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W1: Machine Learning for Sensing, Communication and Networking in IoT - VTC2020-Fall Victoria

Paper Submission Deadline Extended to 19 June 2020

W1: Machine Learning for Sensing, Communication and Networking in IoT

Organizer: Waleed Ejaz, Thompson Rivers University, Canada
Organizer: Shree Krishna Sharma, SnT, University of Luxembourg, Luxembourg

Abstract: Recent advances in sensing, communications and networking have enabled the support of real-life applications within several Internet of Things (IoT) verticals including smart city, smart home, smart transportation, smart utilities and E-health. In order to support the ever-increasing number of IoT devices and heterogeneous applications, it is crucial to design resource-efficient and scalable upcoming 5G and beyond systems in a way that they can operate in the complex wireless environment while supporting the emerging Machine-Type Communications (MTC)/IoT traffic. Towards enabling the effective design and operation of these IoT systems, the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques has recently caught the attention of various research communities, industries and the related stakeholders. On one hand, some researchers argue that employing ML in communication systems offers little benefits because communication systems were primarily designed for bandwidth, power and complexity optimization. On the other hand, big data-driven solutions, including Deep Learning (DL) can be highly advantageous for data-driven prediction, analysis and performance improvement by utilizing time-dependent properties of network elements. This could be achieved by adapting the ML/DL models to the dynamicity of the underlying communication environment along with the training data generated by the IoT devices. In addition, DL techniques may provide significant benefits towards automating the conventional acquisition/sensing/processing/computing tasks in communication networks supporting heterogeneous IoT applications, and towards enhancing the security and privacy of IoT systems. However, there are several issues to be addressed for the applications of ML/DL techniques in the complex IoT environments involving resource-constrained IoT/MTC devices, including heterogeneity, limited computational capability, distributed nature, and distinct traffic characteristics.

Bio: Dr. Waleed Ejaz (S’12-M’14-SM’16) is an Assistant Professor in the Department of Applied Science & Engineering at Thompson Rivers University, Kamloops, BC, Canada. Previously, he held academic and research positions at Ryerson University, Carleton University, and Queen’s University in Canada. He received the B.Sc. and M.Sc. degrees in Computer Engineering from the University of Engineering and Technology, Taxila, Pakistan and the National University of Sciences and Technology, Islamabad, Pakistan, and the Ph.D. degree in Information and Communication Engineering from Sejong University, Republic of Korea, in 2014. He has co-authored over 90 papers in prestigious journals and conferences, and 3 books. His current research interests include Internet of Things (IoT), energy harvesting, 5G and beyond networks, and mobile edge computing. He is an Associate Editor of the IEEE Communications Magazine, IEEE Canadian Journal of Electrical and Computer Engineering, and the IEEE ACCESS. Dr. Ejaz completed certificate courses on “Teaching and Learning in Higher Education” from the Chang School at Ryerson University. He is a registered Professional Engineer (P.Eng.) in the province of British Columbia, Canada. Dr. Ejaz is a senior member of IEEE, member of ACM, and ACM distinguished speaker.

Bio: Dr. Shree Krishna Sharma (S’12-M’15-SM’18) is currently Research scientist at the Interdisciplinary Center for Security, Reliability and Trust (SnT), University of Luxembourg. Prior to this, he held research positions at the University of Western Ontario, Canada, and Ryerson University, Canada, and also worked as a Research Associate at the SnT after receiving his PhD degree in Wireless Communications from the University of Luxembourg in 2014. He has published about 100 technical papers in scholarly journals, international conferences, and book chapters, and has over 2100 google scholar citations with an h-index of 22 and an i-10 index of 47. His current research interests include 5G and beyond wireless, Internet of Things, machine-type communications, machine learning, edge computing and optimization of distributed communications, computing and caching resources. He is a Senior Member of IEEE and is the recipient of several prestigious awards including “FNR Award for Outstanding PhD Thesis 2015” from FNR, Luxembourg, “Best Paper Award” in CROWNCOM 2015 conference, “2018 EURASIP JWCN Best Paper Award” and “FNR Award for Outstanding Scientific Publication 2019” from FNR, Luxembourg. He has been serving as a Reviewer for several international journals and conferences; as a TPC member for a number of international conferences including IEEE ICC, IEEE GLOBECOM, IEEE PIMRC, IEEE VTC and IEEE ISWCS; and an Associate Editor for IEEE Access journal. He co-organized a special session in IEEE PIMRC 2017, a workshop in IEEE SECON 2019, worked as a Track co-chair for IEEE VTC-fall 2018 conference, and published an IET book on “Satellite Communications in the 5G Era’’ as a lead editor.

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