ABSTRACT: As a promising system-architecture, an integrated terrestrial, UAV-aided, airplane-assisted as well as satellite-based global coverage-solution and its enabling techniques will be highlighted as a further step towards the provision of seamless global coverage.

An open scientific challenge is that of striking the most appropriate trade-off amongst the conflicting performance metrics of throughput, transmit power, latency, error probability - just to name a few. The 5G system meets this challenge by introducing three modes of operations, namely the enhanced Mobile BroadBand (aMBB), the Ultra Reliable Low-Latency Communications (URLLC) and the massive Machine Type Communications (mMTC) modes.

However, further research is required for findig all the optimal operating points of wireless systems with the aid of sophisticated multi-component system optimization, leading to the Pareto-front of all optimal solutions.

BIO: Lajos Hanzo is a Fellow of the Royal Academy of Engineering (FREng), FIEEE, FIET and a EURASIP Fellow, Foreign Member of the Hungarian Academy of Science. He holds honorary Doctorates from the University of Edinburgh and the Technical University of Budapest. He co-authored 19 IEEE Press - JohnWiley books and 1900+ research contributions at IEEE Xplore. For further information on his research in progress and associated publications please refer to IEEE Xplore.

ABSTRACT: In this talk, we discuss another side of big data analysis, bad data analysis, where the badness means the complexities resulting in the reproducibility issues. Some background knowledge is first introduced on the volatility of the big data analysis, which shows 1) “big” does not necessarily mean “better” and 2) the so-called multi-objective data analysis (against badness) is vitally important in advancing the state-of-the-art. Two examples are used for demonstration of the big data analysis, one for big data from complex networks and the other for big data from gene expression image processing. Finally, conclusions are drawn and some future directions are pointed out.

BIO: Dr. Zidong Wang is currently Professor of Dynamical Systems and Computing in the Department of Computer Science, Brunel University London, U.K. From 1990 to 2002, he held teaching and research appointments in universities in China, Germany and the UK. Prof. Wang's research interests include dynamical systems, signal processing, bioinformatics, control theory and applications. He has published 250+ papers in IEEE Transactions and 60+ papers in Automatica with an H-index of 120. He is a holder of the Alexander von Humboldt Research Fellowship of Germany, the JSPS Research Fellowship of Japan, William Mong Visiting Research Fellowship of Hong Kong.

Prof. Wang serves (or has served) as the Editor-in-Chief for Neurocomputing, the Deputy Editor-in-Chief for International Journal of Systems Science, and an Associate Editor for 12 international journals including IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Neural Networks, IEEE Transactions on Signal Processing, and IEEE Transactions on Systems, Man, and Cybernetics-Part C. He is a Member of the Academia Europaea, a Fellow of the IEEE, a Fellow of the Royal Statistical Society and a member of program committee for many international conferences.

ABSTRACT: The Internet of Things (IoT) aims at connecting myriads of data collecting devices deployed at physical locations in the field. Traditionally, a custom set of such devices would collect data sets specifically targeted to one type of application usually hosted in the cloud; another application would require another set of sensors and so on, thus leading to stove-piped solutions and introducing major management complexity. The Data Exchange paradigm aims at a horizontal approach, where sensors collect data which becomes a reusable resource among a plurality of applications removing the dedicated sensor-data-app vertical model. Moreover, modern data processing techniques including AI, machine learning and a variety of different analytics, such as video analytics, drives the development of innovative IoT applications and services which, due to Data Exchange, may combine, correlate and analyse data from more than one source driving higher value insights from low-level data. Another noteworthy dimension is introduced by the “IoT Continuum”. IoT Continuum characterises the spread of hosting compute infrastructures from the very customer edge, where devices are situated such as general-purpose gateways, the network edge, e.g., 5G MEC nodes, all the way to the cloud, and anywhere in between the edge and cloud ends where compute nodes are available. These new techniques driving IoT service development as well as the IoT Continuum define the New Generation of IoT Architecture (NG-IoTA), on which this talk will be focusing. We will present a set of architectural principles underpinning NG-IoTA, talk about the manifestation of these principles in various solutions regarding Edge Computing, orchestration, IoT analytics, service management and their application in a few use-cases offering real world example implementations of these capabilities.

BIO: Nektarios Georgalas is a Principal Researcher at British Telecom's Research and Innovation department. In his current role, he is Director of the BT/Intel and the BT/Huawei Co-labs, two collaborative research programmes with key BT partners delivering innovations in the areas of Cloud, Data Centres, Network Virtualisation, Smart Cities, IoT and Mobility. During his career with BT, since 1998, he has managed numerous collaborative and internal research projects in areas such as network management, market-driven data management systems, policy-based management, distributed information systems, SOA/Web Services, Model Driven Design and Development of telecoms OSS, Cloud and NfV. Nektarios has led numerous international collaborations on the application of advanced techniques for design, development and operation of telecoms Networks and Operation Support Systems environments. In the past he was very active leading and contributing to key programmes within the TeleManagement Forum, where he established international standards teams, led Catalysts and influenced the Forum's strategy towards a model-driven and software-defined ecosystem of digital services in dynamic marketplaces; most recently he is involved in TMF’s Smart City Forum and related Catalysts. His work has been recognised several times by numerous international awards including the TMForum's "Excellence Award for Innovation" 2010, "Most Innovative Catalyst Award" 2014, "Best New Catalyst Award" 2015 and "Most Significant Contribution to Frameworx Award" 2015, “Most Innovative Catalyst – Smart X Commercial” 2016, “Outstanding Performance in the Catalyst Programme” 2017 and “Smart City Innovator of the Year” Excellence Award 2017. Other recognition accolades include Global Telecoms Business's "Business Service Innovation Award" 2010, 2012 and 2013. He has been Finalist in UK IT Industry Award for "Best IT Innovation" in 2013 and Highly Commended for the IET Innovation Award for Telecommunication in 2009. He has also achieved "Best innovation for Large Enterprise" and "Best Customer Experience Innovation" Finalists in BT Innovation Awards 2010. Nektarios has been recognised in BT's TSO "Brilliant People" 2015. Nektarios is inventor and co-inventor of 12 patents. He has also authored more than 70 papers in international journals and conferences and served as guest editor in the areas of IoT, Big Data and Data Science in highly recognised international journals including IEEE. He has served as General Co-chair, Programme Co-Chair, Programme Committee and Keynote Speaker and Invited Panellist in top international IEEE academic and TMForum conferences.

ABSTRACT: Federated learning has been a hot research area in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing systems and infrastructures to ease the development of various federated learning algorithms. Just like deep learning systems such as Caffe, PyTorch, and Tensorflow that boost the development of deep learning algorithms, federated learning systems are equivalently important, and face challenges from various issues such as unpractical system assumptions, scalability and efficiency. Inspired by federated systems in other fields such as databases and cloud computing, we study the system design requirements for federated learning systems. We find that two important features for federated systems in other fields, i.e., heterogeneity and autonomy, are rarely considered in the existing federated learning systems. In this talk, we will take a systematic comparison among the existing federated learning systems and present our research progress and future system research opportunities and directions.

More details about our research can be found at http://www.comp.nus.edu.sg/~hebs/ and related survey (https://arxiv.org/abs/1907.09693).

BIO: Dr. Bingsheng He is currently an Associate Professor and Vice-Dean (Research) at School of Computing, National University of Singapore. Before that, he was a faculty member in Nanyang Technological University, Singapore (2010-2016), and held a research position in the System Research group of Microsoft Research Asia (2008-2010), where his major research was building high performance cloud computing systems for Microsoft. He got the Bachelor degree in Shanghai Jiao Tong University (1999-2003), and the Ph.D. degree in Hong Kong University of Science & Technology (2003-2008). His current research interests include cloud computing, database systems and high performance computing. His papers are published in prestigious international journals (such as ACM TODS and IEEE TKDE/TPDS/TC) and proceedings (such as ACM SIGMOD, VLDB/PVLDB, ACM/IEEE SuperComputing, ACM HPDC, and ACM SoCC). He has been awarded with the IBM Ph.D. fellowship (2007-2008) and with NVIDIA Academic Partnership (2010-2011). Since 2010, he has (co-)chaired a number of international conferences and workshops, including IEEE CloudCom 2014/2015, BigData Congress 2018 and ICDCS 2020. He has served in editor board of international journals, including IEEE Transactions on Cloud Computing (IEEE TCC), IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS), IEEE Transactions on Knowledge and Data Engineering (TKDE), Springer Journal of Distributed and Parallel Databases (DAPD) and ACM Computing Surveys (CSUR). He has got editorial excellence awards for his service in IEEE TCC and IEEE TPDS in 2019.