Date: Tuesday, 7 April 2020 and Wednesday, 8 April 2020
Room: Ascot Room
Progressing the future of the Internet of Things (IoT) requires solving key fundamental challenges in computing and information processing. Today, IoT systems enable collecting billions of bits of information from various sensors, devices, and systems. The collected information could be valuable for various markets. Processing the information requires huge computing power. High-performance computing (HPC) may empower artificial intelligence (AI) for producing results and findings that are way beyond what we have witnessed. Furthermore, IoT data analytics platforms that provide easy use of machine learning models (e.g., deep learning) can essentially transform the way businesses operate through data-driven insights.
While IoT and AI offer various benefits to societies, due to larger scales, more complex models, and increased data volumes, the existing challenges of computing and information processing are amplified. These challenges include the deployment of sensors, data collection, communication, and data analytics. To address these challenges, there has been a vast amount of research and development work in the last decade. The work in fields such as high-performance computing, big data analytics, cloud computing, and edge/fog computing can be considered in this context. In addition to those, In addition to the existing challenges, new challenges arise. Processing IoT data, sharing data securely and transparently, creating insights, as well as data privacy are among such key challenges. IoT data analytics platforms aim to solve these problems of information processing.
In this topical area track, we focus on the advancements as well as challenges of the high-performance computing and IoT data analytics platforms. In this context, our topics will include but are not limited to:
- High-performance computing
- High-performance software systems/operating systems
- Network virtualization
- IoT platforms
- Data analytics using AI and ML methods
- Open data markets
- Cloud computing
- Edge/fog computing
- IoT data privacy and security
- Smart cities/smart mobility
- Smart industry
Fawzi Behmann, TeleNet Management Consulting, Round Rock, TX USA
Fawzi is a visionary, thought leader, author that has been blessed with great academic and career opportunities. He holds a Bachelor of Science with honors and distinction from Concordia University, Montreal, QC, master’s in computer science from University of Waterloo, Waterloo, ON, and Executive MBA from Queens University, ON, Canada. All this has contributed to a solid base in science, math, logic, technology and business. This academic foundation empowered Fawzi in his career path in the areas of communications and networking spanning supply-chain from service provider with Teleglobe Canada, to equipment vendor with Nortel Networks, to semiconductor with Motorola/Freescale in Canada and USA. Since 2009, Fawzi started TelNet Management Consulting, Inc. in Texas, offering consulting services in the areas of IoT/GIS/mobile/wearables technology positioning, smart networking solutions for key markets such as fitness & healthcare, smart homes/building, smart energy, smart infrastructure and smart cities. Recently, Fawzi collaborated with consortiums and offered consultation and proposals for risk-based GIS/IoT in the area of public safety to Interior ministry of Ghana and Togo and Business KPI-based Improvement Strategy and 5-Year Roadmap for 38 departments of the ministry of Environment & Water at UAE. Fawzi has been a keynote & distinguished speaker, and presenter at several domestic and international conferences. He is active in international forums and standards activities with ITU, ITRS and IEEE. Fawzi is a senior member of IEEE and is currently the vice chair for IEEE ComSoc North America Board, chair of IEEE Computer Society (Austin Chapter) and has been a chair of IEEE ComSOc/SP Austin chapters, IEEE Central Texas PACE chair and other volunteered positions. Fawzi was a recipient of IEEE Region 5 Outstanding member service award for 2013 and 2014. He also, received IEEE Communications Society Chapter Achievement award in North America and Chapter of the Year award globally across 212 chapters.
Gurkan Solmaz IoT Research Group, NEC Laboratories Europe, Heidelberg, Germany
Gürkan Solmaz is a Senior Researcher in the IoT Research group at NEC Laboratories Europe in Heidelberg, Germany. His research interests include mobile computing/networking, mobility, and cloud-edge systems aspects of IoT with a particular focus on crowd mobility in smart cities as well as autonomous vehicles/drones in IoT applications. He received his BS degree in Computer Engineering from Middle East Technical University (METU) in Turkey and his MS and Ph.D. degrees in Computer Science from the University of Central Florida (UCF) in the USA. He co-authored more than 30 papers and he was co-recipient of two best paper awards and the UCF Computer Science Ph.D. Student of the Year First Runner-up award. He has been a regular member of the technical program committees of IEEE conferences and a member of IEEE, Communications Society (ComSoc), ACM, SIGMOBILE, and ACM Future of Computing Academy (FCA).
Mustafa Ilhan Akbas, Embry-Riddle Aeronautical University, Florida, USA
Dr. M. Ilhan Akbas is an Assistant Professor of Electrical and Computer Engineering at Embry-Riddle Aeronautical University. He holds a Ph.D. degree in Computer Engineering from the University of Central Florida (UCF), where he also received the UCF Interdisciplinary Information Science and Technology Laboratory Fellowship. Dr. Akbas has research interests in connected and autonomous vehicles, cyber-physical systems, software defined networks, modeling and simulation. He was at the center of emerging autonomous vehicle research capability at the Florida Polytechnic University, where he was a founding member of the Advanced Mobility Institute. He has been covered by media and gave invited talks at international venues for his research on developing novel solutions for the testing and validation of autonomous vehicles. His research projects have been supported by NSF, Florida Cyber and industry. He received his BS and MS degree in Electrical and Electronics Engineering from Middle East Technical University (METU) in Turkey and his Ph.D. degree in Computer Engineering from the University of Central Florida (UCF) in the USA, where he also received the UCF Interdisciplinary Information Science and Technology Laboratory Fellowship. Before academia, Dr. Akbas had professional experience in defense industry, where he participated in multinational telecommunications projects and became a Cisco Certified Network Associate (CCNA). Dr. Akbas is a member of IEEE, ACM and Complex Systems Society. He is the author of more than 50 articles, which have appeared in prestigious venues such as IEEE Transactions, Society of Automotive Engineers (SAE) edge reports and flagship conferences of IEEE and ACM. He is an NSF Panelist and serves in the technical program committees of IEEE conferences.
Martin Bauer, NEC Laboratories Europe (NLE), Heidelberg, Germany
Martin Bauer is a Senior Researcher at the IoT research group at the NEC Laboratories Europe (NLE) in Heidelberg, Germany. He received his MSc in Computer and Information Science from the University of Oregon, USA, in 1998, and both his Dipl. Inf. and his doctorate degree from the University of Stuttgart, Germany, in 2000 and 2007 respectively. In 2005 he joined the NEC Laboratories Europe (NLE), where he has been working on context and IoT related research projects as well as standardization activities in the same areas. In particular, Martin Bauer is/has been working on a number of European research projects, most recently in the large scale pilots in IoT, Autopilot and Synchronicity, and in the EU-Japan project Fed4IoT. In standardization, he has actively contributed to OMA NGSI, oneM2M and currently ETSI ISG CIM, primarily on topics related to context management and semantics. Martin Bauer is a personal member of the FIWARE Foundation, which is managing, promoting and evolving the open source platform for smart IoT scenarios. He currently serves as a member of the FIWARE Technical Steering Committee (TSC). Furthermore, he is the AIOTI WG03 sub-group chair for the semantic interoperability topic. He has (co-) authored more than 50 technical papers and has been active as peer reviewer and program committee member for several journals, conferences and workshops. He is a member of IEEE, IEEE Computer Society, IEEE Communications Society and ACM.
Talk Title: Managing IoT Information and using it for Situation Classification
Abstract: The Internet of Things is growing in scale and in some application areas, like smart cities, the awareness is growing that to achieve the full IoT potential, the individual application silos have to be integrated and information has to be shared across the silos. Due to the scale, but also due to the fact that there are different organizational units, e.g. departments, each managing parts of the information, a centralized solution is not realistic. Instead distributed and federated solutions are needed that enable different units to stay in control of their information, enabling partial sharing of information depending on the recipient.
As an approach for integrating information from different information source, the NGSI-LD API and information model will be introduced, which is being specified by the Context Information Management Industry Specification Group of the European Telecommunications Standards Institute (ETSI ISG CIM). The NGSI-LD API can be the interoperability mechanism on top of existing local platforms and supports distributed and federated deployments across different organizational units. This enables the scalable management of IoT information in large IoT scenarios.
The underlying information model is based on a knowledge graph, which can serve as a basis for data analytics, in particular for situation classification. The idea is to make use of the encoded knowledge and combine it with machine learning approaches, deriving higher-level situations, which can be integrated into the graph as new knowledge.
Vittal Siddaiah, Texas State University, Texas, USA
Vittal is a student of electrical engineering at Texas State University and a system validation engineer at Intel with 15 years of experience in silicon validation. He architects machine-learning-based bus function models, regression, and triaging tool suites. He is distinguished for his contributions to the high-performance design of tools in the field of data-analytics and measurements. He has earned several recognitions and awards, including “One Generation Ahead Award” and “Waste Elimination Award.” Vittal is passionate about mentoring engineers and students. He has won the “Best Trainer Award” at Intel. Some of the domains include Hardware-software co-design, Operations Research, Image Processing, Computer Vision, Python, and C++. Vittal has Bachelors in Electronics Engineering, and Masters in Management, M Phil in Management, Masters in Mathematics.
Talk Title: Accelerated AI on HPC
Abstract: Artificial Intelligence (AI) is the panacea for both prescriptive and predictive analytics through Machine Learning (ML) techniques. Demand for computational performance has been snowballing over the decades. In the solution to this growing demand, there are AI accelerators that are domain-specific, especially in the areas of AI applications that include variants of neural networks in machine learning. High-performance computing (HPC) is accelerating this transformation by enabling the use of AI capabilities to existing HPC workflows (HPC-on-AI) and the massive scaling of AI algorithms to take advantage of the capabilities of HPC systems (AI-on-HPC). Most ML algorithms begin with the two phases, Training and Inferencing. Inferencing phases are relatively less compute-intensive as compared to the training phase. The training phase is critical, and with the advent of deep neural networks, there is an increase in accuracy while it results in exponential growth in computation. Essential applications in the field of digital pathology, astrophysics, it is both data-intensive and compute-intensive. Experimental results show that parallelization would yield better performance but do not scale with the increase in the number of threads. Beyond a threshold, for huge-sized imaging, the memory requirements are high along with the increased I/O latencies. These inherent latencies impact on performance. An increase of computational threads would only result in a performance drop. Patching techniques would help but might compromise accuracy resulting in false-positives and false-negatives. As compared to the classification-problem domain, the segmentation-problem field is challenging as they demand more accuracy. There is a continual challenge for a unified framework in tools that can scale and optimize to the demand.
Luis Sanchez, University of Cantabria, Spain
Dr. Luis Sanchez is Associate Professor of Telecommunications Engineering at University of Cantabria (Spain). He holds a Ph.D. degree in Telecommunications from the University of Cantabria (2009). Currently, his research is focused on the aspects of machine learning and network technologies of the Internet of Things (IoT) and its application to Smart Cities and Industry 4.0. He also works on the integration of these networks in the Semantic Web for the provision of interoperable services through the “Web of Things”. His research activity has been developed mainly in the framework of international cooperative projects from the 5th, 6th and 7th Framework Program and Horizon 2020 of the European Commission. In this sense, he has collaborated in more than a dozen of projects in the last 5 years where he has acted as Technical Coordinator for some of them. As a result of this work, he has (co-) authored more than 60 scientific articles in journals and international congresses of recognized prestige as well as participated as guest speaker at major conferences in the area. Moreover, he frequently peer-reviews articles for journals included in the first quartiles of the JCR and for international conferences and workshops taking part also in the organizing and program committees of the latter. Finally, he is also regularly invited to participate as external expert reviewer for the European (EC), Spanish (AEI), French (ANR) and Italian (MIUR) Research Agencies evaluating research projects for competitive Calls.
JaeSeung Song, Sejong University, Seoul, South Korea
JaeSeung Song (email@example.com) is an associate professor in the Computer&Information Security Department at Sejong University and a Standards Fellow at Korea Electronics Technology Institute. He holds the position of oneM2M Technical Plenary Vice-Chair. Prior to his current position, he worked for NEC Europe Ltd. and LG Electronics in various positions. He received a Ph.D. at Imperial College London in the Department of Computing, United Kingdom. He holds B.S. and M.S. degrees in computer science from Sogang University. His research interests span the areas of software engineering, networked systems and security, with focus on the design and engineering of reliable and intelligent IoT/M2M platforms, particularly in the context of semantic IoT data interoperability. He also holds leadership roles in several conferences such as an IoT series editor of IEEE Communications Standards Magazine and an IoT track chair of IEEE Conference on Standards for Communications and Networking.
Talk Title: Development of Data-Centric Smart City based on global IoT standards and semantics
Abstract: Since the Internet-of-Things (IoT) has been introduced, the IoT sector continues to get a lot of attention from the government and industry as a whole. One reason for this is because IoT is a fundamental technology that enables Artificial Intelligence (AI) applications. Another reason is that IoT is considered a promising solution in its own right to solve conventional Smart City problems such as waste management and environmental monitoring. The first generation of smart city projects around the world provides various lessons learnt and challenges, including the importance of semantics, the need for a data-centric platform and the lack of supporting Smart City enabling technologies (such as Complex Event Processing and Blockchain).
The talk will discuss the development of a data-centric Smart City with the aim of improving the quality of city life and enabling a sustainable city environment that is being developed by a “CityHub” project funded by the Korean government. The project is all about managing data and providing various tools and technologies that will enable services to use data from CityHub. In order to support global interoperability, the CityHub project is designed based on an IoT global standard, oneM2M. A data-centric IoT service layer platform being developed in CityHub supports several standards-based technologies, for example, Smart Device Template (SDT 4.0), semantic enablement, ontologies for Smart City services and data analytics to trigger enhanced actions.
Semih Aslan, Texas State University, Texas, USA
Dr. Semih Aslan received a B.Sc. degree in electrical engineering from Istanbul Technical University in 1994, M.Sc. degree in electrical engineering from Illinois Institute of Technology in 2003, and Ph.D. degree in computer engineering from Illinois Institute of Technology in 2010. He has worked as a Senior FPGA Design Engineer with the Motorola LTE Division and as a post-doctoral researcher at Illinois Institute of Technology. He joined the Ingram School of Engineering at Texas State University in 2011, where he is currently an Associate Professor. Dr. Aslan is the founding director of the System Modeling and Renewable Technology (SMART) Lab. He currently advises graduate students on green energy, system design and data analysis projects and has numerous publications. He is a Senior IEEE member.
Talk Title: “Challenges in Internet of Medical Things (IoMT)”
Abstract: In today’s fast-moving technology environment, sensors are constantly being added to the IoT domain, from traffic monitoring to object tracking. Transmitting, storing and analyzing this data brings more challenges every day in our already struggling infrastructure. The introduction of 5G and the potential solutions that 5G offers will ease these challenges, especially in the Internet of Medical Things (IoMT). There are many areas related to healthcare which could use the speed and latency that 5G offers to address many areas, such as homecare, visual doctors, home monitoring, tele-health/tele-medicine and treatment, as well as remote surgery. With the improvements that 5G brings as well as advances in real-time data analysis, localized data collection and processing, Artificial Intelligence (AI) and Machine Learning (ML) data can be analyzed and stored more effectively.
George Koutitas, Texas State University, Texas, USA
Dr. George Koutitas is an academic and entrepreneur in electrical and computer engineering. He has more than 10 years of academic and business experience in Smart Grids, Wireless Networks and Augmented Reality.vDr. George Koutitas is an Assist. Professor in Electrical and Computer Engineering and the Director of the XReality Research lab at Texas State University. George is also the a founder of two hi-tech startup companies in the area of AR (called Augmented Training Systems Inc.) and Energy (called Gridmates Inc.vDr. George Koutitas holds a B.Sc. degree in Physics from Aristotle University of Thessaloniki in Greece, an M.Sc. degree (with Distinction and prizes from ‘Nokia’) in Mobile and Satellite Communications from the University of Surrey, UK and a PhD in Electrical Engineering under EPSRC scholarship from the Centre for Communications Systems Research, UK. His post-doctoral studies were funded from the Dept. of Electrical and Computer Engineering of the University of Thessaly and the European Union. George is a member of IEEE and IET and has published more than 42 scientific publications in peer reviewed journals and conferences, the author of 2 books and the inventor of 1 patent (pending). His research is cited in more than 1080 scientific publications (src. Google Scholar).
Talk Title: “Modern applications of AR and IoT”
Abstract: A huge variety of IoT connected devices are deployed in the close vicinity of a user creating a huge amount of data that is hard to access and interact with. We address this problem with a new concept of integrating real-world smart things and virtual-world avatars/objects in a computer-generated virtual environment so that entities in either worlds can interact with one another in a real-time manner. The presentation will discuss about a state-of-the-art AR/IoT platform that is developed at Texas State University that integrates a sensor network of Internet of Things (IoT) with an Augmented Reality (AR) device to create a “4D” experience. The 4D experience provides real-time spatio-temporal visualization and allow the user to interact with the IoT network in a highly intuitive and shared experience fashion. In addition, the presentation will discuss how network offloading techniques will be vital for the operation of 5G networks since they can help minimize redundant data traffic in 5G networks related to AR/VR applications.
For AR applications, one of the most data and CPU “hungry” process is object detection since it is related to feature point extraction, database searches and image transfer. One way to reduce this workload is to minimize the data transfer and database searches by designing and developing algorithms to manage those requests. We will introduce an algorithm that considers the location of the user as well as the field of view and aims to utilize these inputs to reduce a) the amount of redundant object detection function calls and b) minimize the database search by creating clusters.
Damian Valles, Texas State University, Texas, USA
Dr. Damian Valles is an Assistant Professor for the Ingram School of Engineering at Texas State University. He focuses on High-Performance Computing (HPC), Machine Learning (ML), and Embedded System implementations under the High-Performance Engineering (HiPE) research group. Dr. Valles received his B.S., M.S, and PhD. from The University of Texas at El Paso from the Electrical and Computer Engineering Department, focusing on Reconfigurable Processors and HPC research. Dr. Valles did a post-doc at Montana Tech as the HPC Application Scientist under the Computer Science department. He also worked as an HPC System Administrator in the Information Systems department and adjunct position in the Computer Science department at Wake Forest University. He is currently a member of IEEE, ACM, ACM’s SIGHPC, and SHPE.
Talk Title: “Smarter Implementations of Operating Systems with Application Specific Modules with Trained Models”
Abstract: As the implementation of many sensors and computational devices at different levels will become part of every engineering project infrastructure plan, network communication must also adapt to how to react to data correctly. The technology has become essential to deliver information that can be customized and learn behaviors through the mass telemetry of data captured in IoT, cloud, edge, and HPC environments. Active system-level learning will become a way to help deal with the amount of data for these projects will generate. Reinforcement learning will drive the design of models that can actively act on all different types and uses of data as they become available.
The effort to design Operating Systems (OS) through active-learning modules will network data in a smarter communication paths, levels of priorities, and understanding data flows. Reinforcement will provide customization of devices at different locations that require different data flows and react appropriately to higher priority events when it is location-specific. The ultimate goal is for the Reinforcement models to also operate at an encrypted level to provide security to the data traveling through different points. In which, the cycle repeats with customized hardware that will be able to handle requirements that the OS will run and provide intelligence in the data movement within large infrastructures.
Louis Calvin Touko Tcheumadjeu, German Aerospace Center (DLR), Berlin, Germany
Dipl.-Ing. Louis Calvin Touko Tcheumadjeu is a senior engineer in electrical engineering and computer science with more than 15 years business experience in the design and development of complex software systems. He received his engineering diploma in electrical engineering and computer science from Technical University Berlin (TUB), Germany, in 2004. After graduation he has worked as a research scientist on strategic research projects at the distributed artificial intelligence laboratory (DAI-Labor) of the TUB and at the Deutsche Telekom Laboratory (T-Labs) in Berlin. In 2007, he joined the German Aerospace Center (DLR) and since this time he works as research scientist at the Institute of Transportation Systems in Berlin, Germany. He participated in several national and international ITS projects mainly funded by different German Federal Ministries and the European Union. He is author of several scientific publications in peer reviewed journals, conferences and books. He received the best paper award at the INTSYS conference 2017 funded by the European Alliance for Innovation in Hyvingaa, Finland. He was a speaker at several national and international conferences (IEEE ITSC, ITS World Congress and ITS European Congress, World Conference on Transport Research – WCTR, Disaster Management and SUMO conference). He is currently engaged in research and development of innovative ITS telematics solutions for individual mobility management with focus on the IoT technology.
Talk Title: Internet of Things (IoT) for autonomous driving in case of Automated Valet Parking (AVP)
Abstract: The service development of autonomous driving use cases progressed by the internet of Things (IoT) is introduced in the presentation. In the context of six autonomous driving use cases, which were developed and realised for the European large scale project AUTOPILOT, the focus of the presentation is on the system design and development of the Automated Valet Parking (AVP) use case enabled by the IoT technology to support smart mobility for the future. Finally, the results of the field operational test at a pilot site in the Netherlands (Brainport) are presented.
Tom Coughlin, Coughlin Associates
Tom Coughlin, President, Coughlin Associates is a digital storage analyst and business and technology consultant. He has over 37 years in the data storage industry with engineering and management positions at several companies. Coughlin Associates consults, publishes books and market and technology reports and puts on digital storage-oriented events. Dr. Coughlin has many publications and six patents to his credit. Tom is also the author of Digital Storage in Consumer Electronics: The Essential Guide, which is now in its second edition with Springer. Coughlin Associates provides market and technology analysis as well as Data Storage Technical and Business Consulting services. Tom publishes the Digital Storage Technology Newsletter, the Media and Entertainment Storage Report, the Emerging Non-Volatile Memory Report and other industry reports. Tom is also a regular contributor on digital storage for Forbes.com and other blogs.
He is an IEEE Fellow, Past President of IEEE-USA and is active with SNIA and SMPTE. For more information on Tom Coughlin and his publications and activities go to www.tomcoughlin.com.
Talk Title: Computing At the Core and Edge
Abstract: The rise of IoT applications coupled with advanced wireless networking technology and AI applications employed to make real-time decisions based upon captured sensor data and inter-infrastructure communication will swell the need for computational power, networking, memory and digital storage. With the slowing of traditional computer scaling laws, new computer architectures are needed to power the IoT world. These architectures include new computational methods such as neural networks for processing in data centers, chiplet technologies to more effectively use advanced lithographic technologies and perhaps quantum computing for fast processing of data, when this technology has matured. Disaggregation of computing will result in smaller specialized processors working at the edge and endpoints to offload work from CPUs. Specialized packaging of compute, network and storage will provide “data centers in a box” for edge deployment. Non-volatile memories will replace volatile applications for many endpoint applications. This talk will explore developments in computing that will enable future generations of IoT infrastructure.
Eric M. Simone, Chief Executive Officer, ClearBlade, Inc.
Eric Simone is the Founder and CEO of ClearBlade Inc., an Enterprise Edge Computing Internet of Things (IoT) software company focused on large Enterprises in the transportation, building facilities, and connected products markets. Prior to starting ClearBlade, Eric was the founder and CTO of Compete Incorporated, which sold to Perficient Inc. (PRFT) for $63M in May of 2000. Earlier in his career, Eric achieved success in senior engineering, product and sales positions at IBM and Johns Hopkins Hospital. Eric has a degree in Computer Science from Purdue University and is a recognized Distinguished Alumni. Eric resides in Austin, Texas with his wife Toni and 2 sons, Xander and Dexter.
Talk Title: Enterprise IoT Architecture and Edge Computing creating a smart building at American Dream
Abstract: Smart Buildings are at the forefront of the Industrial Internet of Things. But creating a smart building is an extremely complicated task, requiring the connecting of hundreds and possibly thousands of components, devices, systems, and interfaces. Many hardware vendors are offering smart building components (elevators, HVAC, lighting, security, power etc.) creating a confusing marketplace of devices, capabilities, and systems that do not communicate with one another. Using examples from our customer American Dream in East Rutherford, NJ, I will explain how enterprise software and edge computing is being used to enhance customer service and manage all of their disparate building components to a create a truly smart building.
American Dream is a large “mega mall” based in East Rutherford, NJ developed by the Triple Five Group, a family-run real estate conglomerate that also owns the Mall of Americas. The first phase of American Dream opened Oct 25th. American Dream is approximately 3 million square feet (45% retail and 55% entertainment) and projects 40 million visitors annually. The complex includes over 450 shops, services and amenities, complemented by the best in entertainment, food, art and culture. American Dream will also feature a distinct shopping environment dedicated to iconic luxury brands and younger, fashion-forward retail. ClearBlade is providing the IoT Platform and Edge software infrastructure to deliver a complete and personalized real-time customer experiences while enabling future applications for entertainment and building operations.
Franck Le Gall, EGM in Sophia-Antipolis, France
Franck Le Gall, PhD Eng. in Physics and Telecommunications, is CEO at Easy Global Market, an innovative SME focused on integration and validation of emerging technologies. He is driving company development of advanced testing technologies as well as integration of IoT and data platforms. He involves himself in standardisation area including oneM2M and ETSI ISG CIM. Previously, he has participated in large R&D projects within the big industry (Orange, Alcatel, Thomson) and spent 9 years as Director within an innovation management company. He directed more than 10 large scale projects and studies related to the evaluation and monitoring of innovation and technical programs as well as research projects. He is now participating in several EU research projects in domains such as water management, cities, aquaculture and agriculture providing technical knowledge on the whole sensors to applications data chain. He has authored many scientific papers as well as patents.
Talk Title: Decision support with Digital twins and context information management
Abstract: The concept of digital twin developped in the early 2000s is gaining popularity. It supports the complete lifecycle of products from development to operation where the IoT based connection of a physical asset to its virtual counterpart provides new grounds for activity monitoring and planning. Several European research project are embracing this paradigm to explore new domains of applications. As examples, the FIWARE4WATER project is investigating digital twins for water distribution newtorks whereas the iFishienci project develops digital twins for fishes to support the developemnt of the aquaculture domain. These 2 projects are building upon the latest evolution of the NGSI-LD specification for context information management using property graphs model. The presentation will introduce the overall context and describe the investigated scenarios from a usage and data management point of view.
Brad Kirby, CPA, CA, CBP VP, Market Development
Brad is a seasoned digital transformation professional with a passion for bleeding edge technology. He currently serves as VP, Market Development at EDJX, an early-stage that is building the world’s largest edge cloud for the era of connected thing with unprecedented reach, simplicity and security. He has a strong entrepreneurial spirit and after launching his first start-up in 2004, his professional career commenced at Deloitte in 2007. After the 2008 financial crisis, he achieved his Chartered Accountant designation and was offered a transfer to Deloitte’s financial advisory group in Cayman Islands to aid in hedge fund insolvency, restructuring and asset recovery. The crisis revealed unprecedented level of fraud and insolvency committed onshore by investment managers, tasking firms like Deloitte with challenging asset recovery, restructuring, and forensic litigation engagements to recover assets for investors. and Brad was also instrumental in creating proprietary data analytic software, developing a custom fund management system, and various other technology to efficiently recover assets for investors. In 2013, he moved back to Toronto to join Brookfield Asset Management, one of the world’s largest real asset manager and spent 5 years splitting time as Brookfield’s global finance transformation initiatives while also overseeing global capital markets and treasury, which included $400B of debt, 400 lending relationships, and over $1T in annual derivatives trading for financial risk management. Brookfield was the largest non-financial institution FX trader on Wall Street, where he was the youngest signing officer and a trusted fiduciary of over $250B of assets. In 2017, he was named Director of Brookfield’s private equity pubco (BBU), where he was instrumental in executing 13 acquisitions in 10 months totaling $21B in 6 countries.
In 2018, Brad left Brookfield to pursue his passion for technology and launched DLT Advisory, a consulting firm focusing on fintech and distributed ledger technology start-ups. Brad was an advisor to EDJX when it was formed in June 2018 and Brad joined EDJX full-time in summer 2019. In Jan 2020, he started The Next Wave Podcast with Dean Nelson (former Head of Uber Compute) and James Thomason (Former CTO of Dell Cloud). He is considered a though leader in blockchain, finance, and edge computing. Recent speaking engagements include:
- Feb 2020 – Tech Super Expo – Practical Guide to DLT
- Dec 2019 – Edge Computing World – Edge Economics Workshop
- Nov 2019 – TMAC – Central Bank Digital Currency Panel – Bank of Canada, JP Morgan
- Aug 2019 – Blockchain Futurist – The Problem with Stablecoins
- April 2019 – Toronto Blockchain Week – DLT Consensus Algorithms – IBM and ConsenSys,
Ella Peltonen, University of Oulu, Finland
Dr Ella Peltonen is a research scientist with the Center for Ubiquitous Computing, University of Oulu, Finland. She gained her PhD at the University of Helsinki and did her postdoc period at the Insight Centre for Data Analytics, University College Cork, Ireland. Her research focuses on pervasive everyday sensing, edge-native machine learning, and “from data to actions” including ubiquitous recommendation systems and data analytics. She has been granted Marc Weiser Best Paper Award 2015, Rising Stars in Networking and Communications 2017, The European Initiative EPIC Grant 2018, and Nokia Foundation Jorma Ollila Grant 2018.
Talk Title: Distributed real-time learning for Internet of Things
Abstract: Sensor-driven IoT systems are well-known for their capacity to accelerate massive amounts of data in a comparable short period of time. To have any use, the information delivery and decision making based on the data require efficient learning models. The current cloud and high performance computing infrastructures, as well as modern edge computing systems especially in the 5G and beyond networks, can be addressed to resolve these challenges. However, there are several application areas especially in mobile, vehicular, and urban computing, where just harnessing more computational power does not solve computational and real-rime requirements of the modern sensing systems that operate in mobile and context-dependant environments. For now, mathematical challenges of distributed computing and real-time learning algorithms have not been profoundly addressed in the context of the IoT and real-world sensing applications. Data-driven systems also require giving the full attention into information delivery, data management, data cleaning, and sensor fusion technologies that need to be equally distributed and real-time competent as the learning algorithms themselves. The key challenge here is to uniform collaboration between different aspects of the system, from data processing and delivery to the algorithms and learning models, not forgetting the computational capacity and networking capabilities, all this in real-time with real-world applications.