W5: 8th Workshop on Connected Intelligence for IoT and Industrial IoT Applications- C3IA

Co-chair: Abdellah Chehri, Royal Military College of Canada, Canada
Co-chair: Gwanggil Jeon, Incheon National University, Korea
Co-chair: Imran Ahmed, Anglia Ruskin University, UK
Co-chair: Vu Khanh Quy, Hung Yen University of Technology and Education, Vietnam

Keynote Speaker: Pei Xiao, University of Surrey, United Kingdom

Abstract: Nowadays, industrial enterprises and companies are addressing the challenge of transforming Industrial IoT (IIoT) ideas, Industry 4.0, Cyber-Physical Systems (CPS), and similar concepts into reality. In the Industry 4.0 era, various data management research challenges have to be addressed. Huge amounts of heterogeneous sensor data must be processed in real-time to control the production machines. Data processing through smart devices is more significant compared to information processing capacity. Data has become humongous, even coming from a single source. Besides, unstructured data from production reports or external sources must also be integrated to analyze and optimize the production process. Therefore, when data emanates from all heterogeneous sources distributed over the globe, its magnitude makes it harder to process up to a needed scale.

The world has seen many breakthroughs in machine learning and artificial intelligence research. By integrating the advances in smart devices, and big data analysis with the advances in machine learning, the future role of smart systems, networks, and applications is becoming limitless. It’s expected to revolutionize the world’s future within the next few years. By integrating hardware, software, data collection, and advanced data analytics techniques, such as predictive and prescriptive analysis, advanced systems can develop by leveraging tools and real-time insights on industry performance. Furthermore, advanced artificial intelligence solutions enable deeper insights and more intelligent, more agile methods that improve operational performance at an industrial scale.

Keynote Speaker:

Pei Xiao

Title: Multitask Learning Empowered SCMA System Design for Industrial IoT Applications

 

 

Abstract: Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme, and serves as an enabler for industrial IoT applications. The design of good sparse codebooks and efficient multiuser decoding are central to the SCMA system performance. In the first part of the talk, we discuss how to leverage deep learning to jointly design the SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. In this second part of the talk, we present SCMA assisted grant-free multiple access (GF-MA) schemes to minimize the signalling overhead and latency of the random access protocol. To alleviate the throughput degradation under the congested user traffic, a user barring mechanism is applied to manage the traffic load. It is shown that the proposed schemes enjoy higher maximum throughput and lower access latency, compared to the conventional random access scheme.

Biography

Pei Xiao is a professor of Wireless Communications at the Institute for Communication Systems, home of 5GIC and 6GIC at the University of Surrey. He is the technical manager of 5GIC/6GIC, leading the research team in the new physical layer work area, and coordinating/supervising research activities across all the work areas (https://www.surrey.ac.uk/institute-communication-systems/5g-6g-innovation-centre). Prior to this, he worked at Newcastle University and Queen’s University Belfast. He also held positions at Nokia Networks in Finland. He has published extensively in the fields of communication theory, RF and antenna design, signal processing for wireless communications, and is an inventor on over 15 recent 5GIC patents addressing bottleneck problems in 5G systems.

 

Co-chair Bios:

Abdellah Chehri

Bio: Dr A. Chehri is an Associate Professor at the Department of Mathematics and Computer Science at the Royal Military College of Canada (RMC), Kingston, Ontario. Dr. Chehri completed his Ph.D. at University Laval (Quebec) and his Master’s studies at University Nice-Sophia Antipolis-Eurecom (France). Dr. Chehri is a co-author of more than 200 peer-reviewed publications in established journals and conference proceedings sponsored by established publishers such as IEEE, ACM, Elsevier, and Springer. Dr. Chehri has served on roughly thirty conference and workshop program committees. In addition, he served as guest/associate editor for several well-reputed journals. Additionally, he is a Senior Member of IEEE, a member of the IEEE Communication Society, IEEE Vehicular Technology Society (VTS), and IEEE Photonics Society.

Gwanggil Jeon

Bio: Dr. G. Jeon received his B.S., M.S., and Ph.D. degrees from Hanyang University, Korea, in 2003, 2005, and 2008, respectively. From 2009 to 2011, he was a postdoctoral fellow at the University of Ottawa, Canada, and from 2011 to 2012, he was an assistant professor at Niigata University, Japan. He is a professor at Xidian University, China and Incheon National University, South Korea. His research interests fall under the umbrella of image processing, deep learning, artificial intelligence, smart grid, and Industry 4.0.

Imran Ahmed

Bio: Dr. Imran Ahmed (Senior Member, IEEE) is currently associated with Anglia Ruskin University, Cambridge, UK. He received his PhD degree in computer science from the University of Southampton, Southampton, U.K., in 2014. He also completed post-doctoral research degrees from the Incheon National University, South Korea, in Dec 2020 and from the University of Quebec in Chicoutimi, Quebec, Canada, in Sep 2021. He also worked as an Associate Professor with the Institute of Management Sciences, Hayatabad, Peshawar. His research interests include deep learning, machine learning, data science, computer vision, feature extraction, digital image and signal processing, medical image processing, biometrics, pattern recognition, and data mining. He has attended several national and international conferences in these areas and published numerous articles in refereed journals and conference proceedings. Dr Ahmed has been a guest editor and technical reviewer in several international journals and conferences.

Vu Khanh Quy

Bio: Dr. V. K. Quy was born in Hai Duong, Vietnam, in 1982. He received the M.Sc. and Ph.D. degrees from the Posts and Telecommunications Institute of Technology, Hanoi, Vietnam, in 2012 and 2021, respectively. He is currently a Lecturer with the Hung Yen University of Technology and Education (UTEHY), Hung Yen, Vietnam. His research interests include wireless communications, mobile computing, smart IoT systems, next-generation networks, Internet of Things, and next-communication networks

To submit a paper, please visit: https://vtc2024s-rr-wks.trackchair.com/track/2255