Prof. Nei Kato
Dean with Graduate School of Information Sciences, Tohoku University, Japan
Title: Multi-AP Coordination Approaches over Emerging WLANs: Future Directions and Open Challenges
Bio:
Nei Kato is a full professor and the Dean with Graduate School of Information Sciences, Tohoku University. He has researched on computer networking, wireless mobile communications, satellite communications, ad hoc & sensor & mesh networks, UAV networks, AI, IoT, and Big Data. He is the Editor-in-Chief of IEEE Internet of Things Journal, the fellow committee chair of IEEE VTS. He is a Fellow of the Engineering Academy of Japan, a Fellow of IEEE, and a Fellow of IEICE.
Abstract:
The 802.11 IEEE standard aims to update current Wireless Local Area Network (WLAN) standards to meet the high demands of future applications, such as 8K videos, augmented/virtual reality (AR/VR), the Internet of Things, telesurgery, and more. Two of the latest developments in WLAN technologies are IEEE 802.11be and 802.11ay, also known
as Wi-Fi 7 and WiGig, respectively. These standards aim to provide Extremely High Throughput (EHT) and lower latencies. IEEE 802.11be includes new features such as 320 MHz bandwidth, multi-link operation, Multi-user Multi-Input Multi-Output (MIMO), orthogonal frequency-division multiple access, and Multiple-Access Point (multi-AP) cooperation (MAP-Co) to achieve EHT. With the increase in the number of overlapping Access Points (APs) and inter-AP interference, researchers have focused on studying MAP-Co approaches for coordinated transmission in IEEE 802.11be, making MAP-Co a key feature of future WLANs. Additionally, the high overlapping AP densities in EHF bands, due to their smaller coverage, must be addressed in future standards beyond IEEE 802.11ay, specifically with respect to the challenges of implementing MAP-Co over 60GHz bands. In this talk, the state-of-the-art in MAP-Co features and their drawbacks concerning emerging WLAN, several novel future directions and open challenges will be provided.
Prof. Yan Zhang
University of Oslo, Norway
Title: Ubiquitous Computing Power Networks
Bio:
Yan Zhang is currently a Full Professor with the Department of Informatics, University of Oslo, Norway. His research interests include next-generation wireless networks leading to 6G, green and secure cyber-physical systems. Dr. Zhang is an Editor for several IEEE transactions/magazine. Since 2018, Prof. Zhang has been listed as a Highly Cited Researcher by Clarivate Analytics (i.e., Web of Science). He is Fellow of IEEE, Fellow of IET, elected member of Academia Europaea (MAE), elected member of the Royal Norwegian Society of Sciences and Letters (DKNVS), and elected member of Norwegian Academy of Technological Sciences (NTVA).
Abstract:
Firstly, we introduce the concept and model of ubiquitous computing power network. Then, new and unique scientific research problems in ubiquitous computing power networks are defined and solved, including the optimal allocation of computing resources, computing power collaboration and clustering mechanism, and distributed computing power sharing. Finally, we point out the future scenarios and open questions of ubiquitous computing power networks.
Prof. Guangjie Han
Hohai University, China
Title: Indoor Localization with Deep Gaussian Process and Uncertainty Maps
Bio:
Guangjie Han is a professor, currently serving as the Dean of the School of IoT Engineering at Hohai University. He is an IEEE Fellow, IET/IEE Fellow, and AAIA Fellow. His main research interests include smart oceans, industrial IoT, artificial intelligence, networks, and security. In recent years, he has published more than 350 high-level SCI journal papers, including over 130 papers in the IEEE/ACM Trans. series, in international journals such as IEEE JSAC, IEEE TMC, IEEE TPDS, and IEEE TCC. His publications have been cited over 17200 times on Google Scholar, with an H-index of 68. He has authored three monographs and translated one book. He has led more than 30 provincial and ministerial-level research projects, including national key R&D programs and national natural science foundation key projects. He has been granted 130 national invention patents and 6 PCT international authorized patents. He has received numerous awards, including the second prize of the China Business Federation Science and Technology Award, the third prize of the Jiangsu Provincial Science and Technology Award, the second prize of the Liaoning Provincial Science and Technology Progress Award, and the Best Paper Award of the IEEE Systems Journal in 2020. For five consecutive years (2019-2023), he has been listed as one of the top 2% of scientists globally, as well as for the Chinese Highly Cited Researchers list for four consecutive years (2020-2023). Currently, he serves as an associate editor for more than ten international journals, including IEEE TII, IEEE TVT, IEEE TCCN, and IEEE Systems. He has been awarded the “333 High-level Talents in Jiangsu Province” (second level), the “Outstanding Contribution Young and Middle-aged Experts in Jiangsu Province,” the “Minjiang Scholar Lecture Professor,” and the “May 1st Labor Medal” of Changzhou City.
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 model based on isolation forest. The research results have important theoretical value and practical significance for exploring the security technology and application of UASNs.
Prof. Joey Tianyi Zhou
A*STAR Centre for Frontier AI Research (CFAR) & the Centre for Advanced Technologies in Online Safety (CATOS), Singapore
Title: Dataset Distillation and Pruning: Streamlining Machine Learning Performance
Bio:
Joey Tianyi Zhou is a Principal Scientist, Deputy Director with A*STAR Centre for Frontier AI Research (CFAR), and he is also a Principal Scientist in the Centre for Advanced Technologies in Online Safety (CATOS), Singapore. Concurrently, is holding an adjunct faculty position at the National University of Singapore (NUS). Before working at CFAR, he was a senior research engineer with SONY US Research Center in San Jose, USA. Dr. Zhou received a Ph.D. degree in computer science from Nanyang Technological University (NTU), Singapore. His current interests mainly focus on improving the efficiency and robustness of machine learning algorithms. In these areas, he has published more than 100 papers and received the Best Student Paper Nomination at the European Conference on Computer Vision (ECCV’16), Best Paper Award at the International Joint Conference on Artificial Intelligence (IJCAI) workshops, and Best Poster Award and runner-up prize at International Conference on Computer Vision (ICCV19) on HANDS workshop and its competition, respectively.
Dr. Zhou regularly organizes workshops/tutorials at top-tier international conferences like CVPR, IJCAI, ICDCS, etc. He is serving as an Associate Editor and Editorial Board for Artificial Intelligence Journal (AIJ), IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) and IEEE Access, IET Image Processing, and Area Chairs in top machine learning conferences like ICLR, ICML, NeurIPS, IJCAI etc.
Abstract:
In the rapidly evolving field of machine learning, “Dataset Distillation and Pruning” has emerged as a key strategy for enhancing model efficiency. Dataset distillation involves extracting essential information from extensive datasets to create refined, smaller-scale data that maintains model robustness while reducing computational burden. It can be likened to distilling knowledge from vast amounts of data.
On the other hand, dataset pruning is akin to pruning unnecessary branches from a tree. This technique involves removing redundant or minimally impactful data points, resulting in a more streamlined, faster, and resource-efficient machine learning model. By eliminating extraneous information, dataset pruning aids in constructing lean algorithms with outstanding performance and without unnecessary computational overhead.
These two approaches collectively address the challenges posed by the abundance of data in the digital age. Dataset distillation and pruning complement each other in model compression research and further optimize the entire machine learning workflow’s energy consumption, ultimately facilitating sustainable deployment of large-scale data and models on endpoints.
Prof. Yun Lin
Harbin Engineering University, China
Title: The security and reliability of Mobile Multimedia Communication
Bio:
Yun Lin (M’14, SM’23) received the B.S.degree from Dalian Maritime University, Dalian, China, in 2003, the M.S. degreefrom the Harbin Institute of Technology, Harbin, China, in 2005, and the Ph.D.degree from Harbin Engineering University, Harbin, China, in 2010. He was aresearch scholar with Wright State University, USA, from 2014 to 2015. Now, heis currently a full professor in the College of Information and CommunicationEngineering, Harbin Engineering University, China. His current researchinterests include machine learning and data analytics over wireless networks,signal processing and analysis, cognitive radio and software defined radio,artificial intelligence and pattern recognition. He is a IET Fellow.
He had published more than 200international peer-reviewed journal/conference papers, such as the IEEE TSP,TII, TCOM, IoT, TVT, TCCN, TR, TITS, INFOCOM, GLOBECOM, ICC, VTC, ICNC. He isserving as an Editors-in-Chief of EAI Endorsed Transactions on MobileCommunications and Applications, editors for the IEEE TRANSACTIONS ONRELIABILITY, IEEE Internet of Things, Digital Communications and Networks,Wireless Network, KSII Transactions on Internet and Information Systems, andInternational Journal of Performability Engineering. He serves as GC2022co-chair of Mobile and Wireless Networking Symposium, General Vice Chair ofVTC-2021 Fall, General Chair of ADHIP 2020, ADHIP 2023 and Mobimedia 2022, TPCChair of MOBIMEDIA 2020, ICEICT 2019 and ADHIP 2017, and TPC member ofGLOBECOM, ICC, ICNC, ICCC, WCSP and VTC. He had successfully organized severalinternational workshops and symposia with top‐ranked IEEE conferences,including INFOCOM, GLOBECOM, DSP, ICNC, among others. He has gotten the bestpaper of ICCC 2023, ICCT 2023, VIS 2022, ICUS 2022, Mobimedia 2022, ADHIP 2021,CSPS 2018. He is a recipient of IEEE Outstanding service award of ICCC 2023,ICCT 2023, PHM 2023, Trustcom 2021, IEEE Outstanding Track Chair Award of MASS2021.
Abstract:
With the rapid deployment of 5GNR, themobile communication network has been greatly developed and expanded, and thesecurity problems hidden in it have become more and more significant. Comparedwith traditional security technology, radio frequency fingerprintidentification technology can provide reliable physical layer securityprotection, and has strong application value in the future 5G, even 6G.Thisreport will introduce the security issues in 5G system, as well as thebackground and development process of RF fingerprint technology, share therelevant achievements of the research group in solving the security problems ofwireless communication physical layer by using RF fingerprint, and look forwardto the future development trend of RF fingerprint technology.
Prof. Qi Xuan
Zhejiang University of Technology, China
Topic: AI Models and Security for Electromagnetic Signal Recognition
Bio:
Qi Xuan received the B.S. and Ph.D. degrees in control theory and engineering from Zhejiang University, Hangzhou, China, in 2003 and 2008, respectively. He was a Postdoctoral Researcher with the Department of Information Science and Electronic Engineering, Zhejiang University, from 2008 to 2010, and a Research Assistant with the Department of Electronic Engineering, City University of Hong Kong, Hong Kong, in 2010 and 2017. From 2012 to 2014, he was a Postdoctoral Fellow with the Department of Computer Science, University of California at Davis, Davis, CA, USA. He is currently a Professor with the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China. His current research interests include network science, graph data mining, deep learning, cyberspace security, machine learning, and computer vision. He has published more than 100 international peer-reviewed journal/conference papers, such as the IEEE TKDE, IEEE TIFS, IEEE TNNLS, IEEE TIE, IEEE TNSE, IEEE TCSS, IEEE TCCN, PRE, ICSE, FSE, WWW.
Abstract:
This report focuses on building an accurate, efficient, robust, and secure trustworthy AI ecosystem for electromagnetic spectrum.In particular, I will introduce the key technologies and application research of the team in the field of electromagnetic signal recognition: including CNN and GNN for signal recognition, multimodal marginal prototype architecture for open set scenarios, multiple adversarial distillation defense training architecture, and deep testing for the robustness of AI models.