Keynote Speakers
ISCTIS 2023 Keynote Speakers
Prof. Guangjie Han（Dean）
IEEE Fellow, IET/IEE Fellow、AAIA Fellow, Hohai University, China
Profile
Guangjie Han (Fellow, IEEE) is currently a Professor with the Department of Internet of Things Engineering, Hohai University, Changzhou, China. He received his Ph.D. degree from Northeastern University, Shenyang, China, in 2004. In February 2008, he finished his work as a Postdoctoral Researcher with the Department of Computer Science, Chonnam National University, Gwangju, Korea. From October 2010 to October 2011, he was a Visiting Research Scholar with Osaka University, Suita, Japan. From January 2017 to February 2017, he was a Visiting Professor with City University of Hong Kong, China. From July 2017 to July 2020, he was a Distinguished Professor with Dalian University of Technology, China. His current research interests include Internet of Things, Industrial Internet, Machine Learning and Artificial Intelligence, Mobile Computing, Security and Privacy. Dr. Han has over 500 peer-reviewed journal and conference papers, in addition to 160 granted and pending patents. Currently, his H-index is 60 and i10-index is 257 in Google Citation (Google Scholar). The total citation count of his papers raises above 13600+ times..Dr. Han is a Fellow of the UK Institution of Engineering and Technology (FIET). He has served on the Editorial Boards of up to 10 international journals, including the IEEE TII, IEEE TCCN, IEEE Systems, IEEE/CCA JAS, IEEE Network, etc. He has guest-edited several special issues in IEEE Journals and Magazines, including the IEEE JSAC, IEEE Communications, IEEE Wireless Communications, Computer Networks, etc. Dr. Han has also served as chair of organizing and technical committees in many international conferences. He has been awarded 2020 IEEE Systems Journal Annual Best Paper Award and the 2017-2019 IEEE ACCESS Outstanding Associate Editor Award. He is a Fellow of IEEE.
Title
Multi-Dimensional Dynamic Trust Management Mechanism in Underwater Acoustic Sensor Networks
Abstract
The underwater acoustic sensor network (UASN) is the core module to realize the "smart ocean". At present, the UASN has not yet fully played its role in the complex water environment. The fundamental reason lies in the lack of effective methods to ensure network security and reliable data transmission. This report mainly introduces the team's research work on the trust management mechanism of UASNs. The main research contents include: 1) Intrusion detection algorithm based on energy consumption prediction model; 2) Multi-dimensional trust calculation algorithm based on fuzzy theory; 3) Trust evaluation algorithm based on cloud theory; 4) Trust cloud migration mechanism based on AUV; 5) Trust update mechanism based on reinforcement learning; 6) Anomaly-resilient trust modelbased on isolation forest. The research results have important theoretical value and practical significance for exploring the security technology and application of UASNs.
Prof. Tianrui Li, Southwest Jiaotong University, China（Dean）
Profile
Dr Tianrui Li is a Professor and the Director of the Key Lab of Cloud Computing and Intelligent Technique of Sichuan Province, Southwest Jiaotong University, China. Since 2000, he has co-edited 8 books, 12 special issues of international journals, received 28 Chinese invention patents and published over 420 research papers (e.g., AI, IEEE TPAMI, IEEE TKDE, IEEE TEC, IEEE TFS, IEEE TIFS, IEEE ASLP, IEEE TIE, IEEE TC, IEEE TVT) in refereed journals and conferences (e.g., ACL, ICDE, ICML, IJCAI, KDD, UbiComp, WWW, ICDM, CIKM, EMNLP). 5 papers were ESI Hot Papers and 18 papers was ESI Highly Cited Papers. He serves as Editor-in-Chief of Human-Centric Intelligent Systems, area editor of International Journal of Computational Intelligence Systems, editors of Information Fusion and WIREs Data Mining and Knowledge Discovery, Associate Editor of ACM Transactions on Intelligent Systems and Technology, etc. He is an IRSS Fellow and Steering Committee Chair (2023-2024), IEEE CIS Emergent Technologies Technical Committee (ETTC) member (2019-2020), IEEE CIS Senior Members Committee member (2018-2020), a senior member of ACM and IEEE, ACM SIGKDD member, Chair of IEEE CIS Chengdu Chapter (2013-2018) and Treasurer (2016-2021) and Secretary (2022-) of ACM SIGKDD China Chapter.
Title
Application Cases of Intelligent Techniques for Sustainable Cities
Abstract
With the rapid development of urbanization, sustainable urban management has entered the era of big data. Massive data has been accumulated around urban management related fields. How to effectively obtain useful knowledge from these big data by deep mining and intelligent learning techniques has become the key problem to be solved in the current sustainable urban development. This report focuses on intelligent application cases such as urban taxi route recommendation, rental recommendation, ambulance deployment, food delivery optimization, subway stop time scheduling, and finally gives the challenges of sustainable urban big data analysis.
Prof. Liangyin Chen, Sichuan University, China
Profile
Head of the Internet of Things Laboratory of the School of Computer Science of Sichuan University, Head of the Sichuan Civil Aviation Airport Operation and Control engineering Technology Research Center (co constructor), Head of Sichuan Dafang, the Joint Laboratory of New Technology for Flight Operation and Control of Civil Aviation Airport, and Head of the Internet of Things Department. The main research directions are embedded systems and personal Big data, mobile computing, sensor networks, etc. He was earlier engaged in the research on group based mobile ad hoc network neighbor discovery in the world, and the relevant research results were published in CCF's important B-level international conferences ACM SenSys' 11, IEEE SECTION '12, and the international top journals IEEE Transactions on Mobile Computing (TMC, CCF (China Computer Federation) computer network A-level top international journals), including Electronics Letters, IEEE Communication Letters More than 50 papers have been published in journals such as Int'l Journal of Distributed Sensor Networks (IJDSN), National First Level Journals "Software Journal", "Computer Research and Development", among which more than 30 papers are included in SCI/EI. Responsible for the National Natural Science Foundation project "Research on Neighbor Discovery Mechanism in Low Power Mobile Ad hoc Networks" and the provincial science and technology support project "Research on Electronic Transaction Service Support Platform for 3G/4G Operation Services"; Participated in the guide preparation and project research of the key project of the Joint Civil Aviation Fund of the Natural Science Foundation of China (NSFC) "Research on Key Technologies of Operational Situation Awareness of Civil Aviation Airline hub Based on Big data"; Participated in the development and research work of the application demonstration project of the Provincial Department of Science and Technology, titled "Research and Development of Core Software for Intelligent Collaborative Management of Flight Ground Services in the Field of Civil Aviation Airports"; Participated in part of the research work of Professor Tian He's National Science Foundation project "Addressing Research Challenges in Low Duty Cycle Wireless Sensor Networks"; Led multiple horizontal projects. As a co constructor, Chuan Dafang participated in the construction of "Sichuan Civil Aviation Airport Operation and Control engineering Technology Research Center". As the person in charge of Chuan Dafang, I participated in the construction of the Joint Laboratory of Industry, University and Research on New Technologies for Flight Operation and Control at Civil Aviation Airport.
Title
A Survey of Decentralizing Applications via Blockchain:The 5G and Beyond Perspective
Abstract
Trusted third parties (TTPs) are frequently used for serving as an authority to issue and verify transactions in applications. Although the TTP-based paradigm provides customers with convenience, it causes a whole set of inevitable problems such as security threats, privacy vulnerabilities, and censorship. The TTP-based paradigm is not suitable for all modern networks, e.g., 5G and beyond networks, which are been evolving to support ubiquitous, decentralized, and autonomous services. Driven by the vision of blockchain technologies, there has been a paradigm shift in applications, from TTP-based to decentralizedtrust-based. Decentralized applications (DApps) with blockchains promise no trust on authorities, tackling the key challenges of security and privacy problems. A main thrust of blockchain research is to explore frameworks and paradigms for decentralizing applications, fostering a number of new designs ranging from network architectures to business models. Therefore, the report provides a compact and concise survey on the state-ofthe-art research of decentralizing applications with blockchain in the 5G and beyond perspective. The four burning 5G and beyond challenges and five aspects of motivation for decentralizing applications with blockchain are presented. Then, nine fundamental modules of blockchains and the potential influence of these modules on decentralization are also explained in depth. The report also discusses the interrelation between decentralization and some desired blockchain properties. Particularly, the capabilities of blockchain for decentralizing applications through reviewing DApps for 5G and beyond are presented. Three blockchain paradigms and how developers to make right choices for 5G and beyond are clearly distinguished. Finally, learned lessons and open issues in applying blockchain for decentralizing applications are highlighted. Lessons learned and open issues from this report will facilitate the transformation of centralized applications to DApps.
Prof. Tang Liu, Sichuan Normal University, China（Vice Dean）
Profile
Tang Liu is currently a Professor and vice dean of College of Computer Science at Sichuan Normal University where he directs MobIle computiNg anD intelligence Sensing (MINDs) Lab. He received his B.S. degree in computer science from the University of Electronic and Science of China in 2003 and the M.S. and Ph.D. degrees in computer science from Sichuan University in 2009 and 2015, respectively. From 2015 to 2016, he was a Visiting Scholar with the University of Louisiana at Lafayette.His current research interests include Internet of Things, Wireless Networks and Mobile Computing. He has published more than 50 peer-reviewed papers in technical conference proceedings and journals, including INFOCOM, TMC, TON, TOSN, IPDPS, TWC, TVT, etc. He has served as the Reviewer for the following journals: TMC, TOSN, Computer Networks, TSUSC, IEEE IoT J, and so on. He also has served as the TPC member of several conferences, such as HPCC, MSN, BigCom and EBDIT.
Title
Research and Exploration of Efficient Wireless Rechargeable Networks
Abstract
With the maturity of wireless power transfer technology, wireless rechargeable networks consisting of wireless chargers and rechargeable sensors have attracted widespread attention from academia and industry. However, due to the attenuation of radio waves during transmission, charging efficiency has become a key hurdle that stunts the growth of wireless rechargeable networks. In this talk, I will introduce the latest research work conducted by our team in improving wireless charging efficiency over the past year. It mainly includes: (1) we found and made utilize of the neglected back lobe energy of directional chargers, established a directional charging model considering both main and back lobes, and designed a scheduling scheme of the mobile directional charger to minimize the number of dead sensors and maximize the energy usage effectiveness; (2) based on the wave interference, we built a concurrent charging model and designed an omnidirectional chargers placement algorithm and a sensors deployment algorithm, aimed at maximizing the overall charging utility.
Prof. Xinwei Yao, Zhejiang University of Technology, China（Vice Dean）
Profile
Dr. Xin-Wei Yao is a Professor in the School of Computer Science and Technology (School of Software Engineering), and the Vice Dean of the Institute for Frontiers and Interdisciplinary Sciences, the director of the Smart Crowd Sensing and Collaboration (SCC) at Zhejiang University of Technology (ZJUT), Hangzhou, China. He received a B.E. degree in Mechanical Engineering from ZJUT in 2008. From March 2012 to February 2013, he was a visiting researcher at the Department of Computer Science at the Loughborough University, UK. He received the Ph.D. degree in Computer Science and Technology from ZJUT in December 2013. From August 2015 to August 2016, he was a visiting Professor at the Department of Electrical Engineering at the State University of New York at Buffalo, USA.His research interests are in the Artificial Intelligence of Things (AIoT), Smart Crowd Sensing and Collaboration, Wireless Nano-Bio-communication Networks, Generalized Artificial Intelligence, Internet of Things (IoT) and so on.
Title
The Internet of Nano-Things
Abstract
Perceptual systems at the microscopic scale promise brand new solutions for numerous applications in many domains, especially in medical and industry fields. The Internet of Nano-Things(IoNT), a perceptual system at nano scale, due to its great application potential and special characteristics, has become a research hot spot in recent years. Free space path loss, signal absorption, as well as reflections, refractions, and diffractions caused by physical objects within the micro environment are radically different compared with the traditional networks, which requires innovative communication mechanism in nanoscale. In this talk, I will discuss the signal interference and coverage range issues of communication in IoNT in a three-dimensional scenario, and present up-level protocols designed for IoNT. Evaluation via simulations highlights the potential of the new protocols for IoNT system.
Prof. Michele Luglio, University of Rome “Tor Vergata”
Profile
LUGLIO MICHELE is professor of telecommunications at University of Rome “Tor Vergata”, visiting Professor at the Computer Science department of University of California Los Angeles (UCLA), the Italian expert delegate and co-chair of the advisory committee 5JAC of ESA. He works on designing innovative satellite communications systems for multimedia services, both mobile and fixed, and coordinates the laboratory of the Satellite Multimedia Group at University of Rome Tor Vergata. His research is focused on network protocols, resource management, heterogeneous networks and on 5G development with particular regard to satellite systems.
Title
The next G (r)evolution passes through the sky
Subtitle
Advances in research that stress the role of satellite networks to win the challenge of present 5G and future 6G.
Abstract
5G is being to be deployed and commercial service already started. The full exploitation of its capabilities is far to be achieved because so far only the physical layer innovations were actually implemented and in limited bandwidths, while all the network management capabilities shall be enjoyed in the next future. Research efforts have been dedicated to progress in the design and development of innovative functions to properly and fruitfully use the satellite to complement terrestrial component aiming to satisfy the requirements of 5G. Moreover, the adaption of the communication standard has been pursued in 3GPP and ITU context. Also 6G started to be conceived and the start of its standardization process is already scheduled Megaconstellations are being deployed and seem to be the technical solution for a significant use of space to provide connectivity but, on the basis of available information, some considerations about their correlation with 5G and 6G will be shared.
Prof. Noor Zaman Jhanjhi, Taylor’s University, Malaysia
Profile
Dr. Noor Zaman Jhanjhi is currently working as Associate Professor | Programme Director - of Postgraduate Research Programmes [Computing] | Director of the Center for Smart society 5.0 [CSS5] and Cluster Head for the Cybersecurity cluster at the School of Computer Science Faculty of Innovation and Technology, Taylor’s University, Malaysia.
Dr. Dany KAMUHANDA, University of Rwanda, Rwanda
Profile
Dr. Dany KAMUHANDA received a PhD in Computer Application Technology in 2020 at Huazhong University of Science and Technology (China). In 2015, he received a master’s degree in Computer Science and Information Systems at Nelson Mandela Metropolitan University (South Africa). In 2010, he received a bachelor’s degree in Computer Science at Kigali Institute of Education (Rwanda).Currently, he is doing Post-doctorate at the University of Zurich, and he is a lecturer of Computer Science at the University of Rwanda. From 2011 to 2015, he worked at Kavumu College of Education as an Assistant Tutor of Computer Science. His current research interests include data science, machine learning and specifically, community detection in social networks.
Title
NMF-based Local Community Detection in Social Networks
Abstract
Local community detection consists of finding a group of nodes closely related to the seeds, a small set of nodes of interest. Such group of nodes are densely connected or have a high probability of being connected internally than their connections to other clusters in the network. Some local community detection methods focus on finding a single community for each of the seeds. However, a seed member usually belongs to multiple local overlapping communities. We discuss Nonnegative Matrix Factorization (NMF)-based algorithms for multiple local community detection supervised by a single seed.
1) The first method denoted as N-MLC consists of three main steps: (a) local sampling with a breadth-first search (BFS) up to several levels depending on the network density (3 levels by default); (b) using Nonnegative Matrix Factorization (NMF) to learn the structure of the sampled subgraph and generate soft community membership indicators for each vertex of the subgraph.
2) The second method, denoted as S-MLC is an improved version of N-MLC in terms of local sampling and estimation of the number of communities which in turn improves community detection accuracy. Here the Personalized PageRank (PPR) is used for local sampling, and a sparse NMF (SNMF) to estimate and detect communities. Another method of estimating the number of communities based on the sparsity behavior of SNMF decompositions is proposed. It is based on the sparse coding idea which aims to find few representatives of a large population. In a community detection context, each node belongs to a few communities which should be shown by the output of SNMF decomposition. The main task is to find a suitable number of components (number of communities) that can be activated to represent the entire dimension of the adjacency matrix.
Assoc. Prof. Ping Liang, Southwest Minzu University, China
Profile
In 2002, She received a master's degree in software Master of Engineering from the University of York in the United Kingdom, and in 2017, he received a doctor's degree in computer science from Khon Kaen University in Thailand, a deputy senior, and served as a master's supervisor in software engineering and a master's supervisor in computer technology. Research direction: Natural language processing, machine learning, including achievements in natural language clustering, emotion analysis, multimodal data fusion, etc., published 3 SCI papers as the first author, 2 EI journal papers, 1 CSCD retrieval paper, authored 2 Chinese translations, and obtained 1 invention patent; Hosted 3 school level projects. Participate in the research team of neuroscience neural networks.
Title
Deep learning and Medical imaging analysis
Abstract
Medical imaging is an important computer-aided tool for clinical diagnosis. The data from medical imaging account for 90% of clinical data, particularly in the area of neurological disorders. It is necessary to fully tap the significane of medical imaging information in clinical intelligent diagnosis, intelligent decision-making and prognosis, etc. Along with the emerging deep learning, using deep neural network to analyze medical imaging has become the mainstream of current research. Deep learning has applied in every link in the process of medical imaging analysis, from the generation of medical imaging data, the pre-processing of medical imaging, to the classification and prediction of medical imaging. In view of the current problems we face nowadays, the potential for future development is huge.