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Keynote Lectures

6G: Vision, Requirements, Technical Challenges, Standardization & Implementations
Shahid Mumtaz, Instituto de Telecomunicações, Portugal

LPWAN Technologies: The Use Case of LoRa-based WSNs and Its Applications
Sandra Sendra, Universitat Politécnica de Valencia, Spain

Efficient Deep Learning Methods for IoT Applications: Current Challenges and Future Directions
Muhammad Sajjad, NTNU, Norway

Fusing Intelligence into Big Data Transfer in High-performance Networks
Chase Wu, New Jersey Institute of Technology, United States

 

6G: Vision, Requirements, Technical Challenges, Standardization & Implementations

Shahid Mumtaz
Instituto de Telecomunicações
Portugal
 

Brief Bio

Shahid Mumtaz is an IET Fellow, IEEE and ACM Distinguished speaker, recipient of IEEE ComSoC Young Researcher Award (2020), IEEE Senior member, founder and EiC of IET “Journal of Quantum communication”, Vice-Chair: Europe/Africa Region- IEEE ComSoc: Green Communications & Computing society and Vice-chair for IEEE standard on P1932.1: Standard for Licensed/Unlicensed Spectrum Interoperability in Wireless Mobile Networks. He is also a Visiting Senior 5G Consultant at Huawei, Sweden.

He has more than 12 years of wireless industry/academic experience. He has received his Master's and Ph.D. degrees in Electrical & Electronic Engineering from Blekinge Institute of Technology, Sweden, and University of Aveiro, Portugal in 2006 and 2011, respectively. From 2002 to 2003, he worked for Pak Telecom as System Engineer and from 2005 to 2006 for Ericsson and Huawei at Research Labs in Sweden. He has been with Instituto de Telecomunicações since 2011 where he currently holds the position of Auxiliary Researcher and adjunct positions with several universities across the Europe-Asian Region.

 He is the author of 4 technical books, 12 book chapters, 250+ technical papers (150+ Journal/transaction, 80+ conference, 2 IEEE best paper award- in the area of mobile communications. He had/has supervised/co-supervising several Ph.D. and Master Students. He uses mathematical and system-level tools to model and analyze emerging wireless communication architectures, leading to innovative Master's theoretically optimal new communication techniques. He is working closely with leading R&D groups in the industry to transition these ideas to practice. He secures the funding of around 2M Euro.


Abstract
6G is the next step in the evolution of mobile communication and will be a key component of the Networked Society. In particular, 5G will accelerate the development of the Virtual world. To enable connectivity for a wide range of applications and use cases, the capabilities of 6G wireless access must extend far beyond those of previous generations of mobile communications. Examples of these capabilities include ultra high data rates, ultra low latency, ultra-high reliability, energy efficiency and extreme device densities, and will be realized by the development of 5G in combination with new radio-access technologies. Therefore, this talk explains the different key technology components of 6G and from implementation to standardization.



 

 

LPWAN Technologies: The Use Case of LoRa-based WSNs and Its Applications

Sandra Sendra
Universitat Politécnica de Valencia
Spain
 

Brief Bio
Dr. Sandra Sendra (sansenco@upv.es) received her degree of Technical Engineering in Telecommunications in 2007. She received her M.Sc. of Electronic Systems Engineering in 2009 and her Ph.D. in electronic engineering (Dr. Ing.) in 2013. Currently, she is associate professor at the Polytechnic University of Valencia (Spain). She is Cisco Certified Network Associate Instructor since 2009 and HP-ATA instructor since 2015. She is vocal inside the IEEE Spain Section for the term 2020-2021, member of the Membership Development group for the term 2018-2019 and active member inside the IEEE WIE Spain for the term 2016-2018. She has authored 6 book chapters and 2 books. She has more than 110 research papers published in national and international conferences, international journals (more than 45 with ISI Thomson JCR). She has been the co-editor of 8 conference proceedings and guest editor of several international journals. She is editor-in-chief of the international journal "WSEAS Transaction on Communications" since 2012, guest editor several SI in International Journals related to underwater communications, sensors and actuator networks (Sensors and Applied Science by MDPI and International Journal ACM/Springer Mobile Networks & Applications (MONET) by ACM/Springer).She has been the co-editor of 10 conference proceedings and associate editor in 6 international journals: “Network Protocols and Algorithms”, “International Journal On Advances in Intelligent Systems”, “International Journal On Advances in Networks and Services”, “International Journal On Advances in Telecommunications”, “Designs”, “Signals” She has been involved in more than 100 Program committees of international conferences, and more than 50 organization and steering committees. She has been the general chair (or co-chair) of 3 International conferences. Her research interests, but no limited, include saving energy techniques in electronic circuits, sensor deployment, WSN, UWSN and the application of these technologies for environmental monitoring.


Abstract
Wireless sensor networks have been consecrated, over the years, as the integral solution to solve most of the daily problems. The field of application of the WSN ranges from basic tasks of monitoring single events to complex intelligent systems for monitoring large cities or industrial processes. IEEE 802.11 standard has been historically used to deploy this type of network. However, the intrinsic problems of this technology are relegating it to a background level in monitoring issues. In recent years, a new type of medium and long-range network has been developed for the Internet of Things: LPWAN networks (Low Power Wide Area Network) which were created to anticipate the planned obsolescence of GPRS networks. LPWAN networks are consolidating as the best solution to implement low-cost applications and to improve our daily lives. This keynote speech aims to present the main LPWAN technologies we can currently use for developing new applications that help us in our daily tasks. Special attention will be paid to the operating characteristics of LoRa/LoRaWAN that make this technology so attractive in the development of systems and applications for home and hospital environments, for monitoring in rural, urban or domestic areas, solutions for monitoring large cities or industrial processes, including very sensitive areas such as healthcare and the welfare of people. Finally, this keynote will discuss the challenges and open research issues in the area and LWPAN technology.



 

 

Efficient Deep Learning Methods for IoT Applications: Current Challenges and Future Directions

Muhammad Sajjad
NTNU
Norway
 

Brief Bio

Muhammad Sajjad received the Master’s degree from the Department of Computer Science, College of Signals, National University of Sciences and Technology, Rawalpindi, Pakistan in 2012, and the Ph.D. degree in digital contents from Sejong University, Seoul, South Korea in 2015. He is currently working as an ERCIM Research Fellow at NTNU, Norway. He is an Associate Professor with the Department of Computer Science, Islamia College University Peshawar, Pakistan. He is also the Head of the Digital Image Processing Laboratory with Islamia College University Peshawar, where many students are involved in different research projects under his supervision, such as Big data analytics, medical image analysis, multi-modal data mining, summarization, image/video prioritization and ranking, fog computing, the Internet of Things, autonomous navigation, and video analytics. His primary research interests include computer vision, image understanding, pattern recognition, robotic vision, and multimedia applications, with a current emphasis on economical hardware and deep learning, video scene understanding, activity analysis, fog computing, the Internet of Things, and real-time tracking. He has published more than 65 papers in peer-reviewed international journals and conferences. He is serving as a professional reviewer for various well-reputed journals and conferences. Currently, he is the associate editor at IEEE Access and acting as a guest editor at IEEE Transactions on Intelligent Transportation Systems.


Abstract
Recent innovations and progress in technologies like wireless multimedia surveillance networks, embedded systems, cloud computing, medical diagnostics, blockchain, and Big data analytics have created numerous opportunities for IoT based applications. An enormous number of sensors deployed in IoT infrastructures like smart cities, smart homes, intelligent transportation, healthcare, and precision agriculture are a major source of highly precious big data. Lack of intelligent mechanisms for storage, indexing, retrieval, and management of sensors big data hinders efficient processing to gather actionable intelligence. Consequently, revealing concealed information and gathering actionable intelligence from IoT data is becoming increasingly sophisticated and infeasible to be accomplished with conventional Machine Learning (ML) paradigms. To this end, Deep learning (DL) can play a crucial role in the effective utilization of IoT devices, producing actionable intelligence for correct and timely decisions. Researchers around the world are motivated by the performance of DL based smart systems including computer vision, machine translation, information retrieval, fault detection, and speech recognition, etc. Due to the limited resources of IoT devices, optimization of DL models, and inferencing are investigated for resource-constrained devices in order to improve their intelligence and efficiency. Furthermore, the heterogeneous nature of underlying hardware in IoT infrastructures raise compatibility issues regarding processing and fusion of information. Hardware/software heterogeneity, data collection, data preprocessing, data generation of IoT in a massive scale, and resource-constrained nature of IoT devices are the current egoistic trends for the researchers to be investigated. In this talk, I will discuss the aforementioned aspects and will envision merging deep learning with IoT to explore new horizons for applications such as health monitoring, disease analysis, indoor localization, intelligent control, home robotics, traffic prediction, traffic monitoring, autonomous driving, and manufacture inspection, which are a few of the futuristic goals for the researchers today.



 

 

Fusing Intelligence into Big Data Transfer in High-performance Networks

Chase Wu
New Jersey Institute of Technology
United States
 

Brief Bio
Chase Wu is currently a Professor and the Associate Chair of the Department of Computer Science and the Director of the Center for Big Data at New Jersey Institute of Technology. His research interests include big data, machine learning, high-performance networking, parallel and distributed computing, sensor networks, scientific visualization, and cybersecurity. His research in networking develops fast and reliable data transfer solutions to help users in a wide spectrum of scientific domains move big data over long distances for collaborative data analysis. His research in computing develops high-performance, intelligent workflow solutions to manage and optimize the execution of big data computing applications in heterogeneous network environments. He has published over 270 research articles in highly reputed conference proceedings, journals, and books, and won best paper awards at many conferences.


Abstract
High-performance Networks (HPNs) have been increasingly used for big data transfer in support of large-scale scientific and business applications. The main challenge is to meet user data transfer requirements while minimizing the waste of expensive bandwidth resources. This talk presents an exploratory analysis of data transfer performance based on several years of real-life performance data in emulated and production HPNs, and the development of a machine learning-assisted performance predictor to facilitate bandwidth reservation that matches actual user needs and avoids bandwidth over-provisioning.



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