IEEE 7th World Forum on Internet of Things
14 June-31 July 2021 // New Orleans, Louisiana, USA

Tutorials

 

All Scheduled Times are US Eastern Time (EDT)

 

Track Presenter Days Scheduled
Tuto1.1 and 1.2: Privacy for IoT: Signal Processing Theories and Methods Wee Peng Tay, Yang Song and Chongxiao Wang, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore Monday, June 14, 2021

10:30am-12:30pm

1:30pm-3:30pm

Tuto2.1 and 2.2:  Softwarization and Virtualization in 5G and Beyond Mobile Networks Fabrizio Granelli, Univ. of Trento, Italy

Frank Fitzek, TU Dresden, Germany

Tuesday, June 15, 2021

10:30am-12:30pm

1:30pm-3:30pm

Tuto3.1 and 3.2:  Controlling the IoT: Building an All-Wireless BLE SDN Testbed Alessandro Chiumento, Pervasive Systems Research Group, University of Twente, Enschede, Netherlands

Yuri Murillo, Networked Systems, Wavecore, ESAT, KU Leuven, Leuven, Belgium

Wednesday, June 16, 2021

10:30am-12:30pm

1:30pm-3:30pm

Tuto4.1 and 4.2:  Deep Reinforcement Learning and its Applications in Future Wireless Networks Dinh Thai Hoang, University of Technology Sydney, Australia

Shimin Gong, Sun Yat-sen University, China

Dusit Niyato, Nanyang Technological University, Singapore

Thursday, June 17, 2021

10:30am-12:30pm

1:30pm-3:30pm

Tuto5.1 and 5.2:  Prototyping Mobile-enabled Medical Devices using MIT App Inventor platform Sarvesh Karkhanis, Freelance Educator and Tech. Consultant, Thane, Maharashtra, India

 

Friday, June 18, 2021

10:30am-12:30pm

1:30pm-3:30pm

Tuto6.1: FedIoT: Network-Aware Federated Learning for Distributing AI through IoT Systems Christopher G. Brinton, Electrical and Computer Engg., Purdue University, USA

Rajesh M. Hegde, Electrical Engineering, Indian Institutes of Technology, Kanpur, India

Tao Zhang, Emerging Network Tech., National Institute of Standards and Technology, USA

Monday, June 14, 2021

10:30am-12:30pm

Tuto7.1:  The Emerging field of the Internet of Musical Things: Enabling Technologies and Open Challenges Luca Turchet, Deptt. Information Engg. and Computer Science, University of Trento, Italy

Carlo Fischione, Department of Networks and System Engg., KTH Royal Institute of Technology, Sweden

Marco Centenaro, R&D Department, Athonet S.r.l., Italy

Tuesday, June 15, 2021

10:30am-12:30pm

Tuto8.1:  IoT Enabling Technologies, RFID, Sensor and Actuators Payal Shah; Boeing Company, USA Wednesday, June 16, 2021

10:30am-12:30pm

Tuto9.1:  Edge/Fog Computing and Networking for AI-enabled IoT (AIoT) Hung-Yu Wei, Dept of Electrical Engineering, National Taiwan University, Taiwan

Ai-Chun Pang, Dept. of Computer Science and Information Engineering, National Taiwan University, Taiwan

Thursday, June 17, 2021

10:30am-12:30pm

Tuto10.1: Architecting Secure IoT Devices: Risk Based point of view Isaac Dangana CEH. Senior IoT Security Analyst, Red Alert Labs, France Friday, June 18, 2021

10:30am-12:30pm

 

 

Tutorial Session: 4 Hour


TUT-01: Privacy for IoT: Signal Processing Theories Methods

TUT-02: Softwarization and Virtualization in 5G and Beyond Mobile Networks

TUT-03: Building a BLE SDN Testbed for Online Network Protocol Testing

TUT-04: Deep Reinforcement Learning and its Applications in Future Wireless Networks

TUT-05: Prototyping Mobile-enabled Medical Devices using MIT App Inventor platform

 

Tutorial Session: 2 Hour


TUT-06: FedIoT: Network-Aware Federated Learning for Distributing AI through IoT Systems

TUT-07: The Emerging field of the Internet of Musical Things: Enabling Technologies and Open
Challenges

TUT-08: IoT Enabling Technologies, RFID, Sensor and Actuators

TUT-09: Edge/Fog Computing and Networking for AI-enabled IoT (AIoT)

TUT-10: Architecting Secure IoT Devices: Risk Based point of view

 

 

 

 

TUT-01: Privacy for IoT: Signal Processing Theories Methods
Presenters:

Wee Peng Tay, Yang Song, Chongxiao Wang, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

 

Wee Peng Tay received the BS degree in Electrical Engineering and Mathematics, and the MS degree in Electrical Engineering from Stanford University in 2002. He received the Ph.D. degree in Electrical engineering and Computer science from the Massachusetts Institute of Technology in 2008. He is currently an Associate Professor in the School of Electrical and Electronic Engineering at Nanyang Technological University, Singapore. He was an Associate Editor for the IEEE Transactions on Signal Processing (2015 – 2019), and is currently an Associate Editor for the IEEE Transactions on Signal and Information Processing over Networks, an Editor for the IEEE Transactions on Wireless Communications, and an Editor for the IEEE Open Journal of Vehicular Technology. His main research interests are in information and signal processing over networks, distributed inference and estimation, privacy for IoT, machine learning, and applied probability.

 

Yang Song received the B.Eng. degree in Information Engineering from Zhejiang University City College, China, in 2007. He received his M.Eng. and Ph.D. from the Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, in 2008 and 2013, respectively. From Feb. 2014 to Feb. 2016, he was a postdoc in the Signal & System Theory Group, University of Paderborn, Germany. Currently, he is a Senior Research Fellow at the School of Electrical and Electronic Engineering, Nanyang Technological University. His research interests include machine learning and signal processing. He is an Associate Editor of IET Signal Processing.


Chongxiao Wang received his B.Eng. degree in Electrical and Information Engineering from Zhejiang University of Sci‐Tech, Hangzhou, China, in 2011, and M.Sc. in Electrical Engineering from National University of Singapore in 2013, respectively. From 2013 to 2015, he worked as a software engineer at Continental AG. He is currently a research associate at the School of Electrical and Electronic Engineering, Nanyang Technological University. His research interests include statistical signal processing and inference privacy for machine learning.


Abstract: With the ubiquitous adoption of IoT devices like on‐body sensors, smart home appliances, and smart phones, massive amount of data about users’ habits, routines and preferences are being collected by service providers. Such data are utilized by service providers to improve the quality of life, e.g., by making building heating and ventilation systems more intelligent and adaptive. However, the same data can also be exploited to learn users’ private behaviors, habits and lifestyle choices. For consumers to widely adopt IoT systems, privacy protection mechanisms are a necessary feature of future IoT products. In this tutorial, we discuss the different aspects of privacy in an IoT system from a signal processing perspective. In a decentralized IoT network, a fusion center receives information from multiple sensors to infer a public hypothesis of interest. To prevent the fusion center from abusing the sensor information, each sensor sanitizes its local observation using a local privacy mapping, which is designed to achieve both inference privacy of a private hypothesis and data privacy of the sensor raw observations. We present different inference and data privacy metrics proposed in the literature and discuss the relationships between them. We show how privacy can be achieved in linear dynamical systems in which the state vector consists of both public and private states, and discuss privacy‐preserving estimation strategies over linear multitask networks, where agents’ local parameters of interest or tasks are linearly related. We present data‐driven inference privacy preserving frameworks to sanitize data so as to prevent leakage of sensitive information that is present in the raw data, while ensuring that the sanitized data is still compatible with the service provider’s legacy inference system. We develop an inference privacy framework based on the variational method and include maximum mean discrepancy and domain adaption. Finally, we present a deep learning model as an example of the proposed inference privacy framework.

 

TUT-02: Softwarization and Virtualization in 5G and Beyond Mobile Networks
Presenters:

Fabrizio Granelli, Univ. of Trento, Italy

Frank Fitzek, TU Dresden, Germany

 

Fabrizio Granelli is Associate Professor at the Dept. of Information Engineering and Computer Science (DISI) of the University of Trento (Italy). From 2012 to 2014, he was Italian Master School Coordinator in the framework of the European Institute of Innovation and Technology ICT Labs Consortium. He was Delegate for Education at DISI in 2015‐2016 and member of the Executive Committee of the Trentino Wireless and Optical Testbed Lab. He was IEEE ComSoc Distinguished Lecturer for 2012‐15, IEEE ComSoc Director for Online Content in 2016‐17 and IEEE ComSoc Director for Educational Services in 2018‐19. Prof. Granelli is coordinator of the research and didactical activities on computer networks within the degree in Telecommunications Engineering. He was advisor of more than 80 B.Sc. and M.Sc. theses and 10 Ph.D. theses. He is author or co‐author of more than 250 papers published in international journals, books and conferences in networking, with particular reference to performance modeling, cross‐layering, wireless networks, cognitive radios and networks, green networking and smart grid communications. Frank H. P. Fitzek is a Professor and chair of the communication networks group at Technische Universität Dresden coordinating the 5G Lab Germany. He received his diploma (Dipl.‐Ing.) degree in electrical engineering from the University of Technology ‐ Rheinisch‐Westfälische Technische Hochschule (RWTH) ‐ Aachen, Germany, in 1997 and his Ph.D. (Dr.‐Ing.) in Electrical Engineering from the Technical University Berlin, Germany in 2002 and became Adjunct Professor at the University of Ferrara, Italy in the same year. In 2003 he joined Aalborg University as Associate Professor and later became Professor. He co‐founded several start‐up companies starting with acticom GmbH in Berlin in 1999. He has visited various research institutes including Massachusetts Institute of Technology (MIT), VTT, and Arizona State University. In 2005 he won the YRP award for the work on MIMO MDC and received the Young Elite Researcher Award of Denmark. He was selected to receive the NOKIA Champion Award several times in a row from 2007 to 2011. In 2008 he was awarded the Nokia Achievement Award for his work on cooperative networks. In 2011 he received the SAPERE AUDE research grant from the Danish government and in 2012 he received the Vodafone Innovation price. His current research interests are in the areas of wireless and mobile 5G communication networks, mobile phone programming, network coding, cross layer as well as energy efficient protocol design and cooperative networking.


Frank H. P. Fitzek is a Professor and chair of the communication networks group at Technische Universität Dresden coordinating the 5G Lab Germany. He received his diploma (Dipl.‐Ing.) degree in electrical engineering from the University of Technology ‐ Rheinisch‐Westfälische Technische Hochschule (RWTH) ‐ Aachen, Germany, in 1997 and his Ph.D. (Dr.‐Ing.) in Electrical Engineering from the Technical University Berlin, Germany in 2002 and became Adjunct Professor at the University of Ferrara, Italy in the same year. In 2003 he joined Aalborg University as Associate Professor and later became Professor. He co‐founded several start‐up companies starting with acticom GmbH in Berlin in 1999. He has visited various research institutes including Massachusetts Institute of Technology (MIT), VTT, and Arizona State University. In 2005 he won the YRP award for the work on MIMO MDC and received the Young Elite Researcher Award of Denmark. He was selected to receive the NOKIA Champion Award several times in a row from 2007 to 2011. In 2008 he was awarded the Nokia Achievement Award for his work on cooperative networks. In 2011 he received the SAPERE AUDE research grant from the Danish government and in 2012 he received the Vodafone Innovation price. His current research interests are in the areas of wireless and mobile 5G communication networks, mobile phone programming, network coding, cross layer as well as energy efficient protocol design and cooperative networking.


Abstract: A big step lies ahead, when moving from today’s 4G cellular networks to tomorrow’s 5G network. Today, the network is used for content delivery, e.g. voice, video, data. Tomorrow, the 5G network (and possibly beyond that) will be fully softwarized and programmable, with new degrees of freedom. The aim of the tutorial is to illustrate how the emerging paradigms of Software Defined Networking, Network Function Virtualization, and Information Centric Networking will impact on the development of future systems and networks, both from the theoretical/formal as well as from the practical perspective. Main focus will be on mobile networks, i.e. 5G and beyond. The tutorial will provide a comprehensive overview of the individual building blocks (software defined networking; network function virtualization; information centric networks) enabling the concept of computing in future networks, starting from use cases and concepts over technological enablers (Mininet; Docker) and future innovations (machine learning; network coding; compressed sensing) to implementing all of them on personal computers. Practical hands‐on activities will be proposed, with realistic use cases to bridge theory and implementation by several examples, through the usage of a pre‐built ad‐hoc Virtual Machine (ComNetsEmu) that can be easily extended for new experiments. The instructions to download the Virtual Machine will be provided in advance of the event. The main objective of the tutorial will be to expose attendees to the most recent technologies in the field of networking and teach them how to use them in a real setup in the “hands‐on” session. A related book written by the two presenters “Computing in Communication Networks” was published in 2020 by Elsevier, and it provides in‐depth description of the concepts and hands‐on activities presented in the tutorial, to enable interested attendees to learn additional details on the reviewed technologies.


TUT-03: Building a BLE SDN Testbed for Online Network Protocol Testing
Presenters:

Alessandro Chiumento, Pervasive Systems Research Group, University of Twente, Enschede, Netherlands

Yuri Murillo, Networked Systems – Wavecore, ESAT, KU Leuven, Leuven, Belgium

 

Alessandro Chiumento is Assistant Professor in the Pervasive Systems group at the University of Twente, the Netherlands. He is an expert on reconfigurable networks and cross‐layer analysis of wireless systems. He has been working on distributed management solutions for dynamic systems for large and heterogeneous wireless networks. His current interests include softwerised industrial IoT networks, dynamic quality of service, machine learning for wireless and wireless for machine learning.

 


Yuri Murillo is a postdoctoral researcher in the Networked Systems group at the Electrical Engineering Department (ESAT) of KU Leuven, Belgium. His research primarily focuses on mesh networks, opportunistic networking protocols, development of new channel propagation models and system design of novel networking paradigms. His current research interests include optimization of BLE mesh networks, efficient management of massive IoT networks and wireless power transfer for battery‐less IoT networks.

 


Abstract: This tutorial focuses on Bluetooth Low Energy (BLE) mesh networking and elaborates on the design choices and implementation of the BLE Software Defined Network (SDN) testbed at KU Leuven. The BLE SDN testbed offers an all wireless control plane implementation that provides efficient network configuration and management for such a low power and resource constrained network. The tutorial will first introduce the benefits of SDN for Internet of Things (IoT) networks, along with its current implementation challenges, and will give an overview of the possible solutions needed to ensure isolation of control and data plane in the wireless domain. Then, the architecture of the testbed will be explained, focusing on both the hardware used and software developed. A hands‐on demonstration of the interaction of the described hardware and software will follow, where attendees will be able to program the nodes and configure and execute a network test. Finally, the tutorial will showcase the benefits of applying the SDN framework to BLE mesh with a proof of concept where a network congestion is automatically detected and solved by the central network controller.

 

TUT-04: Deep Reinforcement Learning and its Applications in Future Wireless Networks
Presenters:

Dinh Thai Hoang, University of Technology Sydney, Australia

Shimin Gong, Sun Yat-sen University, China

Dusit Niyato, Nanyang Technological University, Singapore

 

Dinh Thai Hoang is currently a faculty member with the School of Electrical and Data Engineering, the University of Technology Sydney, Australia. He received his Ph.D. from the Nanyang Technological University, Singapore, in 2016. His research interests include emerging topics in wireless communications and networking such as ambient backscatter communications, deep reinforcement learning, IoT, mobile edge and 5G/6G networks. He is an Exemplary Reviewer of IEEE TCOM in 2018 and an Exemplary Reviewer of IEEE TWC in 2017 and 2018. Currently, he is an editor of IEEE WCL and IEEE TCCN.


Shimin Gong is currently an Associate Professor with the School of Intelligent Systems Engineering, Sun Yat‐sen University, Shenzhen, China. He received the Ph.D. degree from Nanyang Technological University, Singapore, in 2014. His research interests include IoT, wireless powered communications, and backscatter communications, with a special focus on optimization and machine learning in wireless communications. He was a recipient of the Best Paper Award on MAC and Cross‐layer Design in IEEE WCNC 2019. He has been the Lead Guest Editor of the IEEE TCCN, a special issue on Deep Reinforcement Learning on Future Wireless Communication Networks.


Dusit Niyato (M’09‐SM’15‐F’17) is currently a professor in the School of Computer Science and Engineering and, by courtesy, School of Physical & Mathematical Sciences, at the Nanyang Technological University, Singapore. He received B.E. from King Mongkuks Institute of Technology Ladkrabang (KMITL), Thailand in 1999 and Ph.D. from the University of Manitoba, Canada in 2008. He won the Best Young Researcher Award of IEEE Communications Society (ComSoc) Asia Pacific (AP) and The 2011 IEEE Communications Society Fred W. Ellersick Prize Paper Award. Currently, he is serving as the EiC of IEEE COMST, a senior editor of IEEE WCL, an area editor of IEEE TWC, an editor of IEEE TCOM, an associate editor of IEEE TMC, IEEE TVT, and IEEE TCCN. He was a guest editor of IEEE JSAC. He was a Distinguished Lecturer of the IEEE Communications Society for 2016‐2017. He was named the 2017, 2018, 2019 highly cited researcher in computer science. He is a Fellow of IEEE.


Abstract: Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and largescale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this tutorial, we aim to provide fundamental background of DRL and then study recent advances in DRL to address practical challenges in wireless networks. In particular, we first give a tutorial of deep reinforcement learning from basic concepts to advanced models to motivate and provide fundamental knowledge for the audiences. We then provide a case study together with implementation details to help the audiences having better understanding how to practice with DRL. After that, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.

 

TUT-05: Prototyping Mobile-enabled Medical Devices using MIT App Inventor platform
Presenter:

Sarvesh Karkhanis, Freelance Educator and Tech. Consultant, Thane, Maharashtra, India

 

Sarvesh Karkhanis is a Computer Scientist, Maker, Educator and a featured Inventor whose invention of a potentially life‐saving Medical Device was recently featured in the news. He is also the founder of COVID‐ 19: Makers Collaborative, an online collaboration platform for COVID‐19 innovators. Mr. Karkhanis is a MIT certified Master Trainer in Educational Mobile Computing from 2019 cohort. Mr. Karkhanis utilized MIT App Inventor platform for development of his Medical Device, which was part of his Senior Capstone Project at college. He is a veteran user of MIT App Inventor platform, utilizing the platform for his projects for almost a decade. Mr. Karkhanis has an wide range of experience teaching Robotics as well as science and technology related topics to school and college level students. Now Mr. Karkhanis wishes to share his knowledge, Design Thinking process and journey as an inventor with others, who wish to work in the field of IoT Healthcare technology.


Abstract: This tutorial is a jumpstart lesson on how medical researchers or product designers in the field of Healthcare can utilize the powerful MIT App Inventor platform for rapid‐prototyping of IoT enabled Medical devices. This tutorial is conducted in the DIY format and would enable audience to learn linking and utilizing simple rapid‐prototyping tools to create an actual Medical Device prototype. Though the audience are not required to, they are encouraged to bring the recommended inexpensive material from the provided Bill of Material for an enriching hands‐on experience, through which they would build the prototype IoT Medical Device. 

 

TUT-06: FedIoT: Network-Aware Federated Learning for Distributing AI through IoT Systems
Presenters:

Christopher G. Brinton, Electrical and Computer Engg., Purdue University, USA

Rajesh M. Hegde, Electrical Engineering, Indian Institutes of Technology, Kanpur, India

Tao Zhang,Manager, Emerging Network Tech., National Institute of Standards and Technology, USA

 

Christopher G. Brinton is an Assistant Professor in the School of Electrical and Computer Engineering at Purdue University. Previously he was the Associate Director of the EDGE Lab and a Lecturer of Electrical Engineering at Princeton University. Dr. Brinton’s research interest is at the intersection of machine learning and networked systems, specifically in distributed machine learning, behavioral signal processing, and data‐driven network optimization. He is a co‐founder of Zoomi Inc., a big data startup company that has provided learning optimization to more than one million users worldwide and holds a US Patent in machine learning for individualized learning. His book The Power of Networks: 6 Principles That Connect our Lives and associated Massive Open Online Courses (MOOCs) have reached over 400,000 students to date. Since joining Purdue ECE in fall 2019, Dr. Brinton has won a 2019 Purdue Seed for Success Award, the 2020 Purdue ECE Outstanding Faculty Mentor Award, and the 2020 Ruth and Joel Spira Outstanding Teacher Award. Dr. Brinton received the PhD (with honors) and Master’s Degrees from Princeton in 2016 and 2013, and the BS Degree (valedictorian) from The College of New Jersey in 2011, all in Electrical Engineering.

 

Rajesh M. Hegde received the Ph.D. degree in computer science and engineering from the Indian Institute of Technology Madras, Chennai, India, in 2005. He joined IIT Kanpur as an Assistant Professor of EE in May 2008. He became an Associate Professor in 2012 and a Full Professor of EE in July 2016. He is currently the Head of the Electrical Engineering Department and also holds the Umang Gupta Chair position with IIT Kanpur India. He was also awarded the P.K. Kelkar Research Fellowship between 2009 and 2013. Between 2005 and 2008, He worked as a Researcher with the California Institute of Telecommunication and Information Technology and concurrently as a Lecturer (2007) with the Department of Electrical Engineering, University of California San Diego, CA, USA. He currently heads two research Laboratories at IIT Kanpur namely, Multimodal Information Systems Lab and Wireless Sensor Networks Lab with funding obtained from BSNL, DST, MietY, LG Soft, Samsung Research and Indian space research organization. He has published prolifically at several international conferences and journals in the area of signal processing, communication and networks. He is also a member of the National working groups of ITU‐T (NWG‐16 and NSG‐6) on developing multimedia applications.

 

Tao Zhang, an IEEE Fellow, has been leading research, product development, and corporate strategies to create disruptive innovations and transform them into practical solutions, standards, and products. He is currently managing the Emerging Networking Technologies Group in the Information Technology Lab at the US National Institute of Standards and Technology (NIST). He was the CTO for the Smart Connected Vehicles Business at Cisco Systems, and the Chief Scientist and the Director of multiple R&D groups working on wireless and vehicular networking at Telcordia Technologies (formerly Bellcore, originally part of the Bell Labs). He cofounded the Open Fog Consortium and the Connected Vehicle Trade Association (CVTA) and served as a founding Board Director for them. Tao holds 50+ US patents and coauthored two books “Vehicle Safety Communications: Protocols, Security, and Privacy” and “IP‐Based Next Generation Wireless Networks”, and 80+ peer‐reviewed papers. He served as the CIO and a Board Governor of the IEEE Communications Society and as a Distinguished Lecturer of the IEEE Vehicular Technology Society. He cofounded and served on leadership roles for multiple international conferences and forums.

Abstract: Many contemporary applications for the Internet of Things (IoT) require data‐intensive, latency‐ sensitive computation based on advanced Artificial Intelligence (AI) capabilities. To satisfy these requirements, new network computing paradigms have been aiming to migrate intelligence from the cloud towards the edge devices themselves. Federated learning has emerged recently as an elegant technique for distributing the training and inference of AI models across the edge, where devices maintain local models based on their own datasets, and an edge server periodically aggregates these into a global model and re‐synchronizes the devices. In the FedIoT tutorial, we will begin by detailing common AI models that are being leveraged for IoT intelligence tasks, and explaining how federated learning supports these models. Then, we will detail two techniques we have recently developed towards making federated learning network‐aware, i.e., accounting for the heterogeneity in computing capabilities across devices and the topology connecting them. Specifically, we will detail (i) a device‐side algorithm for augmenting the local model update process in federated learning with model consensus formation, facilitated by device‐to‐device (D2D) communications, and (ii) a server‐side algorithm for intelligently sampling IoT devices in each aggregation period based on expected contribution to the global model.

 

TUT-07: The Emerging field of the Internet of Musical Things: Enabling Technologies and Open
Challenges
Presenters:

Luca Turchet, Deptt. Information Engg. and Computer Science, University of Trento, Italy

Carlo Fischione, Department of Networks and System Engg., KTH Royal Institute of Technology, Sweden

Marco Centenaro, R&D Department, Athonet S.r.l., Italy

 

Luca Turchet is an Assistant Professor at the Department of Information Engineering and Computer Science of University of Trento. He received master degrees in Computer Science from University of Verona, in classical guitar and composition from Music Conservatory of Verona, and in electronic music from the Royal College of Music of Stockholm. He received the Ph.D. in Media Technology from Aalborg University Copenhagen. His scientific, artistic, and entrepreneurial research has been supported by numerous grants from different funding agencies including the European Commission (Marie Curie Fellow), the European Institute of Innovation and Technology, the European Space Agency, the Italian Minister of Foreign Affairs, and the Danish Research Council. He is co‐founder and Head of Sound and Interaction Design at the music tech company Elk. His main research interests are in music technology, Internet of Things, Extended Reality, human‐computer interaction, and multimodal perception. He is author of more than 90 peer‐reviewed papers (4 of which received an award) as well as of 2 international patents, and is co‐editor of the book “Ubiquitous Music Ecologies” from Routledge Press. He is Associate Editor of IEEE Access, and leading guest editor of special issues of the Journal of the Audio Engineering Society and Journal of Personal and Ubiquitous Computing. He is the Chair of the IEEE Open Innovations Association Conference 2020, the International Workshop on the Internet of Sounds 2020, and the ACM Audio Mostly Conference 2021.


Carlo Fischione is Professor at KTH Royal Institute of Technology, Sweden. He is the Director of the KTH‐ Ericsson “Data Science” Micro Degree Program, an advanced Artificial Intelligence program for Ericsson researchers worldwide. He is the Chair of the IEEE Machine Learning for Communications Emerging Technology Initiative. He received the Ph.D. degree in Electrical and Information Engineering in 2005 and the Laurea degree in Electronic Engineering (Summa cum Laude) in 2001 from University of L’Aquila, Italy. He has had faculty positions at the University of California at Berkley, MIT Massachusetts Institute of Technology, and Harvard University. He was recipient of numerous awards, including the Best Paper Awards from the IEEE Transactions on Communications (2018), the IEEE Transactions on Industrial Informatics (2007), and several Best Paper Awards at IEEE conferences. He has co‐authored over 180 publications, including book, book chapters, journals, conferences, and patents. He has offered consultancy to numerous technology companies such as ABB Corporate Research, Berkeley Wireless Sensor Network Lab, Ericsson Research, Synopsys, and United Technology Research Center. His research interests include optimization with applications to networks, wireless and sensor networks, and Internet of Things. He is Editor of the IEEE Transactions on Communications and the IEEE Journal on Selected Areas in Communications series Machine Learning in Communications and Networks.


Marco Centenaro is a System Engineering Manager at Athonet S.r.l.. He received the B.S. degree in Information Engineering from the University of Padova, Italy, in 2012, and the M.S. and Ph.D. degrees in Telecommunication Engineering from the same University in 2014 and 2018, respectively. He was on leave as Visiting Researcher at Nokia Bell Labs Stuttgart, Germany, and Yokohama National University, Japan, in 2016 and 2017, respectively. Between 2018 and 2019, he was Postdoctoral Research Fellow at the University of Padova, doing research on projects funded by Huawei Technologies, and at Aalborg University, Denmark, working in tight collaboration with Nokia Bell Labs Aalborg. Before joining Athonet, he was an Expert Researcher at Fondazione Bruno Kessler (FBK), Trento, Italy. His research interests include the design of 5G‐and‐beyond mobile networks and the integration of vertical industries into mobile systems. Dr. Centenaro was a recipient of the Ph.D. Award from the Gruppo Telecomunicazioni e Tecnologie dell’Informazione (GTTI – Italian Association of Telecommunications and Information Technologies) in 2018, in recognition of the best Ph.D. theses defended at an Italian University in the areas of communications technologies, and the Best Paper Award at IEEE PIMRC 2018.

 

Abstract: The Internet of Musical Things (IoMusT) is an emerging research field positioned at the intersection of Internet of Things and music technology. The IoMusT refers to the networks of computing devices embedded in physical objects (i.e., the Musical Things) dedicated to the production and/or reception of musical content. Musical Things, such as smart musical instruments or wearables, are connected by an infrastructure that enables multidirectional communication, both locally and remotely. The ecosystems associated with the IoMusT include interoperable devices and services that connect musicians and audiences to support musician‐musician, audience‐musicians, and audience‐audience interactions. In this tutorial, we present a vision in which the IoMusT enables the connection of digital and physical domains by means of appropriate information and communication technologies, fostering novel musical applications and services. We plan to give a comprehensive review of the state‐of‐the‐art technologies enabling the IoMusT, which include embedded systems dedicated to networked audio applications, ultra‐reliable, low‐latency and high‐quality communications, semantic web technologies, and distributed machine learning over wireless networks. This tutorial introduces these technologies, reviews the most important works, explains the core differences between the IoMusT and the general IoT field, and highlights crucial open problems.

 

TUT-08: IoT Enabling Technologies, RFID, Sensor and Actuators
Presenter:

Payal Shah; Boeing Company, USA

 

Payal Shah is currently the lead architect in Technology Planning Provisioning and Integration team within Network Infrastructure Services at The Boeing Company. Her current role involves architecting, standardizing location services technology focusing on Radio Frequency Identification as it evolves in the IoT space. Payal was born in India and lived there for the first 14 years of her life before moving to Los Angeles, CA. She attended the University of Southern California (USC) and majored in Business Information Systems. Payal joined The Boeing Company in 2002 and has varying position starting with developing end user applications using relational database management system such as Oracle. She then was part of a Cold Fusion Web application development team where she was responsible for developing web application involving retirement of legacy system. She then moved to a role as windows mobile application developer to design and build application using VB .NET and ASP .NET. In 2015, Payal transitioned to the network infrastructure team into her current role. Payal has a Master’s in Information Systems and Technology and is currently pursuing a certificate in Applied Cybersecurity at University of Washington.

Abstract: Internet of Things (IoT) is enabling smart factories with innovative method of producing and monitoring material using lower cost, scalable and flexible network architecture for factory operation, warehouses, logistics and industrial product services. IoT when discussion in industrial or factory context is referred to as the Industrial IoT. The Radio Frequency Identification (RFID) technology which evolved from the barcode technology has been the precursor to IoT evolution. RFID itself can be divided further into various categories that has specific applications based on business requirements. The new emerging Bluetooth Low Energy (BLE) standards further help provide a mechanism to gain better accuracy in tracking assets using wireless access points. IoT is enabled by a combination of these technologies working in tandem to provide to facilitate a smart factory environment.

 

TUT-09: Edge/Fog Computing and Networking for AI-enabled IoT (AIoT)
Presenters:

Ai-Chun Pang, Deptt. of Computer Science and Inf. Engg. National Taiwan University, Taiwan

Hung-Yu Wei, Deptt., of Electrical Engineering, National Taiwan University, Taiwan

 

Ai‐Chun Pang received the B.S., M.S. and Ph.D. degrees in Computer Science and Information Engineering from National Chiao Tung University, Taiwan, in 1996, 1998 and 2002, respectively. She joined the Department of Computer Science and Information Engineering, National Taiwan University (NTU), Taipei, Taiwan in 2002, and is now a Professor and Associate Dean of the College of Electrical Engineering and Computer Science, NTU. She was the director of Graduate Institute of Networking and Multimedia in 2013‐2016. She is also an Adjunct Professor of Graduate Institute of Communication Engineering, NTU, and an Adjunct Research Fellow of Research Center for Information Technology Innovation, Academia Sinica, Taiwan. Her research interests include wireless and multimedia Networking, 5G communications, software defined networking, and fog/edge computing. Dr. Pang was a recipient of Outstanding Research Award of Ministry of Science and Technology (MOST) in 2019, Outstanding Professor Award of Chinese Institute of Electrical Engineering in 2015, Outstanding Teaching Award at NTU in 2010, Investigative Research Award of Pan Wen Yuan Foundation in 2006, Wu Ta You Memorial Award of MOST in 2007. She received the Republic of China Distinguished Women Medal in 2009. She is an IEEE Vehicular Technology Society (VTS) Distinguished Lecturer in 2018‐22, and received VTS Women’s Distinguished Career Award in 2020. She is a Fellow of the IEEE.


Hung‐Yu Wei is a Professor in Department of Electrical Engineering and Graduate Institute of Communications Engineering, National Taiwan University. Currently, he serves as Associate Chair in Department of Electrical Engineering. He received the B.S. degree in electrical engineering from National Taiwan University in 1999. He received the M.S. and the Ph.D. degree in electrical engineering from Columbia University in 2001 and 2005 respectively. He was a summer intern at Telcordia Applied Research in 2000 and 2001. He was with NEC Labs America from 2003 to 2005. He joined Department of Electrical Engineering at the National Taiwan University in July 2005. His research interests include next‐generation wireless broadband networks, IoT, vehicular networking, fog/edge computing, cross‐layer design and optimization in wireless multimedia communications, and game theoretical models for communications networks. He received NTU Excellent Teaching Award in 2008 and 2018. He also received “Recruiting Outstanding Young Scholar Award” from the Foundation for the Advancement of Outstanding Scholarship in 2006, K. T. Li Young Researcher Award from ACM Taipei/Taiwan Chapter and The Institute of Information and Computing Machinery in 2012, Ministry of Science and Technology Research Project for Excellent Young Scholars in 2014, Excellent Young Engineer Award from the Chinese Institute of Electrical Engineering in 2014, Wu Ta You Memorial Award from MOST in 2015, and Outstanding Research Award from MOST in 2020. He was a consulting member of Acts and Regulation Committee of National Communications Commission during 2008‐2009. He served as a division director in NTU Computer and Information Networking Center during 2016‐2017. He has been actively participating in NGMN, IEEE 802.16, 3GPP, IEEE P1934, and IEEE P1935 standardization. He serves as Vice Chair of IEEE P1934 Working Group to standardize fog computing and networking architecture. He serves as Secretary for IEEE Fog/Edge Industry Community. He also serves as an Associate Editor for IEEE IoT journal. He is an IEEE certified Wireless Communications Professional. He was the Chair of IEEE VTS Taipei Chapter during 2016‐ 2017. He is currently the Chair of IEEE P1935 working group for edge/fog management and orchestration standard.


Abstract: The IoT and other wireless networking paradigms (e.g., Beyond 5G and 6G Wireless) depend upon low latency interaction between devices at the network edge and the cloud at centralized data centers. In the future B5G/6G wireless networks, the integrated architecture that applies joint design for computing system and communications system and leverages machine‐learning tools to optimize network/application performance will be developed. On the other hand, the potentially huge amount of data resulting from edge devices may make it impractical to directly communicate with the cloud. To significantly expand the usefulness of IoT and other demanding applications, the Fog/Edge paradigm provides the intermediate computing, communications, storage and processing capabilities needed between the cloud and its edge devices, i.e., Fog/Edge bridges the “Cloud‐to‐Thing” continuum.

 

TUT-10: Architecting Secure IoT Devices: Risk Based point of view
Presenter:

Isaac Dangana CEH. Senior IoT Security Analyst, Red Alert Labs, France

 

Isaac Dangana, CEH, is a Senior IoT Security Analyst and Researcher at Red Alert Labs. He has an MSc in Computer Security from the prestigious Engineering School ‐Ecole Pour l’Informatique et les Techniques Avancées, EPITA, in France. He has been involved at different levels, in many cybersecurity related projects covering, Healthcare security, Industrial IoT Cybersecurity, Threat Detection Systems and Penetration Testing. He works on subjects like privacy and risk evaluations, applied cryptography, IoT device design, IoT security testing and certification, etc. In addition to his Senior analyst role, he represents Red Alert Labs on security and compliance working groups of various consortiums like Industrial Internet Consortium IIC, IoT Security Foundation IoTSF and IoXT. Isaac attended numerous conferences and was a co‐speaker at the International Cryptographic Module Conference in Ottawa 2018. He has also attended the Certified Ethical Hacker and CISSP trainings at Koenig institute, India in addition to the practical IoT hacking training with payatu at Hack‐In‐Paris. Isaac has worked previously for eTC (Electronic Test Company), Zenith Bank, and DiagnosticaStago.


Abstract: When the ARPANET, world wide web (WWW) and many other early technologies were created, the world was, in many ways, a much more different place from how we know it today. Sadly, its developers failed to predict, the possibility that the internet could also serve malicious uses as it did for legitimate users. This has forced us to develop (with cost implication) extra features to ensure that our communications are protected today. Thus, technology vendors are usually caught in the dilemma of lower cost vs security. Cost almost always wins and this leaves us with vulnerabilities that go unnoticed, usually until an attacker exploits them. Today the Internet of Things, the network connection of objects around us, though not fully adopted, is showing great promise thus far, attracting the fancy of ordinary users, developers, data analysts and manufacturers for different reasons. To pre‐empt malicious use of IoT and respond to vendor’s dilemma, we must answer an important question: “how do we design ‘smart’ devices to be affordable and secure at the same time”. This tutorial will show you practically, how to conduct a risk‐based conception and design of a cheap and secure IoT device that is fit for purpose.