报 告 人：Giancarlo Fortino
Full Professor
University of Calabria
主 持 人：谭营
智能学院
时 间：2024年7月23日 10:00-11:00
地 址：必赢71886网址登录理科二号楼 2736 报告厅
腾讯会议：619-571-163
报告题目：
报告摘要:
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=82 with about 25000 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.
报告人简介:
The Edge Intelligence (EI) paradigm has recently emerged as a promising solution to overcome the inherent limitations of cloud computing (latency, autonomy, cost, etc.) in the development and provision of next-generation Internet of Things (IoT) services. Therefore, motivated by its increasing popularity, relevant research effort was expended in order to explore, from different perspectives and at different degrees of detail, the many facets of EI.
In such a context, the aim of this seminar is first to analyze the wide landscape on EI by providing a systematic analysis of the state-of-the-art manuscripts in the form of a tertiary study (i.e., a review of literature reviews, surveys, and mapping studies) and according to the guidelines of the PRISMA methodology. A comparison framework is, hence, provided and sound research questions outlined, aimed at exploring (for the benefit of both experts and beginners) the past, present, and future directions of the EI paradigm and its relationships with the IoT and the cloud computing worlds.
Second, we discuss our EI research in the context of the device-edge-cloud continuum paradigm developed in the Horizon Europe project “MLSysOps” and in the PRIN “Fluidware” project along with our vision of Digital Twin enabled by EI and applied to Smart City as a new enabler.
Finally, we will focus on our recently introduced methodology, named EdgeMiningSim, a simulation-driven methodology inspired by software engineering principles for enabling IoT Data Mining/Machine Learning. Such a methodology drives the domain experts in disclosing actionable knowledge, namely descriptive or predictive models for taking effective actions in the constrained and dynamic IoT scenario.
