CCCI 2024 Keynote Speakers

Bio

Prof. Imad Mahgoub is the Tecore Endowed Chair Professor in the College of Engineering and Computer Science, Florida Atlantic University, USA. He is also the Director of Tecore Networks Laboratory at Florida Atlantic University.

Dr. Mahgoub received the Ph.D. degree in computer engineering from the Pennsylvania State University, University Park, the M.S. degree in electrical and computer engineering, and the M.S. degree in applied mathematics, both from North Carolina State University, Raleigh. His research interests include internet of things, smart mobile computing, vehicular networks and intelligent transportation systems, sensor and ad hoc wireless networking, machine learning and big data analytics, cybersecurity, smart health, smart cities, and parallel and distributed systems. His research has been funded by federal government agencies and the industry including NSF, DoD, Tecore Networks, Motorola, IBM, and Xpoint Technologies. He has guided 22 Ph.D. students and 34 M.S. students to completion and has more than 200 publications, including four books.

Dr. Mahgoub is a life senior member of the IEEE and a member of the IEEE Communications, Computer, and Vehicular Technology Societies, and the ACM. He is on the editorial board of the International Journal of Communication Systems, the Journal of Wireless Communications and Mobile Computing, and Electronics journal (Electrical and Autonomous Vehicles Section). He served on the editorial board for the International Journal of Computers and Applications and the Encyclopedia of Wireless and Mobile Communications. He has served as Program Chair for the 20th IEEE HONET 2023, Program Chair for CCCI, from 2020 to 2024, Program Chair for CITS, from 2016 to 2020, and Program Chair for SPECTS in 2015, vice chair, track chair, posters chair, publicity chair, and program committee member for many international conferences and symposia.

Abstract

The Internet of Things (IoT) refers to a self-organizing, pervasive network of objects interconnected through the internet with each object having embedded sensors and software to collect and share data. The number of IoT connected objects is increasing exponentially and is expected to reach 50 billion within the next decade or so, covering and impacting all aspects of our lives including transportation, healthcare, smart homes, industrial, energy, agriculture, structures, and smart cities.

Utilizing artificial intelligence methods can turn IoT data into actionable information enabling informed decisions to be made. This can potentially create countless opportunities in all domains. However, several challenges must be addressed for this potential to be fully realized. These challenges include security and privacy, data management, communication, and interoperability.

In this keynote, we will provide an overview of IoT and its subclasses including wireless sensor networks, vehicular networks, and Internet of Battlefield Things (IoBT). We will describe the current and potential applications of IoT. The keynote will also discuss potential opportunities and the challenges that need to be overcome. We will describe our research effort to address the security and privacy, and communication challenges in IoV and IoBT.

Bio

Tao ZHAND, PhD, Professor, Head of the Department of Automation at Tsinghua University, and Deputy Dean of the School of Information Science and Technology of Tsinghua University, IEEE/IET/AAIA/CAA Fellow, Council Member of the Chinese Artificial Intelligence Society, Council Member of the Chinese Association of Automation, and Council Member of the China Simulation Federation, Associate Editor of IEEE Transactions on Automation Science and Engineering, Associate Editor of IEEE Robotics and Automation Letter, Technical Editor of IEEE/ASME Transactions on Mechatronics. He is the Chair of IEEE China Council Education Committee, Founder and Chair of IEEE Beijing Section Education Society Chapter, Member of IFAC Technical Committee on Robotics (TC4.3), etc.

Professor Zhang mainly engages in the research in the fields of intelligent robots, artificial intelligence technology, and control theory. He has led or participated in more than 30 research projects. He has published over 200 papers, including IEEE TPAMI、TAC、TIP、TII、TIE、etc., and more than 10 academic monographs, translated works, and edited textbooks. He has won the National Higher Education Teaching Achievement Award of China, Natural Science Award of the Ministry of Education of China, Beijing Science and Technology Progress Award, Science and Technology Award of China Aeronautical Society, Natural Science Award of the Chinese Association of Automation, and Electronic Information Science and Technology Award of the Chinese Institute of Electronics, etc.

Abstract

Humanoid robots and embodied intelligence are currently hot topics in technological and economic development. The research on humanoid robots has a history of over 50 years. The concept of embodied intelligence was first proposed in 1950, referring to robots or humanoid beings in virtual environments that can interact with the environment, perceive, and have autonomous planning, decision-making, action, and execution capabilities. Embodied intelligence possesses human like or higher than human abilities such as autonomous perception, cognition, understanding, reasoning, and action. It has a complete human brain structure composed of the "brain," "cerebellum," and "brainstem," as well as a machine body that can achieve action. This report summarizes the development history of humanoid robots, and then introduces the key technologies of humanoid robots from two aspects: "multi-skilled humanoid robot creation methods" and "perception, control and human-computer interaction methods based on artificial intelligence big models", thus pointing out the reasons and significance of the coordinated development of humanoid robots and embodied intelligence. Finally, the broad application prospects of humanoid robots were discussed. This report also introduces the current development status of embodied intelligence from three aspects: the body, brain, and embodied intelligence systems. It discusses key technologies of embodied intelligence from multiple perspectives, including multimodal perception technology, world cognition and understanding technology, intelligent autonomous decision-making technology, and joint planning technology for motion operations. Finally, it elaborates on the various challenges currently faced by embodied intelligence.

Bio

Prof. Sanjay Kumar Dhurandher is presently serving as the Executive Director, at the National Institute of Electronics and Information Technology, Ministry of Electronics and Information Technology, Govt. of India, New Delhi. He is also a Professor at the Department of Information Technology, Netaji Subhas University of Technology (Formerly NSIT, University of Delhi). From 1995 to 2000 he worked as a Scientist/Engineer at the Institute for Plasma Research, Gujarat, India which is under the Department of Atomic Energy, India.

Prof. Dhurandher has published over 280 Research Papers in various International Journals/Conferences/Symposiums. He has also written/edited 10 Books, published by international publishers. He is also the Associate Editor of various International Journals. His research interests include wireless networks, network security, underwater sensor networks, opportunistic networks, and cognitive radio networks.

Prof. Dhurandher has also been actively involved in delivering keynote speeches/invited lectures at various Conferences, Faculty Development Programs, Short-Term Courses held across the world/country. He is also a Senior Member of IEEE and Fellow of IETE.

Abstract

With the evolving digital age, the threats posed by cyber attacks, data breaches, and malware is also increasing manifold. Here, we try to explore innovative strategies to address the growing complexities of cyber security in the digital age and would delve into cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), Blockchain, and Zero Trust Architecture, which are revolutionizing. How do organizations defend themselves against evolving cyber threats. We will also focus on the importance of proactive security measures, adaptive threat detection, and continuous risk assessment. By showcasing real-world case studies and emerging trends, this will aim to equip cyber security professionals with practical insights and strategies for building resilient systems that can effectively counteract modern-day cyber adversaries.

Bio

Dr. Ali Hassan Sodhro, currently working as a Senior Lecturer at Department of Computer Science, Kristianstad University, Kristianstad, Sweden. He worked as a Lecturer in Department of Computer and System Science, Mid-Sweden University, Östersund and visiting lecturer in Gothenburg University, Gothenburg, Sweden (2021). He was Postdoctoral Research Fellow at Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, Lulea, and Linköping University, Linköping, Sweden (2018-2020). Dr. Sodhro is the recipient of one of the most prestigious fellowships; Erasmus Mundus for Postdoctoral Research in University Lumiere Lyon2, Lyon, France (2017-2018).

He obtained his PhD in Computer Applications Technology from Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (SIAT, CAS), Shenzhen, China, and University of Chinese Academy of Sciences (UCAS), Beijing China (2016).

Dr. Sodhro is featured in the list of world’s top 2% Global Scientists published by Stanford University for four consecutive years (2023, 2022, 2021, and 2020). He is serving as an Associate Editor of highly reputable journals i.e., IEEE Transactions on Intelligent Transportation System (T-ITS), IET Communications, IET Electronics Letters, Telecommunications System Journal, and Technical Editor of Computer Communication, Elsevier. He also served as a Guest Editor of several special issues in various top-notch journals such as, IEEE Transactions on Industrial Informatics (TII), Sensors MDPI, Electronics MDPI.

Abstract

Fifth Generation (5G) empowers and revolutionizes the IoT by enhancing and extending the connectivity between portable devices for supporting various applications for instance, healthcare, smart cities, and hospital management. Due to diverse and profound impact of the IoT there are various potential benefits in parallel with the insightful challenges. The heterogenous platform, diverse connectivity, and dynamic features of IoT-5G there are more risks and security vulnerabilities by intruders and attackers. In addition, security particularly authentication is the paramount for implementation, management, and monitoring of the IoT and 5G platforms. An adaptive and reliable authentication framework is vital for IoT-5G devices. Since, due to heterogenous technological trends, and lack of uniform interoperable standard and solution, and restricted computing and networking capacity of IoT devices there is need of dynamic and authenticated data exchange framework. Up to now there are less efforts from research community dedicated to developing an adaptive and intelligent framework with lower-latency fast and continuous authentication to 5G network by adopting the PHY-layer parameters. Our proposed framework follows the multi-authentication behavior with soft notion of trust instead of hard flag of binary sequences. Moreover, we aim to detect the intruders with suspicious behavior at PHY-layer, because it is difficult to prevent the access of such illegitimate entities to higher layers.

Bio

Hui Lu is currently a Professor at School of Electronic and Information Engineering, Beihang University. Her research interests include system modeling and simulation, test and evaluation, intelligent system and application. She has undertaken more than 40 research projects, published more than 100 academic papers and 4 academic books, 7 science and technology awards. She is one of the directors of China Association of Computer Automated Measurement and Control Technology. She is the reviewers of several famous journals of IEEE and other organization. She also is the members of China Simulation Federation, Chines Institute of Electronics.

Abstract

In recent years, intelligent systems with autonomous perception, decision-making, and control ability have emerged in the fields, like aviation and aerospace. At the same time, the safety issue attracted attention from both academic and industrial circles. This talk discusses the robustness performance of intelligent decision model from test view. It outlines the robustness definition, the reason for robustness assessment, the indicator, black-box test method and results from practical intelligent decision model.

Bio

Dawei Shi received the B.Eng. degree in electrical engineering and its automation from the Beijing Institute of Technology, Beijing, China, in 2008, the Ph.D. degree in control systems from the University of Alberta, Edmonton, AB, Canada, in 2014. In December 2014, he was appointed as an Associate Professor with the School of Automation, Beijing Institute of Technology. From February 2017 to July 2018, he was with the Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA, as a Postdoctoral Fellow in bioengineering. Since July 2018, he has been with the School of Automation, Beijing Institute of Technology, where he is currently a professor.

His research interests include the analysis and synthesis of complex sampled-data control systems with applications to biomedical engineering, robotics, and motion systems. Dr. Shi serves as an Associate Editor/Technical Editor for IEEE Transactions on Industrial Electronics, IEEE/ASME Transactions on Mechatronics, IEEE Control Systems Letters, IET Control Theory and Applications and IET Cyber Systems and Robotics. He was a Guest Editor for IEEE/ASME Transactions on Mechatronics and Control Engineering Practice, and was a Member of the Early Career Advisory Board of Control Engineering Practice. He served as an Associate Editor for IFAC World Congress and is a Member of the IEEE Control Systems Society Conference Editorial Board.

Abstract

Advancements in new sensing devices and big data technology have enabled the generation and collection of massive input-output data for control system design. Under this background, it becomes very important to evaluate the validity and effectiveness of the data sampled to be used in modeling and control. In this work, we introduce an event-triggered learning scheme that can evaluate the importance of incoming data samples online and only update model learning when informative data samples are identified. We also show the possibility of applying the learning approach to data-driven predictive control, through tube-based/minmax MPC approaches. An application example for intelligent insulin delivery will be used to illustrate the effectiveness of the proposed approach.