Software Defined Systems: Integrating the Cyber World with the Internet of Things by Leveraging on Network Intelligence
Antonio Puliafito, Università degli Studi di Messina, Italy
Machine Learning in Digital Twin Edge Networks
Yan Zhang, University of Oslo, Norway
IoT for Smart and Connected Health
Honggang Wang, University of Massachusetts Dartmouth, United States
Software Defined Systems: Integrating the Cyber World with the Internet of Things by Leveraging on Network Intelligence
Antonio Puliafito
Università degli Studi di Messina
Italy
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Brief Bio
Antonio Puliafito is a full professor of computer engineering at the University of Messina, Italy. His interests include parallel and distributed systems, networking, IoT, Cloud computing, advanced analytical modeling techniques. He regularly acts as a referee for the European Community since 1998. He contributed to the development of the software tools WebSPN, ArgoPerformance and Stack4Things. He co-authored the text entitled "Performance and Reliability Analysis of Computer Systems", edited by Kluwer. From 2006 to 2008 he acted as the technical director of the Project 901, winner of the CISCO innovation award. He actively contributed to the success of the TriGrid VL and PI2S2 projects. He has been working in several EU funded projects such as: Reservoir, Vision Cloud, CloudWave, Beacon, Frontier Cities. He was also the main investigator of the Italian PRIN2008 research project Cloud@Home, to combine cloud and volunteer computing. He acted as scientific coordinator of the PON 2007-2013 SIGMA project on using cloud computing to manage severe risk phenomena. He is coordinating the #SmartME crowdfunding initiative to develop a smart city framework in the city of Messina. He is the co-founder of SmatMe.io, a startup working on the integration of cloud and IoT in smart cities contexts. He leads the Toolsmart project to enhance re-using of smart city solutions in the cities of Turin, Padua, Lecce and Syracuse.
Abstract
A smart city represents an improvement of today’s cities both functionally and structurally, that strategically utilizes many smart factors, such as information and communications technology (ICT), to increase the city’s sustainable growth and strengthen city functions, while ensuring citizens’ quality of life and health. Cities can be viewed as a microcosm of “objects” with which citizens interact daily: street furniture, public buildings, transportation, monuments, public lighting and much more. Moreover, a continuous monitoring of a city’s status occurs through sensors and processors applied within the real-world infrastructure. Industrial sites represent similar scenarios, where data collected from distributed objects allow to actuate powerful control strategies.
The Internet of Things (IoT) concept imagines all these objects being “smart”, connected to the Internet, and able to communicate with each other and with the external environment, interacting and sharing data and information. Each object in the IoT can be both the collector and distributor of information regarding mobility, energy consumption, air pollution, as well as potentially offering cultural and tourist information.
As a consequence, cyber and real worlds are strongly linked in a smart city, such as in industrial site. New services can be deployed when needed and evaluation mechanisms will be set up to assess the health and success of the system under control. This talk will present some innovative developments in areas related to smart cities and smart industries, leveraging on the features supported by network intelligence at the edge of the network.
Machine Learning in Digital Twin Edge Networks
Yan Zhang
University of Oslo
Norway
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Brief Bio
Yan Zhang is currently a Full Professor with the Department of Informatics, University of Oslo, Norway. His research interests include next-generation wireless networks leading to 6G, green and secure cyber-physical systems. Dr. Zhang is an Editor for several IEEE transactions/magazine. He is a program/symposium chair in a number of conferences, including IEEE IWQoS 2022, IEEE ICC 2021, IEEE SmartGridComm 2021. He is the Chair of IEEE Communications Society Technical Committee on Green Communications and Computing (TCGCC). He is an IEEE Communications Society Distinguished Lecturer and IEEE Vehicular Technology Society Distinguished Speaker. He was an IEEE Vehicular Technology Society Distinguished Lecturer during 2016-2020. Since 2018, Prof. Zhang was a recipient of the global “Highly Cited Researcher” Award (Web of Science top 1% most cited worldwide). He is Fellow of IEEE, Fellow of IET, elected member of Academia Europaea (MAE), elected member of the Royal Norwegian Society of Sciences and Letters (DKNVS), and elected member of Norwegian Academy of Technological Sciences (NTVA).
Abstract
In this talk, we mainly introduce our proposed new research direction Digital Twin Edge Networks (DITEN). We first present the concept and model related to Digital Twin (DT) and DITEN. Then, we focus on new research challenges and results when machine learning is exploited in DITEN, including federated learning, deep reinforcement learning and transfer learning. Edge association and DT mobility, as unique research questions, will be defined and analyzed. We are also expecting that the talk will help the audience understand the future development of edge computing, e.g., digital twin edge networks in the context of Metaverse.
IoT for Smart and Connected Health
Honggang Wang
University of Massachusetts Dartmouth
United States
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Brief Bio
Honggang Wang is a professor of Electrical and Computer Engineering at UMass Dartmouth. His research interests include Internet of Things and its applications in health and transportation (e.g., autonomous vehicles) domains, Machine Learning and Big Data, Multimedia and Cyber Security, Smart and Connected Health, Wireless Networks and Multimedia Communications. He has produced a body of high-quality publications in prestigious journals and conferences in his research areas, winning prestigious best paper awards six times, including Globecom’19 and WCNC’08. He serves as the steering committee and founding Co-chair of IEEE/ACM Conference on Connected Health (CHASE) and TPC co-chair of IEEE CHASE 2016, which is a leading international conference in the field of connected health. He has also been serving as the Editor in Chief (EiC) for IEEE Internet of Things journal since 2020. He was the past Chair (2018-2020) of IEEE Multimedia Communications Technical Committee and the Chair of IEEE eHealth Committee (2020-2021). He is an IEEE Distinguished Lecturer and an IEEE Fellow for his contribution to low power wireless for IoT and Multimedia Applications.
Abstract
Smart and Connected Health (SCH) is the use of Internet, sensing, communications and intelligent techniques in support of healthcare applications. Internet of Things (IoT) systems such as Wireless body area network (WBAN) system with various types of biomedical sensors is one of key infrastructures of SCH and provide an opportunity to address issues in rapidly increasing mHealth/eHealth applications. However, there are significant challenges in this area, such as improving the performance of WBANs, analytics of large and continuous physiological data collected from biomedical sensors and predictive modeling, and securing data transmission and protecting data privacy, especially in mobile and wireless environments. In this talk, I will focus on the introduction of two case studies: (1) developing a wearable biosensor system for the remote detection of life-threatening events in infants; (2) a security system to support reliable and secured data transmissions over WBANs.