Honorary Chairs

Carlos Becker Westphall 

Federal University of Santa Catarina, Brazil

Abderrahim Sekkaki

University HASSAN II of  Casablanca,Morocco 

Workshop Chairs

Mohamed Lahby     

University HASSAN II of  Casablanca, Morocco,  mlahby@gmail.com

Al-Sakib Khan Pathan 

Southeast University, Bangladesh         spathan@ieee.org

Scope

The future network world will be embedded with many network architectures such as wireless sensor networks (WSN), Internet of things (IoT), Cloud networks (CN), Fetmocellus networks (FN), Vehicular networks (VN), and others. As innovative services and applications arise in these network architectures, network management service approaches will need to support scalability and robustness in a more proactive and intelligent fashion.

In recent years, Machine learning (ML) techniques have shown promise to be powerful tools in various domains, such as computer vision, natural language processing (NLP), speech recognition, computational biology, and others. Motivated by these successes, researchers all over the world have recently started to investigate applications of this technology to deal with problems ranging from radio access technology (RAT) selection to low-latency communication, to low energy consumption in WSN, to manage a massive number of IoT devices in real-time as well as the development of networked systems that support machine learning practices.

The objective of this Workshop is to bring together researchers to discuss recent developments related to all aspects of machine learning applied to communication and networking systems.

Topics

Authors are invited to submit previously unpublished papers to this Workshop. Topics include, but are not limited to:

·        Machine learning for next-generation wireless networks

·        Machine learning for next-generation cognitive networks

·        Machine learning for communication and network resource optimization

·        Machine learning for communication and network operation and control

·        Machine learning applied to WSN Applications

·        Machine learning applied WSN Data Management

·        Machine learning applied to WSN Data processing

·        Machine learning applied to IoT Applications

·        Machine learning applied IoT Data Management

·        Machine learning applied to IoT Data processing

·        Machine learning applied to Vehicular network applications

·        Performance analysis of machine learning algorithms in next generation networks

Prospective authors are invited to submit an original Full or Short paper in IEEE PDF format via  the EDAS online submission system: https://isncc-2021-mlngsnworkshop.edas.info/

Important Dates

 Submission Deadline:                                                                 April 1st, 2021                                                                                                       

 Acceptance Notification:                                                              April 15, 2021

 Registration and Camera-ready copies of accepted papers :      May 1st, 2021

 

TPC Members

James Brusey, Coventry University, United Kingdom

Diego Carvalho Federal Centre for Engineering Studies and Technological Education - Brazil

Klaus David, University of Kassel, Germany

Ke-Lin Du, Concordia University, USA

Salim El khediri, College of Computer & Qassim University, Saudi Arabia

Youssef BADDI, UCD, El Jadida, Morocco

Nariman Farsad, Stanford University USA

Mohamed El kamili, University Hassan II, Casablanca Morocco

Jihene Rezgui, College Maisonneuve, Canada

Benoit Hudzia, StateStreet Bank, United Kingdom

Jingon Joung,  Chung-Ang University, Korea

Jun-Won Kim, Chungnam National University, Korea

Steven Latré , University of Antwerp - imec, Belgium

Chun-Hung Liu, Mississippi State University, USA

Tony Luo, Institute for Infocomm Research, Singapore

Amitabh Mishra, University Of Delaware USA

Cong Shen University of Science and Technology of China

Catarina Silva, University of Coimbra, Portugal

Angelo Spognardi Sapienza, University of Rome Italy

Nick Taylor, Heriot-Watt University, United Kingdom

Chen-Khong Tham, National University of Singapore

Dan-Cristian Tomozei ,Cisco Switzerland

Minghua Xia, Sun Yat-sen University China

Kuai Xu, Arizona State University, USA

Yi Zou Intel Corp. USA