Empowering the Human-Centric Continuum: From Semantic Sensor Networks to Edge Intelligence
Maria Bermudez-Edo, Universidad de Granada, Spain, Spain
Cyber-Physical Cloud Services
Johan Eker, Ericsson Research & Lund University, Sweden, Sweden
Green Analytics
Ladjel Bellatreche, National Engineering School for Mechanics and Aerotechnics, France, France
Brief Bio
María Bermúdez-Edo is a Senior Associate Professor at the University of Granada (accredited as Full Professor), where she leads research at the intersection of the Internet of Things (IoT), Semantic Technologies, and Mobile Health. With a career spanning over 30 years, she combines industrial experience with international academic leadership. She began her professional journey in the late 90s at Telefónica I+D and Siemens, working on the deployment of ISDN, X.25 protocols, and the convergence of fixed-mobile networks. Her transition to academia included research secondments at ETH Zurich and UC Berkeley, where she explored the early foundations of programmable networks. She was a Postdoctoral Research Fellow at the University of Surrey, where she worked in four major EU IoT projects, serving as a Work-Package Leader and contributing to the development of IoT-Lite (a W3C Member Submission). Currently, she is the Principal Investigator (PI) of the national project OnTheEdge, which focuses on detecting stress in the elderly using the Computing Continuum, and the regional project Smart Ecomountains, dedicated to sustainability and mobility in rural environments. She serves as an Associate Editor for some journals and is a recognized expert for the Spanish National Research Agency (AEI).
Abstract
The Internet of Things (IoT) has undergone a profound transformation, moving from early hardware-centric connectivity to the sophisticated, software-defined Edge-Cloud Continuum of today. Understanding this historical progression—from isolated network silos to interoperable, intelligent systems—is essential to deciphering the future of the field. Drawing upon a 30-year career spanning industrial telecommunications and academic leadership, the speaker will illustrate how the evolution from semantic data modeling to context-aware architectures has laid the groundwork for modern human-centric IoT. The session explores the synergy between semantic interoperability and the computing continuum to address critical challenges in e-health and rural sustainability. By shifting from raw data flows to decentralized, intelligent systems, we can enable transformative applications that prioritize privacy and social impact. This journey highlights how the integration of advanced analytics at the edge bridges the gap between complex intelligence and real-world human needs. In the conclusions, the keynote will identify key trends that must define the future of the Internet of Things, providing a strategic roadmap toward an autonomous, inclusive, and ethical connected society.
Brief Bio
Johan Eker is a Principal Researcher at Ericsson Research and a full Professor in real-time control systems at Lund university. He earned his PhD in 1999 and then joined the Ptolemy group at UC Berkeley. He is leading the WASP research arena on data-driven operations of large scale systems using machine learning. His current research focus is on cloud and 6G services for cyber-physical systems. His research work ranges from programming language design, real-time control systems, mobile communications. software design for mobile devices, adaptive resource management, IoT and cloud technology. He is the co-designer of the CAL Actor Language, which is part of the MPEG standard ISO/IEC 23001-4:2011. He holds over 70 granted patents in the areas of telecom, IoT and cloud computing. He is involved in the operation of the Ericsson Research Data Center and works with industrial cloud applications.
Abstract
Cloud services are entering a second wave—moving beyond web storefronts and IT systems to include industrial digitalization. Automation and time-sensitive systems, until recently confined to the factory floor, are being offloaded to the cloud, driven in part by the ultra-low-latency promises of 6G networks. Notably, 6G itself is increasingly software-defined, with network functions virtualized and deployed on cloud infrastructure, making the cloud both the enabler and the platform. The core challenge is timing: industrial control loops demand deterministic, sub-millisecond responses, while cloud platforms and hypervisors introduce virtualization overhead, scheduling jitter, and resource contention that fundamentally conflict with hard real-time guarantees. Even as 6G aims to shrink network latency, the very software stack it runs on faces the same real-time limitations. Yet research on this evolution remains siloed—real-time systems specialists, hypervisor developers, network architects, and 6G researchers tend to work independently, optimizing for one dimension without adequately addressing the others.
https://www.kks.se/en/article/johan-eker-good-people-a-coffee-machine-and-a-touch-of-madness/
Brief Bio
Ladjel Bellatreche is an Exceptional Class Full Professor at the National Engineering School for Mechanics and Aerotechnics (ISAE-ENSMA) in Poitiers, France, since 2010. He previously served as an Assistant and Associate Professor at Poitiers University and is currently a Part-time Professor at Harbin Institute of Technology (HIT), China. He has held various visiting positions at international institutions in Australia, Canada, the USA, and China.
Professor Bellatreche’s research covers data science, artificial intelligence, knowledge graphs, query-answering systems, recommender systems, and large language models, with a strong emphasis on query performance and energy efficiency. He has authored over 360 publications in leading international conferences and journals. He has led research teams and played major roles in international conferences such as ADBIS, CoopIS, ER, DaWaK, and IEEE Big Data. He serves as Associate Editor for Data and Knowledge Engineering (Elsevier) and Knowledge and Information Systems Journal (Springer), and is a member of the steering committees of several international conferences, including ER, ADBIS, DOLAP, MEDES, and BDA. He is also active on editorial boards and program committees, and has (co)-supervised 37 PhD students. His work spans multiple application domains, including aeronautics, urban computing, medicine, the film industry, and sustainable development
Abstract
In today's world, our lives are deeply intertwined with computers, making it essential to explore every possible avenue for saving energy across hardware components, system software, and applications. Data Management Systems (DMSYSs) are central to this new energy-conscious paradigm. Among their components, the query processor plays a critical role in ensuring efficient data processing. Studying the energy efficiency of this component has become an urgent necessity.
Traditionally, most query optimizers focus on minimizing input/output operations and maximizing the use of RAM, but often overlook energy considerations. Moreover, there is a common misconception that energy management should be handled solely by the operating system and firmware, relegating DMSYSs to a secondary role. In our view, true energy savings can only be achieved through the integration of both software and hardware solutions. This integration is logical, as query optimizers rely on cost models that already incorporate hardware and software parameters to select optimal query plans.
As scientists, we believe it is our responsibility to encourage the research community to prioritize the energy efficiency of DMSYSs. To this end, we first provide a comprehensive survey to motivate further research in this area. Next, we present a roadmap that highlights recent hardware and software solutions impacting query processors. Finally, we offer practical guidelines for developing green query optimizers.