Talk Title: Securing Cyber-Physical and IoT Systems in Smart Living Environments

Abstract: Our daily lives are becoming increasingly dependent on smart cyber-physical infrastructures, such as smart homes or cities, smart grid, smart transportation, smart healthcare, smart agriculture, and so on. The wide availability of sensor enabled IoT devices and smartphones are also empowering us with fine-grained data collection and opinion gathering via mobile crowdsensing about events of interest, resulting in actionable inferences and decisions. This synergy has led to cyber-physical-human (CPH) convergence in smart living environments, the goal of which is to improve the quality of life. However, CPH and IoT systems are extremely vulnerable to security threats owing to their interdependence, scale, heterogeneity, human behavior, and trust issues. This talk will highlight unique security challenges in smart living environments, build a unified data falsification threat (anomaly detection) landscape for CPH and IoT systems, and propose novel defense mechanisms for securing such systems. Our solutions are based on a rich set of theoretical and practical design principles including AI/ML, data analytics, sensor fusion, uncertainty reasoning, information theory, prospect theory, and reputation/belief models. Case studies with real-world datasets will be presented  to defend smart grid, smart transportation and smart water distribution networks. The talk will be concluded with future research directions.

Biography: Sajal K. Das is a Curators’ Distinguished Professor of Computer Science and Daniel St. Clair Endowed Chair at Missouri University of Science and Technology, USA where he was the Chair of Computer Science Department during 2013-2017. Previously, he served the National Science Foundation (NSF) as a Program Director in the Computer and Network Systems Division. His interdisciplinary research interests include cyber-physical systems, IoT, drones, cybersecurity, machine learning, data science, wireless and sensor networks, mobile and pervasive computing, smart environments, edge and cloud computing, and applied graph theory. He has contributed significantly to these areas and published extensively (more than 500 apers) in high quality journals and peer-reviewed conference proceedings. A holder of 5 US patents, Dr. Das has directed numerous funded projects over $23 million. He coauthored 59 book chapters and four books. According to Google Scholar, his h-index is 99 with more than 41,200 citations. He is the founding Editor-in-Chief of Elsevier’s Pervasive and Mobile Computing journal and serves as an Associate Editor of the IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Mobile Computing, ACM/IEEE Transactions on Networking, ACM Transactions on Sensor Networks, and Journal of Paralel and Distributed Computing. A (co)founder of IEEE PerCom, IEEE WoWMoM, IEEE SMARTCOMP, and ACM ICDCN conferences, he served as the General and Technical Program Chair of many conferences. He is a recipient of 12 Best Paper Awards in prestigious conferences, such as ACM MobiCom and IEEE PerCom, and numerous awards for teaching, mentoring and research including the IEEE Computer Society’s Technical Achievement award for pioneering contributions to sensor networks and mobile computing, and the University of Missouri System President’s Award for Sustained Career Excellence. He graduated 11 postdoctoral fellows, 50 PhD and 31 MS thesis students. Dr. Das is a Distinguished Alumnus of the Indian Institute of Science, Bangalore, and a Fellow of the IEEE and National Academy of Inventors (NAI).

Talk Title: Federated and Split Machine Learning in the Internet of Things

Abstract: In this talk we discuss approaches for distributed machine learning (ML) in wireless networks, in particular vehicular networks and resource-constrained Internet of Things (IoT) networks. Federated Learning (FL) and Split Learning (FL) are popular approaches in such environments. In particular, FL is often used to support privacy in distributed systems. First, we present Early Exit of Communication (EEoC), which adaptively splits ML inference in an IoT edge computing environment to meet latency and energy constraints. This layer-based (vertically partitioned) approach has been extended by Distributed Micro-Split Deep Learning in Heterogeneous Dynamic IoT (DISNET), which adds horizontal partitioning to better support flexible, distributed, and parallel execution of neural network models on heterogeneous IoT devices under dynamic conditions. Then, we also consider the training aspect by developing and evaluating Adaptive REsource-aware Split-learning (ARES), a scheme for efficient model training in IoT systems. Recent work suggests Dynamic FL (DFL) for heterogeneous IoT, which uses resource-aware SL and FL based on similarity-based layer-wise model aggregation.

Biography: Torsten Braun (cds.unibe.ch/about_us/team/current/prof_dr_braun_torsten ) is head of the Communication and Distributed Systems (CDS) research group at the Institute of Computer Science, University of Bern, where he has been a full professor since 1998. He got the Ph.D. degree from University of Karlsruhe (Germany) in 1993. From 1994 to 1995, he was a guest scientist at INRIA Sophia-Antipolis (France). From 1995 to 1997, he worked at the IBM European Networking Centre Heidelberg (Germany) as a project leader and senior consultant. He has been a vice president of the SWITCH (Swiss Research and Education Network Provider) Foundation from 2011 to 2019. He has been a Director of the Institute of Computer Science and Applied Mathematics at University of Bern between 2007 and 2011, and from 2019 to 2021.

Talk Title: Blockchain-Enabled Trust in Edge-based Internet of Things Architectures: State of the Art and Research Challenges

Abstract: Internet of Things (IoT) aims to enable a world where physical objects, integrated into information networks, provide smart services for human beings. The introduction of edge computing in IoT can reduce the decision-making latency, save bandwidth resources, and extend the cloud services to be distributed at the edge of the network. However, edge-based IoT systems currently faces challenges in their decentralized trust management. Trust management plays an essential role for reliable data fusion and mining, provisioning of services with context-awareness, and enhanced user privacy and information security. In this talk, we analyze the edge-based IoT architectures that are available in the literature. Then, a comprehensive review of trust requirements in edge-enabled IoT systems is presented. We notably discuss about blockchain as a key enabler to address many trust related issues in IoT and consider closely the complementary interrelationships between blockchain and edge computing. Finally, we provide a detailed analysis of trusted edge computing based IoT systems’ performance and recommend promising research directions for future investigations.

Biography: Giancarlo Fortino (IEEE Fellow 2022) is Full Professor of Computer Engineering at the Dept of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy. He received a PhD in Computer Engineering from Unical in 2000. He is also distinguished professor at Wuhan University of Technology and Huazhong Agricultural University (China), high-end expert at HUST and NIST (China), senior research fellow at the Italian ICAR-CNR Institute, CAS PIFI visiting scientist at SIAT – Shenzhen, and Distinguished Lecturer for IEEE Sensors Council. He was also visiting researcher at ICSI, Berkeley (USA), in 1997 and 1999 and visiting professor at Queensland University of technology in 2009. At Unical, he is the Rector’s delegate to Int’l relations, the chair of the PhD School in ICT, the director of the Postgraduate Master course in INTER-IoT, and the director of the SPEME lab as well as co-chair of Joint labs on IoT established between Unical and WUT, SMU and HZAU Chinese universities, respectively. Fortino is currently the scientific responsible of the Digital Health group of the Italian CINI National Laboratory at Unical. He is Highly Cited Researcher 2020-2023 in Computer Science by Clarivate. He had 25+ highly cited papers in WoS, and h-index=81 with 23000+ citations in Google Scholar. His research interests include wearable computing systems, e-Health, Internet of Things, and agent-based computing. He is author of 650+ papers in int’l journals, conferences and books. He is (founding) series editor of IEEE Press Book Series on Human-Machine Systems and EiC of Springer Internet of Things series and AE of premier int'l journals such as IEEE TASE (senior editor), IEEE TAFFC-CS, IEEE THMS, IEEE T-AI, IEEE IoTJ, IEEE SJ, IEEE JBHI, IEEE SMCM, IEEE OJEMB, IEEE OJCS, Information Fusion, EAAI, etc. He chaired many int’l workshops and conferences (130+), was involved in a huge number of int’l conferences/workshops (700+) as IPC member, is/was guest-editor of many special issues (80+). He is cofounder and CEO of SenSysCal S.r.l., a Unical spinoff focused on innovative IoT systems, and recently cofounder and vice-CEO of the spin-off Bigtech S.r.l, focused on big data, AI and IoT technologies. Fortino is currently AVP of the Cybernetics area of the IEEE SMCS and former member of the IEEE SMCS BoG and former chair of the IEEE SMCS Italian Chapter.