SPC Special Sessions
Invited Talk 1
Building High-fidelity 3D Digital Twins over Wireless Networks
Prof. Fawad Ahmad
Assistant Professor, Rochester Institute of Technology
Abstract
A live digital twin is a 3D representation of a physical object or scene that continuously replicates the object or scene in near real-time. Live digital twins have the potential to improve safety and efficiency in various fields, such as autonomous driving, construction monitoring, and disaster relief operations. However, achieving the required performance and accuracy for live digital twins is currently impossible due to limited wireless bandwidths and on-board compute resources.
This talk will discuss techniques for overcoming these limitations and building digital twins that can be used in cyber-physical systems to enable novel and exciting capabilities in fields such as autonomous driving and 3D modeling.
This talk will discuss techniques for overcoming these limitations and building digital twins that can be used in cyber-physical systems to enable novel and exciting capabilities in fields such as autonomous driving and 3D modeling.
Biography
Fawad Ahmad is an assistant professor in the Computer Science Department at the Rochester Institute of Technology. His research focuses on building mobile systems that enable humans, and machines likes self-driving cars, and drones to perceive, and understand the world better. He received his PhD degree from the University of Southern California in 2022. His work on autonomous driving has appeared at top-tier systems conferences like MobiSys, and NSDI.
Invited Talk 2
Towards 6G Hyper-Connectivity: Rate-Splitting Multiple Access for Coexistence of
GEO and LEO Networks
Prof. Byungju Lee
Assistant Professor, Incheon National University
Abstract
The sixth generation (6G) wireless networks should be hyper-connected, implying that there are no constraints on the data rate, coverage, and computing. In this talk, we first identify the main challenges and highlight key enabling technologies for 6G hyper-connectivity. Then, we provide a coexistence scenario of GEO and LEO networks along with rate-splitting multiple access (RSMA). RSMA not only embraces the existing multiple access techniques such as orthogonal multiple access (OMA), spatial division multiple access (SDMA), and non-orthogonal multiple access (NOMA) but also provides significant performance gains by efficiently mitigating inter-user interference in a broad range of interference regimes. We conclude the talk with some open issues and future research directions for 6G hyper-connectivity.
Biography
Byungju Lee received B.S. and Ph.D. degrees from the School of Information and Communication, Korea University, Seoul, South Korea, in 2008 and 2014, respectively. From 2014 to 2015, he was a Post-Doctoral Fellow with the Department of Electrical and Computer Engineering, at Seoul National University, Seoul, South Korea. From 2015 to 2017, he was a Post-Doctoral Scholar with the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA. From 2017 to 2020, he was a Senior Engineer with Samsung Research, Seoul, South Korea. He is currently an Assistant Professor with Department of Information and Telecommunication Engineering, Incheon National University, Incheon, South Korea. Prior to joining Incheon National University, he was a Faculty Member with Kumoh National Institute of Technology, Gumi, South Korea, from 2020 to 2022. His research interests include the physical layer system design of future wireless communications, such as integrated terrestrial and non-terrestrial networks and machine learning for wireless networks. He was awarded the 2020 Fred W. Ellersick Prize from the IEEE Communications Society co-recipient of the Bronze Prize in Samsung Best Paper Award Contest in 2018 and was announced as a Qualcomm fellowship awardee in 2010.
Invited Talk 3
PointSplit: Towards On-device 3D Object Detection with Heterogeneous Low-power Accelerators
Prof. Hyung Sin Kim
Assistant Professor, Seoul National University
Abstract
Running deep learning models on resource-constrained edge devices has drawn significant attention due to its fast response, privacy preservation, and robust operation regardless of Internet connectivity. While these devices already cope with various intelligent tasks, the latest edge devices that are equipped with multiple types of low-power accelerators (i.e., both mobile GPU and NPU) can bring another opportunity; a task that used to be too heavy for an edge device in the single-accelerator world might become viable in the upcoming heterogeneous-accelerator world. To realize the potential in the context of 3D object detection, we identify several technical challenges and propose PointSplit, a novel 3D object detection framework for multi-accelerator edge devices that addresses the problems. Specifically, our PointSplit design includes (1) 2D semantics-aware biased point sampling, (2) parallelized 3D feature extraction, and (3) role-based group-wise quantization. We implement PointSplit on TensorFlow Lite and evaluate it on a customized hardware platform comprising both mobile GPU and EdgeTPU. Experimental results on representative RGB-D datasets, SUN RGB-D and Scannet V2, demonstrate that PointSplit on a multi-accelerator device is 24.7x faster with similar accuracy compared to the full-precision, 2D-3D fusion-based 3D detector on a GPU-only device.
Biography
Hyung-Sin Kim is an Assistant Professor in Graduate School of Data Science at Seoul National University (SNU). He received the B.S. degree in Electrical Engineering and the M.S. and Ph.D. degrees in Electrical Engineering and Computer Science (EECS) from SNU in 2009, 2011, and 2016, respectively, all with outstanding thesis awards. He was a Postdoctoral Scholar of Computer Science at the University of California at Berkeley as a member of Real-time, Intelligent, Secure, Explainable systems (RISELab) and Building Energy Transportation Systems (BETS) group led by Prof. David E. Culler until August 2019. He was a Software Engineer at Google until February 2020. His research interest includes machine learning, systems and applications for Ambient AI and Internet of Things. He has published 69 academic papers and received Qualcomm Fellowship (2011), National Research Foundation (NRF) Global Ph.D. Fellowship (2011) and NRF Postdoctoral Fellowship (2016), and won three best paper runner-ups at SenSys, DCOSS, and WisNet.
Invited Talk 4
Channel Modeling for Joint Sensing and Communications Using Novel Ray Tracing
Mr. Tarun Chawla
Director of Business Development, Remcom Inc.
Abstract
Wave propagation is a physical phenomenon and modeling the channel for a wireless link is a fundamental requirement for a system. Channel models exist to assist engineers in the prediction of BER, Capacity and Throughput for wireless performance. However, standard models from legacy wireless generations are insufficient to cover the breath of new use cases, frequencies and antenna technologies for massive MIMO, 5G, 6G and beyond. Deterministic channel modeling using novel ray-tracing for digital twins can predict accurate ToF, angular spreads, band dispersion, channel latency and range doppler for 6G sensing, UWB, NTN, RIS, mobility and more.
Special care must be taken when representing this physical reality of waves using sound foundational computational electromagnetics.
Special care must be taken when representing this physical reality of waves using sound foundational computational electromagnetics.
Biography
Tarun Chawla (Member, IEEE) received the B.S. degree in electrical engineering from Pennsylvania State University, in 2008. He joined Remcom, in 2009, and he is currently the Director of Business Development. His research interest includes millimeter-wave channel modeling.
Invited Talk 5
Towards Highly Efficient Interactive Data-intensive Computing
Prof. Seo Jin Park
Assistant Professor, University of Southern California
Abstract
Traditional cluster designs were originally server-centric and have evolved recently to support hardware acceleration and storage disaggregation. In applications that leverage acceleration, the server CPU performs the role of orchestrating computation and data movement. Data-intensive applications that leverage disaggregation can be adversely affected by the increased PCIe and network bandwidth required for disaggregation. One way to cope with the challenge is a specialized cluster design for important data intensive applications, such as analytics, query processing and ML training. This design replaces servers with one or more headless smart NICs. Because smart NICs can be significantly cheaper than servers, the resulting cluster can run these applications without adversely impacting performance, while obtaining cost and energy savings.
Biography
Seo Jin Park is an Assistant Professor in the Computer Science Department at the Univerisity of Southern California. Before joining USC, he spent a year at Google Systems Research Group. He did his postdoc at MIT CSAIL with Mohammad Alizadeh and received a Ph.D. in Computer Science from Stanford in 2019 with John Ousterhout. His research interest has been broadly in distributed systems: bringing consistency for low latency systems, improving the robustness of a blockchain protocol, optimizing consensus protocols, suppressing tail-latencies, and building efficient performance debugging tools.
Invited Talk 6
Scene Understanding beyond the Visible
Dr. Hang Qiu
Assistant Professor, University of California, Riverside
Abstract
Recent years have seen tremendous iterations on autonomous driving technologies, pushing the deployment of self-driving cars closer to its realization. As the experimental deployments scale, more challenging and less frequent corner cases surface to stress-test the reliability of the autonomous driving system. Examples of these corner cases include limited visibility due to occlusion, degraded perception at long range, transient reflection and so on. To address the limited visibility issue, in particular, cooperative perception has been proposed to leverage vehicle-to-everything (V2X) communication to share perception data with nearby vehicles to fill in the invisible area. In this talk, I will present a line of cooperative perception system research from its initial prototyping, scaling up, to its expansion from perception to end-to-end driving behaviors.
Biography
Hang Qiu is an assistant professor of the Department of Electrical and Computer Engineering at the University of California, Riverside. Previously, he was a postdoctoral scholar in the Platform Lab at Stanford University, a software engineer at Waymo LLC. He received his Ph.D. from the Department of Electrical and Computer Engineering at the University of Southern California and his Bachelor's degree from Shanghai Jiao Tong University. His research focus is on networked cyber-physical systems with edge ML. His work draws upon theories and methods from machine learning, wireless networking, computer vision, and robotics to build robust and cooperative intelligence in edge autonomous systems. He is a USC Annenberg Fellow, a Qualcomm Innovation Fellowship Finalist, an Outstanding Winner of COMAP ICM, a recipient of ACM Mobisys Best Paper Runner-up Award, a recipient of MLSys Outstanding Paper Award.
Invited Talk 7
Finding and Fixing Vulnerabilities in Blockchain Networks
Dr. Min Suk Kang
Associate Professor, KAIST
Abstract
Blockchain networks are distributed systems that are supposed to be secure against active attacks. In this talk, I will present our recent work on finding and fixing vulnerabilities in blockchain networks. First, I will present our Bitcoin partitioning attack, called the Erebus attack. The Erebus attack partitions the Bitcoin network without any routing manipulations, making the attack undetectable to control-plane and even to data-plane detectors. I will discuss how we have been collaborating with the Bitcoin Core team to address this attack and introduce a remaining open problem. In the second part of the talk, I will present our recent attack, called the Gethlighting attack, that partitions target Ethereum nodes from their mainnet without directly occupying target's peer connections. This subtle denial-of-service vulnerability enables an adversary to prevent a target Ethereum node from accepting new blocks for hours, causing effective partitioning attacks. I will end the talk with a brief discussion on the future research directions in blockchain network security.
Biography
Min Suk is an Associate Professor, School of Computing at KAIST since September 2023. Prior to joining KAIST in 2020, Min Suk had been an Assistant Professor of Computer Science Department, School of Computing at National University of Singapore since 2016. His research interests lie in the field of network and distributed systems security, blockchain security, and wireless network security. He obtained his PhD degree in Electrical and Computer Engineering from Carnegie Mellon University in 2016 under the supervision of Virgil D. Gligor in CyLab. He received BS and MS degrees in EECS at Korea Advanced Institute of Science and Technology (KAIST) in 2006 and 2008, respectively.
Invited Talk 8
Systems Support for Visual SLAM
Dr. Steve Ko
Associate Professor, Simon Fraser University
Abstract
This talk will discuss our effort on optimizing visual SLAM (Simultaneous Localization and Mapping) so it can comfortably run on a small device such as smartphones and AR/VR headsets. SLAM systems estimate the state and trajectory of a mobile agent (e.g., a human, a robot, or a vehicle) while also building a map of the environment using onboard sensors such as cameras and LiDARs. Visual SLAM systems achieve this by identifying and tracking incoming color or depth images. However, visual SLAM algorithms are known to be resource-intensive and difficult to run on small devices. As a result, the current practice is to not run a full-scale visual SLAM algorithm but to run a partial algorithm with limited functionality in order to reduce the resource demand of a full-scale algorithm. Due to this reason, SLAM systems are not fully realizing their potential for enabling a wider variety of applications. To improve this status quo, we have been investigating optimization techniques to make full-scale on-device visual SLAM feasible. Our goal is to concretely explore optimization techniques and trade-offs in terms of accuracy, resource consumption, and latency.
Biography
Steve Ko is an Associate Professor in the School of Computing Science at Simon Fraser University. His current research interest is improving the reliability and security of mobile systems by developing techniques at the intersection of mobile systems and software engineering. He received a B.S. degree in Mathematics from Yonsei University, an MS in Computer Science and Engineering from Seoul National University, and a PhD in Computer Science from the University of Illinois at Urbana-Champaign. He worked as a postdoc in the Department of Computer Science at Princeton University. Until 2020, He was with the Department of Computer Science and Engineering at the University at Buffalo, The State University of New York as an Associate Professor.
Invited Talk 9
MixMax: Leveraging Heterogeneous Batteries to Alleviate Low Battery Experience for
Mobile Users
Prof. Jinkyu Lee
Associate Professor, Sungkyunkwan University
Abstract
Despite the physical advance of an existing single-cell battery system, mobile users are still suffering from low battery anxiety. With a careful analysis of users’ battery usage behavior collected for 19,855 hours, we propose a heterogeneous battery system, MixMax, consisting of three complementary battery types tailored to minimizing the low battery time. While composing a heterogeneous battery system opens up a chance to simultaneously improve the capacity and the charging speed, one must face non-trivial challenges to determine the ratio of enclosed batteries and charge/discharge policies during the run-time. They are highly dependent on each other, which entails almost infinite candidates for the choice. MixMax gracefully unwinds the dependencies as it formulates the decision-making problem into an optimization problem and decomposes it into multiple sub-problems instead. To evaluate MixMax, we fabricate coin-cell batteries and experiment with them to model an accurate battery emulator which sophisticatedly reproduces the dynamics of battery systems. Our experimental results demonstrate that MixMax can reduce the low battery time by up to 24.6% without compromising capacity, volume, weight, and more importantly, users’ battery usage behavior. In addition, we prototype MixMax on a smartphone, presenting the practicality of MixMax on mobile systems.
Biography
Jinkyu Lee received the BS, MS, and PhD degrees in computer science from the Korea Advanced Institute of Science and Technology, Republic of Korea, in 2004, 2006, and 2011, respectively. He is currently an associate professor with Department of Computer Science and Engineering, Sungkyunkwan University, Republic of Korea, where he joined in 2014. From 2011 to 2014, he was a research fellow with the Department of Electrical Engineering and Computer Science, University of Michigan, USA. His research interests include system design and analysis with timing guarantees, QoS support, and resource management in real-time embedded systems, mobile systems, and cyber-physical systems. He was the recipient of Best Student Paper Award from 17th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) in 2011 and Best Paper Award from 33rd IEEE Real-Time Systems Symposium(RTSS) in 2012
Invited Talk 10
Performance Estimation after Personalization for Heterogeneous Mobile AI Applications
Dr. Taesik Gong
Research Scientist, Nokia Bell Labs
Abstract
Many applications utilize sensors in mobile devices and machine learning to provide novel services. However, various factors such as different users, devices, and environments impact the performance of such applications, thus making the domain shift (i.e., distributional shift between the training domain and the target domain) a critical issue in mobile sensing. Despite attempts in domain adaptation to solve this challenging problem, their performance is unreliable due to the complex interplay among diverse factors. In principle, the performance uncertainty can be identified and redeemed by performance validation with ground-truth labels. However, it is infeasible for every user to collect high-quality, sufficient labeled data. To address the issue, we present DAPPER (Domain AdaPtation Performance EstimatoR) that estimates the adaptation performance in a target domain with only unlabeled target data. Our key idea is to approximate the model performance based on the mutual information between the model inputs and corresponding outputs. Our evaluation with four real-world sensing datasets compared against six baselines shows that on average, DAPPER outperforms the state-of-the-art baseline by 39.8% in estimation accuracy. Moreover, our on-device experiment shows that DAPPER achieves up to 396x less computation overhead compared with the baselines.
Biography
Dr. Taesik Gong is a Research Scientist at Nokia Bell Labs, Cambridge, UK. His research focuses on algorithmic & systematic research for efficient on-device machine learning. He received his Ph.D. and M.S. in Computer Science from KAIST under the supervision of Prof. Sung-Ju Lee. He received his B.S. in Computer Science from Yonsei University with Summa Cum Laude. His research interests are at the intersection of machine learning and mobile computing. He has published his work in prestigious machine and mobile computing venues, such as NeurIPS, SenSys, and UbiComp. During his Ph.D., he did research internships at Nokia Bell Labs, Google Research, and Microsoft Research. He has received several awards, including the Google Ph.D. Fellowship in Machine Learning, NAVER Ph.D. Fellowship, and Best Ph.D. Dissertation Award from both the School of Computing and the College of Engineering at KAIST.
Invited Talk 11
Software Analysis Practice for ICT Intellectual Property Litigation
Dr. Jae Young Bang
Director of Software Development, Quandary Peak Research
Abstract
I will discuss the roles and responsibilities of a software expert witness in high-stakes ICT intellectual property lawsuits in North America regarding patent infringement, copyright infringement, class action, etc. I will share my real-world experience working with trial attorneys and analyzing technical evidence in lawsuits from software patents, catastrophic software failures, theft of trade secrets, and other matters.
Biography
Dr. Jae Young Bang is the director of software development and a senior computer scientist at Quandary Peak Research with over a decade of experience in software engineering. His background ranges from academic research in software architecture to leading‐edge software development practice.
Dr. Bang currently serves as a testifying expert in software-related litigation, including patent/copyright infringement and breach-of-contract matters. His role includes researching software architecture design and analysis. He also teaches Software Design at the University of California, Irvine.
Dr. Bang currently serves as a testifying expert in software-related litigation, including patent/copyright infringement and breach-of-contract matters. His role includes researching software architecture design and analysis. He also teaches Software Design at the University of California, Irvine.
Invited Talk 12
Research on Automating Security Validation for RMF Standard Implementation
Prof. Dohoon Kim
Assistant Professor, Kyonggi University
Abstract
This study primarily focuses on introducing the Risk Management Framework (RMF), currently being implemented in the integrated security management system of the US Department of Defense (DoD), and creating a K-RMF that is tailored to the domestic context. In particular, throughout this process, various cyber threat modeling techniques are utilized, based on the MITRE framework's Tactics, Techniques, and Procedures (TTPs), to develop ATTACK TREE and DEFEND TREE methodologies.
The objective of this research is to provide an overview of the process of conducting proactive cyber-based risk assessment by leveraging various open-source tools and frameworks, specifically focusing on automating it using the OPEN RMF approach. By adopting the OPEN RMF, the study aims to develop a comprehensive understanding of the risk assessment process in the context of cybersecurity. This involves utilizing open-source resources to enhance the efficiency and effectiveness of risk assessment activities, enabling a more dynamic and adaptive approach to managing cyber risks.
The objective of this research is to provide an overview of the process of conducting proactive cyber-based risk assessment by leveraging various open-source tools and frameworks, specifically focusing on automating it using the OPEN RMF approach. By adopting the OPEN RMF, the study aims to develop a comprehensive understanding of the risk assessment process in the context of cybersecurity. This involves utilizing open-source resources to enhance the efficiency and effectiveness of risk assessment activities, enabling a more dynamic and adaptive approach to managing cyber risks.
Biography
2018 ~ : KYONGGI UNIVERSITY, SUWON, KOREA / Assistant Professor
2012 – 2018: AGENCY FOR DEFENSE DEVELOPMENT DAEJEON, KOREA / Senior Researcher at Cyber & Information Security Division
Research Topic: Malware & Big-Data Analysis, CERT, Reversing, Digital Forensic.
Ongoing Project
1) 2023: Analysis of space cybersecurity policies (PM)
2) 2022 – 2023: (PM) Future challenging defense technology - Development of cyber warfare automatic deception technology Defense Acquisition Program Administration
3) 2023 ~ 2025: (PM) A Study on the Hybrid M&S Based Permissioned Blockchain Resilience for Digital Twin Transformation / NRF
DOHOON KIM received the B.S. degree in mathematics and computer science from Korea University, in 2005, and the M.S degree in information security and computer science from Korea University, in 2007, and the Ph.D. degree in information security and computer science (with specialization in cybersecurity and network security) from Korea University, in 2012. From 2012 to 2018, he was a Senior Research Engineer with the Agency for Defense Development (ADD), Daejeon-si, South Korea.
He is currently a Assistant Professor with the Department of Computer Science, Kyonggi University, Suwon, South Korea, since 2018. His areas of research include cybersecurity, botnet, risk analysis, cyber deception,and moving target defense (MTD).
2012 – 2018: AGENCY FOR DEFENSE DEVELOPMENT DAEJEON, KOREA / Senior Researcher at Cyber & Information Security Division
Research Topic: Malware & Big-Data Analysis, CERT, Reversing, Digital Forensic.
Ongoing Project
1) 2023: Analysis of space cybersecurity policies (PM)
2) 2022 – 2023: (PM) Future challenging defense technology - Development of cyber warfare automatic deception technology Defense Acquisition Program Administration
3) 2023 ~ 2025: (PM) A Study on the Hybrid M&S Based Permissioned Blockchain Resilience for Digital Twin Transformation / NRF
DOHOON KIM received the B.S. degree in mathematics and computer science from Korea University, in 2005, and the M.S degree in information security and computer science from Korea University, in 2007, and the Ph.D. degree in information security and computer science (with specialization in cybersecurity and network security) from Korea University, in 2012. From 2012 to 2018, he was a Senior Research Engineer with the Agency for Defense Development (ADD), Daejeon-si, South Korea.
He is currently a Assistant Professor with the Department of Computer Science, Kyonggi University, Suwon, South Korea, since 2018. His areas of research include cybersecurity, botnet, risk analysis, cyber deception,and moving target defense (MTD).
Invited Talk 13
Homomorphic computation on ciphertexts
Dr. Changmin Lee
Assistant Professor, Korea Institute for Advanced Study
Abstract
After the advent of Iot, cloud computing, and big data, outsourcing computation to untrusted servers without sacrificing privacy of sensitive data has received a lot of attention.
As the solutions, two frame works are suggested: Homomorphic encryption and Functional encryption. Both schemes enable to compute on ciphertexts while privacy preserving. In this talk, I will present basic notions of two schemes including definitions, algorithm, underlying security and its related issues in mathematician's view.
As the solutions, two frame works are suggested: Homomorphic encryption and Functional encryption. Both schemes enable to compute on ciphertexts while privacy preserving. In this talk, I will present basic notions of two schemes including definitions, algorithm, underlying security and its related issues in mathematician's view.
Biography
2012~2017& Ph.D. in Mathematical Science, Seoul National University, Korea.
Advisor: Prof. Jung Hee Cheon
2017.09~2018.09& Postdoctoral researcher, The Research Institute of Basic Sciences, Seoul National University, Korea.
2018.10.~2020.09& Labex Milyon Postdoctoral researcher, ENS de Lyon, France
Advisor: Prof. Damien Stehle
2020.10.~Now& KIAS Fellow (KIAS Assistant Professor), KIAS, Korea
Advisor: Prof. Jung Hee Cheon
2017.09~2018.09& Postdoctoral researcher, The Research Institute of Basic Sciences, Seoul National University, Korea.
2018.10.~2020.09& Labex Milyon Postdoctoral researcher, ENS de Lyon, France
Advisor: Prof. Damien Stehle
2020.10.~Now& KIAS Fellow (KIAS Assistant Professor), KIAS, Korea
Invited Talk 14
Towards Trustworthy Decentralized Web 3.0
Dr. Gyu Myoung Lee
Professor, Liverpool John Moores University
Abstract
IoT and data are becoming essential to support AI-based solutions with the immersiveness of spatial computing. Blockchain, as a machine for creating trust, is revolutionizing the way transactions. In this context, this talk introduces key concepts, features and characteristics of the new Internet, the so-called Web 3.0, and its vision as the Internet of Value, connecting people, machines and AI to transform the world. Starting from the new economic paradigm for cyberspaces, the data ecosystem and its characteristics, this talk presents key challenges for realizing the decentralized platform with trust technology and discusses next steps for future research.
Biography
Prof. Gyu Myoung Lee is with the Liverpool John Moores University (LJMU), UK, as a Professor and with KAIST Institute for IT convergence, Korea, as an Adjunct Professor. Prior to joining the LJMU, he has worked with the Institut Mines-Telecom, Telecom SudParis, France, from 2008. Until 2012, he had been invited to work with the Electronics and Telecommunications Research Institute (ETRI), Korea. He also worked as a research professor in KAIST, Korea and as a guest researcher in National Institute of Standards and Technology (NIST), USA, in 2007. His research interests include Internet of things, data analytics, computational trust, knowledge centric networking and services, multimedia services, and energy saving technologies including smart grids. He has been actively involved in standardization in ITU-T, IETF and oneM2M, etc., and currently serves as a WP chair in SG13, a vice-chair of Focus Group on Autonomous Networks (FG-AN), a Rapporteur of Q16/13 and Q4/20 as well as an Editor in ITU-T. He was also the chair of ITU-T Focus Group on data processing and management (FG-DPM) to support IoT and smart cities & communities. He was awarded the Vice-Chancellor's Award for excellence in research in 2017 and was also awarded the Best Paper Awards in ICIN'2017, WF-IoT'2014, etc. He is a Senior Member of IEEE.
Invited Talk 15
Trust Management for Web3.0
Dr. Tai-Won Um
Associate Professor, Chonnam National University
Abstract
This presentation introduces trust management technologies for Web 3.0. It analyzes the trust requirements to support a reliable Web 3.0 economy, including data sharing and digital asset transactions, and explores the limitations of existing technologies such as blockchain, NFTs, metaverses, and AI, and how trust can be embedded in Web 3.0.
Biography
- 2022.03 ~ Present: Associate Professor, Graduate School of Data Science, Chonnam National University
- 2020.03 ~ 2022.02: Assistant Professor, College of Science and Technology, Duksung Women's University
- 2017.09 ~ 2020.03: Associate Professor, Department of Information and Communication Engineering, Chosun University
- 2006.02 ~ 2017.08: Principal Researcher, Electronics and Telecommunications Research Institute(ETRI)
- 2020.03 ~ 2022.02: Assistant Professor, College of Science and Technology, Duksung Women's University
- 2017.09 ~ 2020.03: Associate Professor, Department of Information and Communication Engineering, Chosun University
- 2006.02 ~ 2017.08: Principal Researcher, Electronics and Telecommunications Research Institute(ETRI)
Invited Talk 16
DAO(Decentralized Autonomous Organizaiton): Organization of Web3
Mr. Hang Jin Kim
Director, MckinleyRice
Abstract
As the most important part to be improved in the transition from Web2 to Web3, how to improve the severity of the centralization problem of platform companies that have secured a monopoly position, and as an alternative to it, DAO as a blockchain-based decentralized organizational structure Consider examples of how they can be used and what problems to solve.
Biography
Focused Research Area : Blockchain , Web3 , Blockchain AI conversence , Blockain SmartCity techs conversence
Work Profie ( Lastest 5 year in Total 28 year )
2021 ~ MckinleyRice Co.,Ltd CSO , Blockchain X
2019~2021 *CityLabs , Chief Dricector of SmartCity Biz Unit
2017~2019 ** Iconloof , Director , Government Relation
* Listed company : Kosdaq ** Listed company in Crypto : Binance
Work Profie ( Lastest 5 year in Total 28 year )
2021 ~ MckinleyRice Co.,Ltd CSO , Blockchain X
2019~2021 *CityLabs , Chief Dricector of SmartCity Biz Unit
2017~2019 ** Iconloof , Director , Government Relation
* Listed company : Kosdaq ** Listed company in Crypto : Binance
Invited Talk 17
Heterogeneous CSMA for Improved Energy Fairness in LoRaWAN
Prof. Chenglong Shao
Assistant Professor, Kyushu Institute of Technology
Abstract
TIn this talk, I present a heterogeneous carrier-sense multiple access (CSMA) protocol named LoHEC to improve energy fairness during channel access in CSMA-based long-range wide area network (LoRaWAN). LoHEC is enabled by Channel Activity Detection(CAD), a recently introduced carrier-sensing technique for detecting LoRaWAN signals even below the noise floor. The design of LoHEC is inspired by the fact that existing CAD-based CSMA protocols operate in a homogeneous manner by having LoRaWAN end devices perform identical CAD regardless of the differences in their used network parameter - spreading factor (SF). This can lead to an energy consumption unbalance among end devices since the consumed energy during CAD is significantly affected by SF. By considering the heterogeneity of LoRaWAN in terms of SF, LoHEC requires end devices to perform different numbers of CADs with different CAD intervals during channel access. The needed CADs and CAD interval are determined based on the CAD energy consumption under different SFs. Experimental results show that LoHEC can significantly improve the energy fairness compared with the existing solutions.
Biography
Chenglong Shao is an Assistant Professor in the Department of Computer Science and Networks at Kyushu Institute of Technology, Iizuka, Japan. He received his B.S. degree in information and communications engineering at Xi'an Jiaotong University, Xi'an, China, in 2010. He received his Ph.D. degree in computer science and engineering at Korea University, Seoul, Republic of Korea, in 2019. He worked as a Research Professor at Korea University in 2019 and a JSPS International Research Fellow at Kyushu University from 2021 to 2023. His research interests include wireless communications and networking, mobile computing for the Internet of Things, wireless security, and networked embedded systems.
Invited Talk 18
Network Switches as Domain-Specific Hardware for Distributed Storage
Prof. Gyuyeong Kim
Assistant Professor, Sungshin Women's University
Abstract
In-network computing is an emerging paradigm that offloads server functionality into the network switch by leveraging the capability of programmable switches for high throughput and low latency. In this talk, I show that network switches can be domain-specific hardware for distributed storage supported by key-value stores. First, I present NetLR, an in-network replication coordinator, which can perform data replication in the switch directly to achieve high performance and strong consistency. Next, I introduce NetStore, a holistic in-network storage accelerator that mitigates performance overhead caused by large requests by co-designing the switch control plane and the switch data plane. Lastly, I present NetClone, an in-network dynamic request cloning mechanism to mask the service-time variability of modern servers. By dynamically cloning requests and blocking slower responses in the switch, NetClone achieves high performance for microsecond-scale workloads.
Biography
Gyuyeong Kim is an assistant professor at the Department of Computer Engineering, Sungshin Women’s University, Seoul, South Korea. He received his B.S. and Ph.D. degrees in computer science from Korea University in 2012 and 2020, respectively.
His research interests include networked systems, in-network computing, and network stack.
His research interests include networked systems, in-network computing, and network stack.
Invited Talk 19
Learning to Optimize MIMO Networks
Prof. Hoon Lee
Associate Professor, UNIST
Abstract
MIMO techniques have been regarded as promising solutions for enhancing spectral efficiency of the future wireless communication networks. The optimization of MIMO networks involves a joint design of multi-antenna signal processing, channel estimation and acquisition processes. This requests computationally expensive nonconvex optimization algorithms with suboptimal performance, especially in large-scale MIMO networks. To tackle these challenges, there have been intensive studies on machine learning approaches for MIMO communication networks. This talk presents an overview of recent machine learning-based MIMO system designs. Technical challenges of existing algorithms and open research opportunities are discussed.
Biography
Hoon Lee received the B.S. and Ph.D. degrees from Korea University, Seoul, Korea, in 2012 and 2017, respectively. Since 2019, he has been with the Department of Information and Communications Engineering, Pukyong National University, Busan, Korea. His research interests include machine learning, signal processing, and optimization for wireless communications.