Keynotes
A decade of LPWAN for smart objects: what's ahead?
Dr. Congduc Pham is Professor at University of Pau, France. His research interests focus on wireless & IoT systems and embedded AI applications for IoT. He published more than 140 scientific articles and gave more than 80 tutorials/keynotes. In H2020 WAZIUP and H2020 WAZIHUB, he was the scientific expert on Internet-of-Thing and LoRa technology. In H2020 HUBIQUITOUS he lead the deployment of SolutionLab for providing access to IoT/AI disruptive technologies in Africa. He coordinated the PRIMA INTEL-IRRIS project on smart irrigation with IoT and PRIMA RESILINK to increase smallholder’s resilience. Both projects develop digital platforms with cost-effective IoT/AI technologies to target the smallholder farmer communities. In PEPR AgriFutur he is leading the scientific activities to build sensing infrastructures to better qualify and quantify agroecological practices.
More info on https://iotsensingsystem.live-website.com/ and https://cpham.perso.univ-pau.fr/.
Abstract
LPWAN technologies targeting the Internet of Things (IoT) domain have been around for about a decade. They provide long-distance connectivity to battery-operated smart objects that were once difficult to connect with multi-hop approaches based on short communication technologies. Coupled with the availability of very powerful and affordable microcontrollers, new challenging applications in sensing, monitoring & surveillance applications that were before difficult to deploy can now be considered for out-of-the-box deployment. Among those technologies, LoRa from Semtech can be considered the leading LPWAN technology operating in the unlicensed bands outside of China. In this talk, we will first introduce selected LPWAN technologies, how they operate and how they fit into the ecosystem depending on application’s needs and constraints. We will then focus on LoRa and present the challenges that still exist in efficiently share the radio resource when deploying large scale LoRa networks, including non-terrestrial networks. In conclusion, we will try to give our vision of what’s ahead in the LPWAN ecosystem.
Professor Congduc Pham
Université de Pau et des Pays de l’Adour (UPPA), France
Nicos Maglaveras
Professor of Medical Informatics Aristotle University of Thessaloniki Greece
Personalised health driven by digital health systems and multi-source health/environmental data, ML/AI/DL analytics and predictive models
Nicos Maglaveras received the diploma in electrical engineering from the Aristotle University of Thessaloniki (A.U.Th.), Greece, in 1982, and the M.Sc. and Ph.D. degrees in electrical engineering with an emphasis in biomedical engineering from Northwestern University, Evanston, IL, in 1985 and 1988, respectively. He is currently a Professor of Medical Informatics, A.U.Th. He served as head of the graduate program in medical informatics at A.U.Th, as Visiting Professor at Northwestern University Dept of EECS (2016-2019), and is a collaborating researcher with the Center of Research and Technology Hellas, and the National Hellenic Research Foundation.
His current research interests include biomedical engineering, biomedical informatics, ehealth, AAL, personalised health, biosignal analysis, medical imaging, and neurosciences. He has published more than 500 papers in peer-reviewed international journals, books and conference proceedings out of which over 160 as full peer review papers in indexed international journals. He has developed graduate and undergraduate courses in the areas of (bio)medical informatics, biomedical signal processing, personal health systems, physiology and biological systems simulation.
He has served as a Reviewer in CEC AIM, ICT and DGRT D-HEALTH technical reviews and as reviewer, associate editor and editorial board member in more than 20 international journals, and participated as Coordinator or Core Partner in over 45 national and EU and US funded competitive research projects attracting more than 16 MEUROs in funding. He has served as president of the EAMBES in 2008-2010. Dr. Maglaveras has been a member of the IEEE, AMIA, the Greek Technical Chamber, the New York Academy of Sciences, the CEN/TC251, Eta Kappa Nu and an EAMBES Fellow.
Abstract
The last years saw a steep increase in the number of wearable sensors and systems, mhealth and uhealth apps both in the clinical settings and in everyday life. Further large amounts of data both in the clinical settings (imaging, biochemical, medication, electronic health records, -omics), in the community (behavioral, social media, mental state, genetic tests, wearable driven bio-parameters and biosignals) as well as environmental stressors and data (air quality, water pollution etc.) have been produced, and made available to the scientific and medical community, powering the new AI/DL/ML based analytics for the identification of new digital biomarkers leading to new diagnostic pathways, updated clinical and treatment guidelines, and a better and more intuitive interaction medium between the citizen and the health care system.
Thus, the concept of connected and translational health has started evolving steadily, connecting pervasive health systems, using new predictive models, new approaches in biological systems modeling and simulation, as well as fusing data and information from different pipelines for more efficient diagnosis and disease management.
In this talk, we will present the current state-of-the-art in personalized health care by presenting cases from COVID-19 and COPD patients using advanced wearable vests and new technology sensors including lung sound and EIT, new outcome prediction models in COVID-19 ICU patients fusing X-Rays, lung sounds, and ICU parameters transformed via AI/ML/DL pipelines, new approaches fusing environmental stressors with -omics analytics for chronic disease management, and finally new ML/AI-driven methodologies for predicting mental health diseases including suicidality, anxiety, and depression.
