Keynote 1

Title: Riding the Waves of Research Hypes: Staying Relevant in IoT and Beyond

Abstract: Researchers may face challenges in sustaining momentum and securing resources as the fields they target mature and evolve over time. The challenges are compounded for certain fields that come with major hypes and opportunities but in the end, they may lose the initial momentum and become saturated with redundant studies. In such cases, researchers need to adapt wisely to still stay relevant and impactful. This talk will explore strategies for continues innovation in such fields by using IoT and IoT security as a case study. We will examine the initial surge of IoT as a hot research topic starting from early 2000s and then point out how the field needed to evolve to keep up its initial impact on our lives. Specifically, we will discuss new challenges arising within the IoT domain as the IoT devices are deployed in many emerging applications. For instance, with the rise of blockchain and cryptocurrencies, there are many efforts to enable cryptocurrencies that may be accommodated by IoT devices with limited computational and communication capabilities. Similarly, as generative AI technologies become more sophisticated and prevalent, it is getting easier to deceive sensor defense systems for IoT devices such as drones that rely on machine learning. We will present our current projects pursued at Advanced Wireless and Security (ADWISE) Lab at Florida International University (FIU) that touches these emerging IoT research areas along with some future directions.

Bio: Dr. Kemal Akkaya is an Eminent Scholar Chaired Professor in the

Knight Foundation School of Computer and Information Sciences (KFSCIS)

at Florida International University (FIU). He received his PhD in Computer

Science from University of Maryland Baltimore County in 2005. Dr. Akkaya

was a visiting professor at The George Washington University in 2013, a

Faculty Fellow at Airforce Research Lab in Summer 2020 and a visiting

faculty at the University of Florida Nelms Institute of Connected World in

2021. He is the co-Director of Center for Security, Privacy and Trustworthy

AI (CIERTA) and leads the Advanced Wireless and Security Lab (ADWISE) at

FIU. His current research interests include security and privacy,

internet-of-things, and cyber-physical systems. Dr. Akkaya is a Fellow of IEEE and senior member of ACM. He is the area editor for IEEE Transactions on Forensics and Security, Elsevier Ad Hoc Network Journal, and Computer Networks Journal. Dr. Akkaya was the editor-in-chief of Springer Nature Computer Science journal (2022-2023), the General Chair of IEEE LCN 2018, General Co-chair of IEEE NOMS 2023, and TPC Chair for IEEE ICC Smart Grid Communications in 2019. He has published over 300 papers in peer-reviewed journal and conferences, 1 book along with 9 patents. These publications received more than 20,000 citations with a Google h-index of 58. He was listed among the top 2% scientists in the world according to a Stanford University study in 2020-24. Dr. Akkaya received FIU Faculty Senate Excellence in Research Award, FIU College of Engineering and Computing Research Award both in 2020 and FIU Top Scholar Award in 2023. He has also received ``Top Cited'' article award from Elsevier in 2010.

Keynote 2

Title: Heterogeneity-aware Distributed Learning for Collaborative Sensing over Wireless Networks

Abstract: Multi-agent collaborative sensing is essential for wireless sensor networks and IoT applications, which faces various practical challenges. For example, there is an increasing need for spectrum monitoring and management to support the growing demands of data-intensive applications across wireless networks. Networks of spectrum sensors are deployed to gain spectrum awareness across time, frequency, and space, performing collaborative tasks like multi-channel spectrum sensing and large-scale radio map estimation. While federated learning enables collaborative learning, its reliance on a shared homogeneous model limits performance in heterogeneous networks. Distributed sensing nodes, constrained by hardware and sensing capabilities, capture only partial views of the network environment, leading to data heterogeneity that traditional federated learning struggles to handle. This talk introduces a distributed multi-task learning architecture designed for heterogeneous networks, such as for wideband spectrum occupancy detection and radio map estimation under partial sensory observations. Through task-aware model decoupling, this approach supports heterogeneous feature extraction, improving spectrum learning across distributed sensors. The heterogeneity-aware approach enhances communication-efficient distributed learning for broad network applications.

Bio: Zhi Tian is a Professor at the Electrical and Computer Engineering

Department of George Mason University. Previously she was on the faculty of

Michigan Technological University, and served a 3-year term as Program

Director at the US National Science Foundation. Her research interests lie in

distributed machine learning, wireless communications, and statistical signal

processing. She is an IEEE Fellow. She was a Member-at-Large of the Signal

Processing Society Board of Governors (2019-2021). She was General

Co-Chair of the IEEE GlobalSIP Conference in 2016 and the IEEE SPAWC

Workshop in 2023. She served as an IEEE Distinguished Lecturer for both the

IEEE Communications Society and the IEEE Vehicular Technology Society. She

is the Editor-in-Chief for the IEEE Transactions on Signal Processing. She

received the IEEE Communications Society TCCN Publication Award in 2018.

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