

11. Xin Yuan, Florida State University

12. Daqing Yun, Harrisburg University

13.  Qingxue (Jack) Zhang, Purdue University


 

Questions?

Contact [nmajd at csusm dot edu] to get more information on the workshop.