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We are happy to provide you with this program where you can expand your knowledge of IoT. You are most welcome to join us.</p> <h3>Upcoming Webinars</h3> <p style="clear: both;"> </p> <p><strong>Full-Duplex Integrated Sensing and Communications for Distributed Systems</strong></p> <p data-renderer-start-pos="195"><strong>Wednesday, March 5, 2025 11:00 AM EST</strong></p> <p data-renderer-start-pos="235"><strong>Presenter:</strong> Dr. Kumar Mishra, United States <span class="acronym-highlight">DEVCOM</span> Army Research Laboratory</p> <p data-renderer-start-pos="312"><strong>Abstract:</strong> As a next-generation wireless technology, the in-band full-duplex (<span class="acronym-highlight">IBFD</span>) transmission enables simultaneous transmission and reception of signals over the same frequency, thereby doubling spectral efficiency. Further, a continuous, up-scaling of wireless network carrier frequencies arising from ever-increasing data traffic is driving research on integrated sensing and communications (<span class="acronym-highlight">ISAC</span>) systems. In this context, we study the co-design of common waveforms, precoders, and filters for an IBFD multi-user (<span class="acronym-highlight">MU</span>) multiple-input multiple-output (<span class="acronym-highlight">MIMO</span>) communications with a distributed MIMO radar. In particular, we tackle a multi-target detection and localization problem in this distributed <span class="acronym-highlight">FD</span> ISAC framework. This co-design problem that includes practical MU-MIMO constraints on power and quality of service is highly non-convex. We solve this problem using an alternating optimization framework and also propose a low-complexity procedure based on Barzilai–Borwein gradient algorithm to obtain the design parameters and mixed-integer linear program for distributed target localization. Numerical experiments demonstrate the feasibility and accuracy of multi-target sensing of the distributed FD ISAC system. Toward the end of the talk, we briefly touch upon our research on other ISAC topics.</p> <p><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Kumar-Vijay-Mishra.jpg" alt="Kumar Vijay Mishra" /><strong>Biography:</strong> Kumar Vijay Mishra obtained a Ph.D. in electrical engineering and M.S. in mathematics from The University of Iowa in 2015. He is a Senior Fellow at the United States DEVCOM Army Research Laboratory and a Research Scientist at the University of Maryland, College Park. He is also a Technical Adviser to radar startups Hertzwell and Aura Intelligent Systems and an Honorary Research Fellow at the University of Luxembourg. A recipient of numerous prestigious awards, including the <span class="acronym-highlight">IEEE</span> Signal Processing Society's Pierre-Simon Laplace Early Career Technical Achievement Award (2024), he has served as a Distinguished Lecturer for several IEEE Societies such as <span class="acronym-highlight">AESS</span>, <span class="acronym-highlight">GRSS</span>, ComSoc, and <span class="acronym-highlight">VTS</span>. He has held editorial and leadership roles in IEEE technical groups and co-edited several books on radar and signal processing. His research interests include radar systems, signal processing, remote sensing, and electromagnetics.</p> <p><a href="https://zoom.us/webinar/register/WN_fjX15-RwSmq2ZHv191NWmQ" target="_blank" rel="noopener">Register for the Webinar</a></p> <p> </p> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true" data-pm-slice="1 1 []"><strong>Edge AI Unleashed: Boosting IoT Performance with Breakthrough Processing-in-Memory Technologies</strong></p> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true"><strong>Friday, April 4, 2025 10:00 AM EDT</strong></p> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true"><strong>Presenter:</strong> Dr. Gabriel Falcao, Associate Professor with the Department of Electrical and Computer Engineering (ECE), University of Coimbra, and a Senior Researcher with the Instituto de Telecomunicações.</p> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true"><strong>Abstract:</strong> The data movement bottleneck imposes severe limitations on the performance and energy consumption of modern computing systems, including those at the extremes: in data centers and at the edge. Recent studies have shown that more than half of the energy for common workloads is spent moving data. Processing-in-Memory (PiM) has emerged as a way to mitigate the data movement bottleneck while meeting the stringent performance, energy efficiency, and accuracy requirements of many applications, particularly those running at the edge. </p> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true">This talk explores a range of cutting-edge PiM technologies, with a special focus on pLUTo, a novel approach that introduces minimal modifications to DRAM substrates to achieve substantial gains in performance and energy efficiency. Experimental results demonstrate the effectiveness of pLUTo and other PiM technologies across diverse workloads, including AI algorithms running at the edge.</p> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true"><strong>Biography:</strong> Gabriel Falcao (S’07–M’10–SM’14) received a Ph.D. degree from the University of Coimbra in 2010. From 2011 to 2012 and again in 2017, he was a Visiting Professor with EPFL in Switzerland. He spent the summer of 2018 as a Visiting Academic with ETHZ in Switzerland. He is currently an Associate Professor with the Department of Electrical and Computer Engineering (ECE), University of Coimbra, and a Senior Researcher with the Instituto de Telecomunicações. From 2021 until 2024, Gabriel was the coordinator of the Ph.D. program in ECE, which he renewed to help create the new Ph.D. in Electrical Engineering and Intelligent Systems at the University of Coimbra. Under this role, he also coordinated the update of the dual Ph.D. degree protocol between the University of Coimbra and Carnegie Mellon University. His research interests include parallel computer architectures, GPU- and FPGA-based accelerators, processing-in-memory systems, quantum computing, energy-efficient processing, and compute-intensive signal processing applications related to communications and medical imaging problems. He published over 50 journals, 80 international conference papers, and 1 international patent licensed to the medical industry. Gabriel was the PI and co-PI of several projects including two from the Portuguese funding agency FCT and one from a Google Faculty Research Award (together with J. Barreto), funded by Google Inc. In 2015, he became the PI of a CUDA Research Center sponsored by NVIDIA to the University of Coimbra and Instituto de Telecomunicações. He is a member of the IEEE Signal Processing Society, IEEE Computer Society Technical Community on Microprogramming and Microarchitecture, a Senior member of IEEE, and a Full member of the HiPEAC Network of Excellence. Gabriel was the General Co-Chair of IEEE SiPS in 2020 and Local Chair of Euro-Par 2021. He serves as an Associate Editor for IEEE Micro magazine and IEEE Transactions on Signal Processing. He also served as Guest Editor of three special issues at IEEE and Springer journals, including the most recent one at IEEE Micro on “The Past, Present, and Future of Warehouse-scale Computing".</p> <p data-prosemirror-content-type="node" data-prosemirror-node-name="paragraph" data-prosemirror-node-block="true" data-pm-slice="1 1 ["orderedList",{"order":1},"listItem",null]"><a href="https://zoom.us/webinar/register/WN_N4wEDBYHSlybcWJgHLCOdg" target="_blank" rel="noopener">Register for the Webinar</a></p> <hr style="clear: both;" /> <h3>Previous Webinars</h3> <p> </p> <p><strong>The Long Trajectory to Trajectory Privacy</strong></p> <p><strong>Thursday, February 6, 2025 8 AM Sydney, Australia Time; Wednesday, February 5, 2025 4:00 PM EST</strong></p> <p><strong>Presenter: </strong>Dr. Salil Kanhere, Professor at the School of Computer Science and Engineering at UNSW Sydney, Australia</p> <p><strong>Abstract: </strong>Our daily movements disclose plenty of sensitive information about us - from our habits to religious and political opinions. At the same time, location trajectories are helpful for various applications such as city planning, pandemic control, or marketing. Therefore, numerous approaches for protecting the privacy of trajectory data have been proposed. Nevertheless, recent works show that we are still far from our goal of releasing high-quality trajectories for arbitrary applications under strong guarantees. This talk will provide an overview of location trajectories and their privacy protection. First, we explore whether existing protection mechanisms hold up to their promises in the age of AI. Through a deep learning-based reconstruction attack, we show that even mechanisms using the de facto privacy standard, differential privacy, might be vulnerable. Based on this, we discuss a framework and goals for the design of satisfactory privacy protection mechanisms. As we find that the existing protection mechanisms struggle from a restrictive privacy-utility trade-off, we explore whether the generation of fake data represents the solution. Through a large-scale experimental study, we examine generative models for trajectory data. While their utility is impressive, this research direction still requires future work to satisfy all the set goals.</p> <p><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Salil-Kanhere.jpg" alt="Salil Kanhere" /><strong>Biography:</strong> Salil Kanhere is a Professor in the School of Computer Science and Engineering at UNSW Sydney, Australia and is affiliated with the UNSW Institute of Cybersecurity (IFCYBER). His research interests span various aspects of cyber security, mobile computing, IoT, blockchain, and applied machine learning. He has published over 350 peer-reviewed articles and is leading several government and industry-funded research projects on these topics. He received the Friedrich Wilhelm Bessel Research Award (2020) and the Humboldt Research Fellowship (2014) from the Alexander von Humboldt Foundation in Germany. He is an ACM Distinguished Member, an IEEE Senior Member and an IEEE Computer Society Distinguished Visitor. He has held visiting positions at RWTH Aachen, I2R Singapore, Technical University Darmstadt, University of Zurich and Graz University of Technology. He serves as the Editor in Chief of the Ad Hoc Networks journal and Associate Editor of IEEE Transactions On Network and Service Management, Computer Communications, and Pervasive and Mobile Computing. He has served on the organizing committee of several IEEE/ACM international conferences and has co-authored two books.</p> <a href="https://drive.google.com/drive/folders/1VgBYYSH8dN2BnNE6HWJ-sgyFb36pxam2?usp=sharing" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a><br /> <p style="clear: both;"> </p> <h4>Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks</h4> <p><strong>Friday, January 17, 2025; 9:00 PM Singapore Time; 8:00 AM EST</strong></p> <p><strong>Presenter:</strong> Dr. Dusit Niyato, President's Chair Professor, Nanyang Technological University, Singapore</p> <p><strong>Abstract:</strong> The evolution of generative artificial intelligence (GAI) has driven revolutionary applications like ChatGPT. The proliferation of these applications is underpinned by the mixture of experts (MoE), which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE's efficiencies, GAI still faces challenges in resource utilization when deployed on local user devices. Therefore, we first propose mobile edge networks supported MoE-based GAI. Rigorously, we review the MoE from traditional AI and GAI perspectives, scrutinizing its structure, principles, and applications. Next, we present a new framework for using MoE for GAI services in Metaverse. Moreover, we propose a framework that transfers subtasks to devices in mobile edge networks, aiding GAI model operation on user devices. Moreover, we introduce a novel approach utilizing MoE, augmented with Large Language Models (LLMs), to analyze user objectives and constraints of optimization problems based on deep reinforcement learning (DRL) effectively. This approach selects specialized DRL experts, and weights each decision from the participating experts. In this process, the LLM acts as the gate network to oversee the expert models, facilitating a collective of experts to tackle a wide range of new tasks. Furthermore, it can also leverage LLM's advanced reasoning capabilities to manage the output of experts for joint decisions. Lastly, we insightfully identify research opportunities of MoE and mobile edge networks.</p> <p><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Dusit-Niyato.jpg" alt="Dusit Niyato" /><strong>Biography: </strong>Dr. Dusit Niyato is currently a President's Chair Professor in the College of Computing & Data Science (CCDS), Nanyang Technological University, Singapore. He received Ph.D. in Electrical and Computer Engineering from the University of Manitoba, Canada in 2008. Dusit's research interests are in the areas of mobile generative AI, edge intelligence, quantum computing and networking, and incentive mechanism design.</p> <a class="css-1rn59kg" href="https://drive.google.com/drive/folders/1HYYsNbFipGSp_khiASVJLu_jTJahTABu" target="_blank" rel="noopener" data-testid="link-with-safety" data-renderer-mark="true"><span style="text-decoration: underline;" data-renderer-mark="true">View the Webinar Recording and Presentation</span></a><br /> <p style="clear: both;"> </p> <h4>Edge Intelligence for the Next-generation IoT Systems </h4> <p><strong>Friday, December 6, 2024; 5:00 pm CET; 11:00 am EST</strong></p> <p><strong>Presenter: </strong>Dr. Giancarlo Fortino, Professor of Computer Engineering at the Dept of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy</p> <p><strong>Abstract:</strong> 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 to explore the many facets of EI from different perspectives and different degrees of detail.</p> <p>During the webinar, Dr. Fortino will present the wide landscape of EI by providing a systematic analysis of the state-of-the-art manuscripts in the form of a tertiary study according to the guidelines of the PRISMA methodology. A comparison framework is provided and sound research questions are 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.</p> <p>Dr. Fortino will also discuss the EI research in the context of the device-edge-cloud continuum paradigm developed in the Horizon Europe project “MLSysOps” and in the PRIN “COMMON-WEARS” project along with their vision of Digital Twin enabled by EI and applied to Smart City as a new enabler.</p> <p>And finally, he will share a recently introduced methodology, EdgeMiningSim, a simulation-driven methodology inspired by software engineering principles for enabling IoT Data Mining/Machine Learning. This methodology drives domain experts to disclose actionable knowledge, namely descriptive or predictive models for taking effective actions in the constrained and dynamic IoT scenario.</p> <p><strong><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Giancarlo-Fortino.jpg" alt="Giancarlo Fortino" />Biography: </strong>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. <em>He is Highly Cited Researcher 2020-2023 in Computer Science by Clarivate</em>. He had 25+ highly cited papers in WoS, and h-index=83 with 26000+ citations in Google Scholar. His research interests include wearable computing systems, e-Health, Internet of Things, and agent-based computing. He is author of 700+ 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.</p> <a href="https://drive.google.com/drive/folders/1cL-BzWx40DkBU77-BsxJCXL354fA9yFb?usp=drive_link" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a><br /> <p> </p> <h4>Towards Large-scale Heterogenous Industrial IoT Systems: Protocol Design, Resource Management and Applications</h4> <p><strong>Thursday, November 21, 2024; 11:00-12:00 EST</strong></p> <p><strong>Presenter:</strong>  Dr. Song Han, Associate Professor and Director of Senior Design in the School of Computing at the University of Connecticut</p> <p><strong>Abstract:</strong> Industrial Internet-of-Things (IIoT) has been making its way into a wide range of industrial applications in recent years. An IIoT paradigm aims at creating a unified sensing, computing and control framework to interconnect all the industrial assets with information systems and business processes, and to streamline the manufacturing process and lead to optimal industrial operations. Because IIoT applications are distinguished from consumer IoT by 1) stringent performance guarantees, and 2) certifiable reliability and robustness, research is needed on both real-time communication technologies and resource management techniques to enable effective sensing and control operations in dynamic and harsh environments.</p> <p>In this talk, Dr. Han will share his R&D experiences on real-time communication protocol designs to meet the stringent performance requirements for a range of IIoT applications.  Building on these protocol designs, he will further present a novel partition-based real-time resource management framework for IIoT systems to achieve dynamic and distributed network resource management in the presence of unpredictable internal and external disturbances.  These ongoing efforts are also being integrated together towards the design of a large-scale heterogenous community research infrastructure to support a broad range of researchers on IIoT-related research projects.</p> <p><strong><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Song-Han.jpg" alt="Song Han" />Biography</strong><strong>:</strong> Dr. Han is an Associate Professor and Director of Senior Design in the School of Computing at the University of Connecticut (UConn).  He received the B.S. degree from Nanjing University in 2003, the M.Phil. degree from the City University of Hong Kong, and the Ph.D. degree from the University of Texas at Austin in 2012, all in Computer Science. Dr. Han’s research interests include industrial IoT systems, cyber–physical systems, real-time and embedded systems, and wireless networks.  He has published over 180 scholarly work, including five outstanding/best paper awards/nominations from top-tier conferences in real-time and embedded systems area. He is currently an Associate Editor for ACM Transactions on Cyber-Physical Systems (TCPS), an executive member for ACM Special Interest Group on Embedded Systems (SIGBED) and the industrial liaison for the IEEE IES Technical Committee on Cloud and Wireless Systems for Industrial Applications (CWAIA). His research is supported by NSF, NASA, AFRL, DOE, DOT, NIH, Emerson, Texas Instruments, Microsoft Research and internal grants from UConn. </p> <a href="https://drive.google.com/drive/folders/1y0y7siVbe280Ulh5ZohyFt6Fb8F9n7cN" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a><br /> <p> </p> <h4>Swarm Localization and Sensing – from Classroom to Future Space Exploration Missions</h4> <p><strong>Friday, October 11, 2024; 6:00-7:00pm CEST: 12:00-1:00pm EDT</strong></p> <p><strong>Presenter:</strong> Dr. Siwei Zhang, Institute of Communications and Navigation, German Aerospace Center (DLR)</p> <p><strong>Abstract: </strong>Robotic swarm is an emerging technology for rapidly sensing dynamic physical processes over large area both on Earth and in future space exploration missions. The nodes in swarm compose a self-organized radio network, providing precise time and position references without additional infrastructures like GPS or base stations. Each node is additionally equipped with environmental sensors, for example a photonic sensor to sense the illumination in caves beneath the lunar surface, a hydrogen sulfide sensor to explore the volcanic activities, or a methane sensor to track organic traces. More importantly, the swarm has a unique capability of formation optimization. Hence, nodes collaboratively choose their trajectories, so that the spatial-temporal information is gathered most efficiently.</p> <p>At the German Aerospace Center (DLR), we design compact and portable nodes with ultra-wideband (UWB) technology for decentralized timing, position and environmental sensing. These nodes can be easily carried or deployed by robots and are thus suitable for technology demonstrations in space-analog missions. These nodes are also served as educational platforms, assisting high school through doctoral students to gain intuitions and in-depth knowledge in signal processing for communications, localization, decentralized estimation, sensor fusion, etc.</p> <p>In this webinar,  Dr. Zhang will start with the fundamentals of swarm localization and sensing, then present the building steps of a swarm system, and finally showcase the usage of the swarm in DLR’s space-analog missions, for example on volcanos and in lava caves.</p> <p><strong><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Siwei-Zhang.jpg" alt="Siwei Zhang" />Biography: </strong>Siwei Zhang received his B.Sc. in electrical engineering from Zhejiang University, China, in 2009, his M.Sc. in communication engineering from the Technical University of Munich, Germany, in 2011, and his Dr.-Ing. (Ph.D.) in electrical engineering from the University of Kiel, Germany, in 2020.</p> <p>He has been a Researcher with the Institute of Communications and Navigation, German Aerospace Center (DLR) since 2012 and a Lecturer at Technical University of Munich and University of Kiel since 2022. He received the 2021 DLR Science Award and Best Paper/Presentation Awards at ION GNSS+ 2015, 2022, IEEE CCNC-RoboCom 2021, and IEEE AeroConf 2023. He is a Member of the SPS Applied Signal Processing Systems Technical Committee and Autonomous Systems Initiative and a Special Session Co-organizer at ICAS 2021, ICASSP 2023, 2024 and SiPS 2024. His research interests lie in statistical signal processing for wireless communications, navigation and sensing, particularly for multi-agent systems. </p> <p data-pm-slice="1 1 []"><a href="https://drive.google.com/drive/folders/1DP2xc95xYRXJ-dgsfxYMi9HGMlR8A7Ue?usp=sharing" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a></p> <p> </p> <h4>Ubiquitous Mechanical Energy Harvesting and Mechanosensing for IoT Applications</h4> <p><strong>Saturday, September 14, 2024; 12:00 - 1:00 AM Beijing; Friday, September 13,12:00 - 1:00 PM EDT</strong></p> <p><strong>Presenter: </strong>Dr. Wenbo Ding, Associate Professor in the Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Beijing, China</p> <p data-renderer-start-pos="319"><strong>Abstract:</strong> The development of the Internet of Things (IoT) has been transforming many traditional industries and brought huge convenience to modern society. However, it also raised great challenges to the sustainable power supplies for dense sensors. The triboelectric nanogenerator (TENG), by utilizing the triboelectric effect and electrostatic induction, is an effective mechanism to harvest ambient low-frequency mechanical energy such as vibration, human kinetic energy, water wave, etc. Dr. Ding will share his work exploring the “Information-Energy” dual-property feature of mechanical stimuli with TENG and other tools. He will discuss the fundamental modeling and performance optimization of TENG as the power source for electronics in IoT and will introduce the design and implementation of emerging energy-efficient sensing systems with the help of advanced signal processing techniques such as compressive sensing, machine learning, image processing, etc. Such works will have broad applications ranging from structural health monitoring, and human-machine interface to wearable electronics.</p> <p><strong><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Wenbo-Ding.jpg" alt="Wenbo Ding" />Biography:</strong> Dr. Wenbo Ding is an associate professor and leads the Smart Sensing and Robotics (SSR) group in Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University. He received B.E and Ph.D. degrees (both with the highest honors) from the Department of Electronic Engineering, Tsinghua University, Beijing, China, in 2011 and 2016, respectively, and then worked as a postdoctoral research fellow in Materials Science and Engineering at Georgia Tech, Atlanta, GA, under the supervision of Professor Z. L. Wang from 2016 to 2019. He has published over 70 journal and conference papers and received many prestigious awards, including the National Early-Career Award, the IEEE Scott Helt Memorial Award for the best paper published in IEEE Transactions on Broadcasting, the 2019 and 2022 Natural Science Award (Second Prize) from Institute of Electronics, the Gold Medal and Special Prize at the 47th International Exhibition of Inventions of Geneva. His research interests are diverse and interdisciplinary, which include self-powered sensors, energy harvesting, wearable devices for health and soft robotics with the help of signal processing, machine learning and mobile computing. He is the associate editor of DSP and the leading guest editor of IEEE JSTSP.</p> <p><a href="https://drive.google.com/drive/folders/1ziAOQzWIXausLmXn2n5Vy0TNZqTtvlCW?usp=drive_link" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a></p> <p> </p> <h4>Living Labs and Digital Twin for Smart Energy Networks</h4> <p><strong>Monday, June 17, 2024; 8:00-9:00 PM UK; 3:00 - 4:00 PM EDT</strong></p> <p><strong>Presenter: </strong>Prof. Zhong Fan, Professor in Net Zero Energy Systems; Director of the Centre for Smart Grid; Assistant Director of the Global Systems Institute, University of Exeter, UK</p> <p data-renderer-start-pos="319"><strong>Abstract:</strong> Professor Fan will introduce the £16M SEND (Smart Energy Network Demonstrator) Project and some of the research work on the applications of digital and AI Technologies to smart energy and sustainability. The aim is to demonstrate that AI and ICT are powerful tools to combat climate change and help us to achieve net zero.</p> <p data-renderer-start-pos="1314"><strong><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Zhong-Fan.jpg" alt="Zhong Fan" />Biography: </strong>Since July 2023, Prof. Zhong Fan has been the Professor in Net Zero Energy Systems and Head of the Centre for Smart Grid at the University of Exeter, UK. From Feb 2017 to June 2023, he was Professor in Computer Science and Director of the SEND Research Centre (Smart Energy Network Demonstrator) at Keele University. Prior to that, he was Chief Research Fellow at Toshiba Research Europe in the UK, leading R&D teams on 5G, IoT and smart grid. He was an advisory board member of the IEEE ComSoc Industry Community on IoT and Executive Member of the China-UK Communications Institute.</p> <p><a href="https://drive.google.com/drive/folders/1lzk646gnexzWxQm2aKVNmgvCCVtssI1r?usp=sharing" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a></p> <p> </p> <h4>QoS-Aware Resource Allocation in Internet of Drones</h4> <p><strong>Friday, 12 April 2024; 11:00am-12:00pm CDT; 12:00pm-1:00pm EDT</strong></p> <p><strong>Presenter:</strong> Dr. Jingjing Yao, Assistant Professor in the Department of Computer Science at Texas Tech University, Lubbock, TX, USA</p> <p data-renderer-start-pos="319"><strong>Abstract:</strong> The Internet of Drones (IoD) integrates drones with the Internet of Things system, enabling these aerial devices to autonomously collect, process, and share information for tasks ranging from surveillance and delivery to environmental monitoring. Despite its potential, the IoD faces challenges including the efficient utilization of limited computing and communication resources, battery conservation, and the protection of data privacy. Effective resource allocation stands as a key strategy in navigating these challenges, enabling the careful distribution of scarce resources to maximize network performance and ensure a high Quality of Service (QoS). This talk will highlight the role of technologies like deep reinforcement learning and federated learning in optimizing the allocation of computing and communication resources. These technologies are poised to overcome the drone battery limitations, boosting network reliability, efficiency, and data privacy in the process.</p> <p data-renderer-start-pos="1314"><strong><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Jingjing-Yao.jpg" alt="Jingjing Yao" />Biography:</strong> Dr. Yao is an Assistant Professor in the Department of Computer Science at Texas Tech University, Lubbock, TX, USA. She earned her Ph.D. in Computer Engineering from the New Jersey Institute of Technology, Newark, NJ, USA. Dr. Yao's research is primarily focused on the Internet of Things, Internet of Drones, applied machine learning in communication and networking, federated learning, mobile edge computing/caching, cybersecurity, and energy harvesting. She has authored and co-authored numerous papers published in IEEE journals and conference proceedings. She is a founding member of the Space and Aerial Systems for Internet of Things (SAS-IoT) Working Group in the IEEE Internet of Things (IoT) Technical Community. She has been recognized through several awards, including the 2016 NJIT Fellowship, the 2021 Hashimoto Prize, and the 2024 Outstanding Dissertation Award.</p> <p><a href="https://drive.google.com/drive/folders/1hhZRK6h4OMYBqAi14hjMm_hW4_w8jdVB?usp=sharing" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a></p> <p> </p> <h4>Random Access for Machine-to-Machine Communications: Challenges and Prospects</h4> <p><strong>Friday, 15 March 2024; 8:00-9:00pm Hong Kong (GMT+8); 8:00-9:00am EST</strong></p> <p><strong>Presenter:</strong> Dr. Lin Dai, Professor of Department of Electrical Engineering of City University of Hong Kong</p> <p data-renderer-start-pos="739"><strong>Abstract:</strong> With the new wave of digital revolution, wireless communication networks are experiencing a radical paradigm shift from the conventional human-to-human (H2H) communications to machine-to-machine (M2M) communications. To facilitate the massive access of machine-type devices, random access is expected to play a crucial role in the next-generation M2M communications. Thanks to its distributed nature, random access has found wide applications to various wireless networks including 5G cellular networks and Wi-Fi networks.</p> <p data-renderer-start-pos="1274">In stark contrast to the wide applications, the theory of random access is much less developed. Analytical models are often tailored for specific performance metrics or access protocols, where differences in modeling assumptions and definitions have led to inconsistent findings. Despite continuous attention for half a century, fundamental issues remain unresolved. In this talk, I will start by reviewing the challenges of establishing a coherent theory of random access, and then introduce our recently proposed unified analytical framework for random access with case studies to show the practical implications of the analysis for the optimal access design of M2M communications. I will conclude the talk by highlighting the challenges and oportunities for the distributed access design of next-generation communication networks.</p> <p data-renderer-start-pos="2110"><strong><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Lin-Dai.jpg" alt="Lin Dai" />Biography:</strong> Dr. Lin Dai received the Ph.D. degree from Tsinghua University, China. She is now a full professor of Department of Electrical Engineering of City University of Hong Kong. She has broad interests in communications and networking theory, with special interests in wireless communications. Her recent research work focuses on modeling, performance analysis and optimal access design of next-generation mobile communication systems. She was a co-recipient of the Best Paper Award at the IEEE WCNC 2007 and the IEEE Marconi Prize Paper Award in 2009. She received The President's Award of City University of Hong Kong in 2017.</p> <p data-renderer-start-pos="2746"><a href="https://drive.google.com/drive/folders/16_YZjZeGvsFqXrYsjfnKqqCaEUIUEMZ7" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a></p> <p> </p> <h4>Advanced Digitalization of Industries: from Industrial IoT to Cloud/Fog Automation</h4> <p><strong>Wednesday, 21 February 2024; 2:00 PM Stockholm, Sweden (8:00 AM EST)</strong></p> <p><strong>Presenter:</strong> Dr. Zhibo Pang, Senior Principal Scientist, ABB Corporate Research Sweden and an Adjunct Professor, KTH Royal Institute of Technology, Sweden.</p> <p><strong>Abstract:</strong> Industrial IoT has been playing important roles in industry digitalization, going beyond the data collection and condition monitoring. Inspired by the fast evolution of 5G, cloud computing, and artificial intelligence, the manufacturing industry are looking for new generation automation systems that can be deployed on open, flexible, and IT-style communication and computing infrastructures. However, major technical challenges must be solved in terms of determinism before the expected benefits are achievable, especially for field level regulatory control. In this presentation, Dr. Pang will share what we have done towards the vision of doing field level regulatory control over cloud and fog computing and wireless networks. Dr. Pang will show the significant improvements in the latency and reliability of the latest wireless technologies such as 5G and WiFi6, as well as the insufficiencies to support the real-time control tasks. More importantly, our preliminary progress suggests, it will be much easier to solve the overall challenge if we can tune the control model according to the latency pattern of the networks. Despite its effectiveness in our specific use case, the generalizability of the proposed “latency-aware control” or “control-computing-communication co-design” is still a big research question. We hope to trigger more discussions on this topic by this talk.</p> <p><strong><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Zhibo-Pang.jpg" alt="Zhibo Pang" />Biography: </strong>Dr. Zhibo Pang, MBA &amp; PhD, is currently a Senior Principal Scientist at ABB Corporate Research Sweden, and Adjunct Professor at KTH. He is a Member of IEEE IES Industry Activities Committee, Vice-Chair of the TC on Cloud and Wireless Systems for Industrial Applications, and Co-Chair of the TC on Industrial Informatics. He is Associate Editor of IEEE TII, IEEE JBHI, and IEEE JESTIE. He was General Chair of IEEE ES2017, General Co-Chair of IEEE WFCS2021. He was awarded the “Inventor of the Year Award” by ABB Corporate Research Sweden, three times in 2016, 2018, and 2021 respectively. He works on enabling technologies in electronics, communication, computing, control, artificial intelligence, and robotics for Industry4.0 and Healthcare4.0.</p> <p data-renderer-start-pos="800"><a href="https://www.kth.se/profile/zhibo" target="_blank" rel="noopener">Homepage<br /></a><a href="https://www.linkedin.com/in/zhibopang/" target="_blank" rel="noopener">Linkedin Profile</a></p> <p style="clear: both;"><a href="https://drive.google.com/drive/folders/1-0fuE0lceHh6D49i8GPuE1hxQKTXj0Dm?usp=sharing" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a></p> <p> </p> <h4 style="clear: both;">When AI Meets IoT: Motivations, Challenges, and Applications</h4> <p><strong>Friday, 15 December 2023; 9:00am-10:00am Sydney, Australia (GMT +11:00) (14 December 2023; 5:00pm-6:00pm EDT)</strong></p> <p><strong>Presenter: </strong>Professor Wei Xiang, Cisco Research Chair of AI and IoT and Director of the Cisco-La Trobe Centre for AI and IoT at La Trobe University, Australia</p> <p><strong>Abstract: </strong>Artificial Intelligence of Things (AIoT) is a newly emerging technology that combines IoT and AI technologies to enable decision making and analytics at IoT devices. IoT enables networks of physical objects that are equipped with sensors, software, and other technologies to exchange data with other devices and systems over the internet, while AI enables data analytics and automated decision making. This talk will start with the motivations of combining AI and IoT technologies as well as the associated challenges. Then Prof. Wei Xiang will talk about his experience in setting up Australia’s first accredited IoT Engineering program at James Cook University, as well as establishing Australia’s only industry-sponsored research centre that specializes in synergizing between AI and IoT technologies at La Trobe University. Before concluding the talk, Prof. Wei Xiang will talk about a wide range of applications and use cases his AIoT Centre has been working on in Australia.</p> <p><strong><a href="https://zoom.us/webinar/register/WN_0OJm_4_eTHGYO8b17vVEAg" target="_blank" rel="noopener"><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Wei-Xiang.jpg" alt="Wei Xiang" /></a>Biography:</strong> Professor Wei Xiang is Cisco Research Chair of AI and IoT and Director of the Cisco-La Trobe Centre for AI and IoT at La Trobe University. Previously, he was Foundation Chair and Head of Discipline of IoT Engineering at James Cook University, Cairns, Australia. Due to his instrumental leadership in establishing Australia’s first accredited Internet of Things Engineering degree program, he was inducted into Pearcy Foundation’s Hall of Fame in October 2018. He is an elected Fellow of the IET in the UK and Engineers Australia. He received the TNQ Innovation Award in 2016, and Pearcey Entrepreneurship Award in 2017, and Engineers Australia Cairns Engineer of the Year in 2017. He was a co-recipient of four Best Paper Awards at WiSATS’2019, WCSP’2015, IEEE WCNC’2011, and ICWMC’2009. He has been awarded several prestigious fellowship titles. He was named a Queensland International Fellow (2010-2011) by the Queensland Government of Australia, an Endeavour Research Fellow (2012-2013) by the Commonwealth Government of Australia, a Smart Futures Fellow (2012-2015) by the Queensland Government of Australia, and a JSPS Invitational Fellow jointly by the Australian Academy of Science and Japanese Society for Promotion of Science (2014-2015). He was the Vice Chair of the IEEE Northern Australia Section from 2016-2020. He was an Editor for IEEE Communications Letters (2015-2017), and is currently an Associate Editor for IEEE Communications Surveys & Tutorials, IEEE Internet of Things Journal, IEEE Access, and Nature journal of Scientific Reports. He has published over 250 peer-reviewed papers including 3 books and 200 journal articles. He has severed in a large number of international conferences in the capacity of General Co-Chair, TPC Co-Chair, Symposium Chair, etc. His research interests include¬¬ the Internet of Things, wireless communications, machine learning for IoT data analytics, and computer vision. </p> <p data-pm-slice="1 1 []"><a href="https://drive.google.com/drive/folders/1T11QK5dF21slMc46yMgLIEWKdhlE8c8t" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a></p> <p> </p> <h4 style="clear: both;">IoT for Rural and Extreme Environments</h4> <p><strong>Friday, 10 November 2023; 9:00-10:00am EST</strong></p> <p><strong>Presenter:</strong> Pietro Manzoni, Professor of Computer Engineering at the "Universitat Politècnica de València," Spain</p> <p><strong>Abstract: </strong>The Internet of Things (IoT) has the potential to collect real-time data from sensors, cameras, and other devices, making it beneficial for various applications. However, rural and extreme environments, such as remote areas, deserts, forests, or mountains, lack reliable communication infrastructure, presenting several technical challenges that must be overcome to successfully use IoT in such settings. These challenges include, for example, limited connectivity, data integration, data quality, security, interoperability, and energy efficiency. Addressing these technical challenges requires a multidisciplinary approach involving data science, computer engineering, and cybersecurity.</p> <p>This talk will present some of the solutions explicitly being developed and deployed in the context of environmental intelligence, i.e., when IoT and digital technologies are used to gain insights into human impacts on the environment and develop strategies to mitigate or reverse the effects of climate change. The talk will present how edge computing and low power wide area networks (LPWAN) are used. Emphasis will be given to applying ML techniques to AI, usually referred to as AIoT, with novel techniques like TinyML.</p> <p><strong><a href="https://zoom.us/webinar/register/WN_0OJm_4_eTHGYO8b17vVEAg" target="_blank" rel="noopener"><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/education/Pietro-Manzoni.jpg" alt="Pietro Manzoni" /></a>Biography:</strong> Pietro Manzoni is a computer engineering professor at the "Universitat Politècnica de València," Spain. He received a master's degree in Computer Science from the "Università degli Studi" of Milan, Italy, in 1989 and a Ph.D. in Computer Science from the "Politecnico di Milano," Italy, in 1995. From November 1992 to February 1993, he was an intern at Bellcore Labs, Red Bank, New Jersey, USA. From February 1994 to November 1994, he was a visiting researcher at the ICSI (International Computer Science Institute) Berkeley, California, USA.</p> <p>His research focuses on using Mobile Wireless Networks to create dynamic systems. Currently, he is developing solutions for the Internet of Things using LPWAN networks and Pub/Sub systems. These solutions have various applications, including environmental intelligence by integrating TinyML-based solutions, sustainable and green IoT, and Smart Tourism. Additionally, he is interested in exploring different aspects of network pluralism and finding ways to provide integrated connectivity in the edge-cloud continuum.</p> <p>He is the coordinator of the [<a href="http://grc.webs.upv.es/" target="_blank" rel="noopener">Computer Networks Research Group GRC</a>)], a senior member of the IEEE, and a member of the [<a href="http://ieee-hyperintelligence.org/index" target="_blank" rel="noopener">IEEE Technical Committee on Hyper-Intelligence</a>], the [<a href="https://www.ieee-metacom.org/sigmeta/" target="_blank" rel="noopener">IEEE SIG on Metaverse</a>], and the [<a href="https://www.sigcas.org/" target="_blank" rel="noopener">ACM SIGCAS - Computers and Society</a>].</p> <p><a href="https://drive.google.com/drive/folders/1sezXSlbxzzkY77Le2oopvlE2M3vKj81a?usp=sharing" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a></p> <p> </p> <h4 style="clear: both;">Event-Based Reinforcement Learning for Cyber-Physical Energy Systems – Smart Buildings, Smart Grid, and Smart Cities</h4> <p><strong>Wednesday, 18 May 2022</strong></p> <p><strong>Presenter:</strong> Professor Qing-Shan (Samuel) Jia, The Center for Intelligent and Networked Systems, Tsinghua University, Beijing, China</p> <p><strong>Abstract: </strong>Cyber-physical energy system (CPES) is where information and energy merge together to improve the overall system performance including economic, comfort, and safety aspects. Artificial intelligence which is enabled by internet of things (IoT), big data, and cloud computing, has a big role in the optimization of CPES. In this talk, we focus on event-based reinforcement learning (eRL) which makes decisions according to events instead of states. This method provides a scalable solution for large-scale multi-stage decision-making problems in which an accurate model may not be available. The performance of this method will be demonstrated by examples in smart buildings, smart micro-grid of buildings, and smart cities, and in particular on the problem of stochastic matching between the renewable power generation and the uncertain charging demand from the plug-in electric vehicles (PHEVs) in a city. We will also discuss extensions of this method to distributed optimization. We hope this work sheds light to the optimization of CPES.</p> <p><strong><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Qing-Shan-Jia.jpg" alt="Qing Shan Jia" />Biography: </strong>Qing-Shan Jia received a B.S. degree in automation in July 2002 and a Ph.D. degree in control science and engineering in July 2006, both from Tsinghua University, Beijing, China. He is a Full Professor in the Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University, where he currently serves as the associate director. He was the vice dean of Tsinghua Global Innovation eXchange (GIX) institute from 2016 to 2019 and the vice-chair of the Department of Automation at Tsinghua University from 2015 to 2018. He was a postdoc at Harvard University in 2006, a visiting scholar at the Hong Kong University of Science and Technology in 2010, and at Laboratory for Information and Decision Systems, Massachusetts Institute of Technology in 2013. His research interest is to develop an integrated data-driven, statistical, and computational approach to find designs and decision-making policies which have simple structures and guaranteed good performance. His work relies on strong collaborations with experts in manufacturing systems, energy systems, autonomous systems, and smart cities. He is currently the executive editor-in-chief of Results in Control and Optimization and an associate editor (AE) of Science China Information Sciences.</p> <p>He was an AE of IEEE Transactions on Automatic Control (2015-2021), IEEE Control Systems Letters (2019-2021), IEEE Transactions on Automation Science and Engineering (2012-2017), and Discrete Event Dynamic Systems – Theory and Applications (2012-2016). He served as the Discrete Event Systems Technical Committee chair in IEEE Control Systems Society (2012-2015), and the co-chair for Smart Buildings Technical Committee in IEEE Robotics and Automation Society (2012-2021). He is currently the Control for Smart Cities Technical Committee chair of the International Federation of Automatic Control, and the Beijing Chapter Chair of IEEE Control Systems Society. He is a member of the 11th Chinese Automation Association Technical Committee on Control Theory (2018-2022) and the 1st Chinese Automation Association Technical Committee on Information Security of Industrial Systems (2016-2020).</p> <p><a href="https://drive.google.com/drive/folders/1sqcZAvsY_IZCXB3OPNfBbATE-jjfMSEg?usp=sharing" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a></p> <p> </p> <h4 style="clear: both;">Industrial Real-time Communication from the Sensor to the Cloud in Converged Networks - OPC UA TSN</h4> <p><strong>Wednesday, 15 September 2021</strong></p> <p><strong>Presenter: </strong><a href="mailto:bruckner@ieee.org">Dietmar Brucker</a>, B&R Industrial Automation</p> <p><strong>Abstract: </strong>The industrial communication market is dominated by Ethernet-based fieldbus systems. Although they share similar requirements and market segments, their implementations and ecosystems differ considerably. As a result, end customers and device manufacturers are faced with a multitude of technologies that need to be produced, run, diagnosed, maintained, and kept in stock. Although the availability of products and services is largely satisfactory, dealing with multiple solutions generates high costs and limits IoT capability and interoperability. This webinar introduces Open Platform Communication Unified Architecture Time-Sensitive Networking (OPC UA TSN) as a new technology and presents the current state of standardization and deployment. This time, the industrial prospects of fulfilling industrial communication requirements while leveraging the cost benefits of standard Ethernet hardware in the midterm are in reach. We anticipate that OPC UA TSN will reveal itself as a game changer in the field of industrial automation, being a candidate for establishing a holistic communication infrastructure from the sensor to the cloud.</p> <p><strong><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Dietmar-Bruckner.jpg" alt="Dietmar Bruckner" />Biography: </strong>Dietmar Bruckner is with B&R Industrial Automation since 2013, where he is a unit manager in the RnD department System Software. He is responsible for the development (i) of a wide range of Industrial Connectivity solutions based on (real-time) Ethernet, and (ii) of the real-time operating system Automation Runtime for PC and PLC platforms. Before B&R, he worked as a University Assistant (i.e., Assistant Professor) at the Institute of Computer Technology of the Vienna University of Technology, managing research teams in the areas of field busses, building automation, and artificial intelligence.</p> <p>He studied Electrical Engineering, where he obtained an MSc in 2004 and a PhD in 2007 and passed both degrees with distinction.</p> <p>Bruckner is a Senior Member of IEEE, and an IEEE IES AdCom member. He published ~70 reviewed scientific publications, and ~100 scientific publications in total as well as 5 international patents and edited the IEEE 61158 standard. He is actively involved in organizing international conferences, editing high ranked international journals (AE of TII, TIE, and IEM), and drafting international standards in the IEEE and OPC Foundation.</p> <p><a href="https://drive.google.com/drive/folders/1TdCFk5EKEoJStohpfjrhjsHib42pC1j6" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a></p> <p> </p> <h4 style="clear: both;">How to Feed a Growing Population While Conserving the Planet's Resources – IoT to the Rescue</h4> <p><strong>Wednesday, 11 August 2021</strong></p> <p><strong>Presenter:</strong> Victor Grimblatt, R&D Group Director and General Manager at Synopsys, Chile</p> <p><strong>Abstract:</strong> The world population is growing and according to FAO (United Nations Food and Agriculture Organization), agriculture production should increase by 70% by 2050. On the other hand, it is also well known that agriculture is dramatically impacting the 9 planetary boundaries defined by Johan Rockström and his group in 2009. We are facing a big dilemma, how to improve the productivity of the soil without impacting the planet and its limits.</p> <p>The growth of crops depends on several parameters such as soil moisture, soil temperature, nutrients (fertilizers), soil pH, soil salinity, etc. Without having an online measurement (real-time) of those parameters we will probably be able to get products from the ground, however, we are not taking into account the impact of what we are doing and the way we are making agriculture. With the appropriate sensors and IoT, we are able not only to know the level of the parameters already mentioned but also to act based on the results.</p> <p><strong><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Victor-Grimblatt.jpg" alt="Victor Grimblatt" />Biography:</strong> Victor Grimblatt has an engineering diploma in microelectronics from Institut Nationale Polytechnique de Grenoble (INPG – France) and an electronic engineering diploma from Universidad Tecnica Federico Santa Maria (Chile). He is doing his PhD on IoT for Smart Agriculture at IMS lab, University of Bordeaux. He is currently R&D Group Director and General Manager of Synopsys Chile, leader in Electronic Design Automation (EDA). He opened the Synopsys Chile R&D Center in 2006. He has expertise and knowledge in business and technology and understands very well the trends of the electronic industry; therefore he is often consulted for new technological business development.</p> <p>He has published several papers in IoT, EDA and embedded systems development, and since 2007 he has been invited to several Latin American Conferences (Argentina, Brazil, Chile, Mexico, Peru and Uruguay) to talk about Circuit Design, EDA, IoT, and Embedded Systems. From 2006 to 2008 he was a member of the “Chilean Offshoring Committee” organized by the Minister of Economy of Chile. In 2010 he was awarded as “Innovator of the Year in Services Export”. In 2012 he was nominated to best engineer of Chile. He is also a member of several Technical Program Committees on Circuit Design and Embedded Systems. Since 2012 he is chair of the IEEE Chilean chapter of the CASS. He is also the President of the Chilean Electronic and Electrical Industry Association (AIE). Since 2021 he is a member of the IEEE CASS Board of Governors (CAS BoG).</p> <p>Victor Grimblatt was professor of Electronics and IC Design in Universidad de Chile and Universidad de los Andes. He is currently lecturing a course IoT for Agriculture at Universidad Santo Tomas, Chile. Victor’s research areas are EDA (Electronic Design Automation), and Smart Agriculture applying Machine Learning, Artificial Intelligence, and IoT.</p> <p><a href="https://drive.google.com/drive/folders/1dKKDc49jL82U1Sf1FCEB0y2XEIpPHzsY?usp=sharing" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a></p> <p> </p> <h4 style="clear: both;">Future Agriculture - Outlook of Photovoltaic Farm IoTs</h4> <p><strong>Wednesday, 7 July 2021</strong></p> <p><strong>Presenter: </strong>Professor Lei Shu, Professor at Nanjing Agricultural University, China and University of Lincoln, UK</p> <p><strong>Abstract: </strong>We believe that the "Photovoltaic Farm Internet of Things" will be the most effective management tool to achieve this new agricultural production mode - "Photovoltaic Farm", and it will also be a new research direction in the field of information technology, which integrates smart agriculture, sustainable energy, and Internet of Things.</p> <p><strong><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Lei-Shu.jpg" alt="Lei Shu" />Biography: </strong>Lei Shu is currently a Professor with Nanjing Agricultural University, China, and a Lincoln Professor with the University of Lincoln, UK. He is also the Director of the NAU-Lincoln Joint Research Center of Intelligent Engineering. He was the vice-director of Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, China, 2013–2017. He received the B.S. degree in computer science from South Central University for Nationalities, China, in 2002, the M.S. degree in computer engineering from Kyung Hee University, South Korea, in 2005, and the Ph.D. degree from the Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland, in 2010. He was a recipient of the 2014 Top Level Talents in Sailing Plan of Guangdong Province, China, the 2015 Outstanding Young Professor of Guangdong Province, the GLOBECOM 2010, ICC 2013, ComManTel 2014, WICON 2016, SigTelCom 2017 Best Paper Awards, the 2017, 2018 and 2020 IEEE Systems Journal Best Paper Awards, the 2017 Journal of Network and Computer Applications Best Research Paper Award, Outstanding Service Award of IUCC 2012, ComcomAP 2014, Outstanding Leadership Award of Healthcom 2017, the Outstanding Associate Editor Award of 2017, and the 2018 IEEE ACCESS.</p> <p>He has published over 400 papers in related conferences, journals, and books in the areas of sensor networks and Internet of Things. His current H-index is 62 and i10-index is 244 in Google Scholar Citation (more than 13400 times). He is a senior member of IEEE, IEEE IES, IEEE ComSoc, a member of ACM.</p> <p><a href="https://drive.google.com/drive/folders/1JJGSSLjcaGrFDFMXQ5xSAV7HvZs4HluK" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a></p> <p> </p> <h4>AI-based Smart Cities Initiatives in Morocco</h4> <p><strong>Wednesday, June 9, 2021</strong></p> <p><strong>Presenter:</strong> Professor Mohamed Essaaidi, Head of the Smart Systems Lab and Former Dean, ENSIAS College of Engineering, Mohamed V University in Rabat<br /> Chief of Party, Interactive Digital Center, Morocco Former Director International Coop. at Ministry of General Affairs & Governance, Morocco Founder & Former Chair of IEEE Morocco</p> <p><strong>Abstract:</strong> As is the case for many countries, Artificial Intelligence (AI) appears to be a priority for the Moroccan government owing to the wonderful and amazing benefits it brings to all kind of sectors and areas related with the country’s and its population activities and needs as well as to their performance. This is evidenced by several initiatives launched in Morocco during the last few years and which aim to harness the benefits of AI for all kind of areas and applications for Morocco and its citizens, such as:</p> <ul> <li>The funding of 45 applied research projects developed by universities, research institutions and start-ups over a period of three years, under Al-Khawarizmi AI Research Program Fund in all areas like industry, agriculture, health, education, mobility, energy, etc.</li> <li>The establishment of the International AI Center at Mohammed VI University.</li> <li>The launch of several start-ups developing AI based solutions and apps for areas as diverse as government, industry, education, health, mobility, smart cities, etc.</li> <li>The launch of training programs (e.g. M.Sc., M. Eng) in several universities throughout the country.</li> <li>The launch of Cities of Professions and Skills targeting to contribute to the training of AI human capital in the country at the level of TVET.</li> </ul> <p>All these initiatives should have a strong impact on Morocco at different levels and in all kind of areas and sectors within the next few years.</p> <p><strong><img style="margin-right: 10px; margin-bottom: 10px; float: left;" src="/images/files/images/Mohamed-Essaaidi.jpg" alt="Mohamed Essaaidi" />Biography:</strong> Mohamed Essaaidi, Prof. Dr. IEEE Senior Member, received the “Licence de Physique” degree, the “Doctorat de Troisième Cycle” degree and the “Doctorat d’Etat” degree in Electrical & Computer Engineering and with honors, respectively, in 1988, 1992 and 1997 from Abdelmalek Essaadi University in Tetuan, Morocco. He is a professor of Electrical & Computer  Engineering and  the Past Dean of  ENSIAS College of Engineering and current Director of Smart Systems Lab of Mohammed V University in Rabat, Morocco. He is also the Chief of Part of Interactive Digital Center Morocco. He is also the founder and past chair of IEEE Morocco Section and Chair of IEEE ComSoc / Computer Morocco Chapter.  He was co-organizer of IEEE Casablanca Core Smart City in 2016.  He was the General Chair of Rabat Smart Cities Week 2020. He is also a member of 2021 HAC Assessment Committee and 2021 IEEE Teaching Excellence Hub Board. Prof. Essaaidi is involved in several initiatives related to Smart Cities in Morocco and within IEEE.</p> <p><a href="https://drive.google.com/drive/folders/1VqjRHs6GtOFoRzxjApnznckWBJhrEpHg" target="_blank" rel="noopener">View the Webinar Recording and Presentation</a></p> <div class="clearfix"></div></article> <div id="content-column-3" class="col-md-3"> <hr class="visible-sm-block visible-xs-block" /> <ul class="nav navbar-nav menu"> <li id="secondary-nav" class="menu-title visible-sm visible-xs">Education</li><li class="item-661 child"><a href="/education/distinguished-lecturer-program.html" ><span>Distinguished Lecturer Program</span></a></li><li class="item-660 child"><a href="/education/seasonal-school.html" ><span>Seasonal School</span></a></li></ul> </div> </div> </div> </div>  <footer class="hidden-print"> <div class="container"> <div class="row"> <div class="col-md-12"> <hr class="footer-separator"> </div> </div> <div class="row"> <div class="col-md-10"> <ul class="list-inline"> <li> <a href="/" title="Home">Home</a> </li> <li> <a href="/home/sitemap" title="Sitemap">Sitemap</a> </li> <li> <a href="/home/contact-us" title="Contact IEEE IOT">Contact IEEE IOT</a> </li> <li> <a href="http://www.ieee.org/accessibility_statement.html" title="Accessibility">Accessibility</a> </li> <li> <a href="http://www.ieee.org/about/corporate/governance/p9-26.html" title="Nondiscrimination Policy">Nondiscrimination Policy</a> </li> <li> <a href="https://secure.ethicspoint.com/domain/media/en/gui/20410/index.html" title="IEEE Ethics Reporting">IEEE Ethics Reporting</a> </li> <li> <a href="http://www.ieee.org/security_privacy.html" title="IEEE Privacy Policy">IEEE Privacy Policy</a> </li> <li> <a href="https://www.ieee.org/about/help/site-terms-conditions.html" title="Terms & Disclosures">Terms & Disclosures</a> </li> </ul> </div> <div class="col-md-2"> <a href="http://www.ieee.org" title="IEEE.org" class="pull-right"> <img src="/templates/ieeesitetemplate/images/ieee-mb-bk.png" alt="IEEE"> </a> </div> </div> <div class="row"> <div class="col-md-12"> <p> <span class="sitename">IEEE Internet of Things</span> <span class="sitetagline"></span> </p> </p> © Copyright 2025 IEEE - All rights reserved. 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