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previously unpublished papers to this Workshop.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
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
