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Authors are invited to submit previously unpublished papers to this Workshop.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.

For submissions, click here:

https://edas.info/N33595

TPC Members

Dr. James Brusey, Coventry University, United Kingdom

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

Prof. Klaus David, University of Kassel, Germany

Prof. Ke-Lin Du, Concordia University, USA

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

Prof. Youssef BADDI, UCD, El Jadida, Morocco

Dr. Nariman Farsad, Stanford University USA

Prof . Mohamed El kamili, University Hassan II, Casablanca Morocco

Prof. Jihene Rezgui, College Maisonneuve, Canada

Dr. Benoit Hudzia, StateStreet Bank, United Kingdom

Prof. Jingon Joung,  Chung-Ang University, Korea

Mr. Jun-Won Kim, Chungnam National University, Korea

Prof. Steven Latré , University of Antwerp - imec, Belgium

Prof. Chun-Hung Liu, Mississippi State University, USA

Dr. Tony Luo, Institute for Infocomm Research, Singapore

Prof. Amitabh Mishra, University Of Delaware USA

Dr. Cong Shen University of Science and Technology of China

Dr. Catarina Silva, University of Coimbra, Portugal

Mr. Angelo Spognardi Sapienza, University of Rome Italy

Prof. Nick Taylor, Heriot-Watt University, United Kingdom

Prof. Chen-Khong Tham, National University of Singapore

Dr. Dan-Cristian Tomozei ,Cisco Switzerland

Prof. Minghua Xia, Sun Yat-sen University China

Dr. Kuai Xu, Arizona State University, USA

Dr. Yi Zou Intel Corp. USA