Founder & President, IPv6 FORUM
Chair, IEEE COMSOC IoT subCommittee
Chair, IEEE COMSOC 5G subCommittee
Co-Chair, IEEE COMSOC SDN-NFV subCommittee
Emeritus Trustee, Internet Society – ISOC
IPv6 Ready Logo Program Board
Chair, ETSI IPv6 ISG
Research Fellow @ University of Luxembourg
Member of 3GPP PCG (Board)
Member of 3GPP2 PCG
Member of UN Strategy Council GAID
Member of the Future Internet Forum for Member State
Board member of WSA
- Co-Founder of ETSI AFI ISG
Title: IPv6-based IoT and Cloud Computing
The public IPv4 address space managed by IANA ( http://www.iana.org ) has been completely depleted back in Feb 1st, 2011. This creates by itself a critical challenge when adding new things and enabling new services on the Internet. Without publicly routable IP addressing, the Internet of Things, and anything that’s part of Internet of Everything, would be greatly reduced in its capabilities and limit its potential success. Most discussions about IoT have been based on the illusionary assumption that the IP address space is an unlimited resource or it’s even taken for granted that IP is like oxygen produced for free by nature.
IPv6 provides enhanced features that were not tightly designed or scalable in IPv4 like autoconfiguration, IP mobility, end to end (e2e) connectivity and e2e services, etc. IPv6 will be addressing the extreme scenarios where IP becomes a commodity service. This new address platform will enable lower cost network deployment of large scale sensor networks, RFID, IP in the car, to any imaginable scenario where networking adds value to commodity.
IPv6 deployment is now in full swing with some countries achieving over 50% penetration such as Belgium. The US has some 75 Million users using IPv6 without even the users knowing it. Recently, Apple has required its apps developers to use IPv6 only for their apps starting from June 1st, 2016, which is a great shot in the arm of IPv6.
There are many inflections happening this decade to influence the design of the first tangible IoT, Cloud Computing, and 5G. It will be a combination of IoT, SDN-NFV, Cloud Computing, Edge Computing, Big IoT Data, and 5G, to sift through to realizing the paradigm shift from current research-based work to advanced IoT, 5G, and Smart Cities.
This talk will be devoted to analyze the transformative impact of IPv6 on the potential mix of IPv6-based IoT, SDN-NFV, Cloud Computing, Big Data, and 5G and its advanced features, highlighting the challenges and the solutions moving forward.
Liverpool John Moores University, UK
Keynote speech title: Machine Learning Approaches for Extracting Genetic Medical Data Information
Bioinformatics is the development and application of computational tools for the field of biological and biomedical research data, including public health informatics and population informatics, in addition to clinical informatics. Bioinformatics represents a promising toolset to move from the standard therapies to tailor medical care to each individual genome, therefore instead of using certain therapy to group of patients suffering of certain disease, they tailor this therapy to each individual genome.
Machine learning algorithms and techniques have been used in bioinformatics. There are many methods available to deal with data including DNA sequence, complex gene-gene interactions data, and clinical data. To assess these complex data, there are several approaches such as multifactor dimensionality reduction, generalized multifactor dimensionality reduction, artificial neural networks for example multilayer feedforward neural networks, and feature selection approaches. These approaches provide capabilities to deal with very big data that include an excessive number of features.
In this talk, two case studies will be discussed for the use of machine learning for extracting genetic information which includes obesity and diabetes.
Dr. Abir HUSSAIN is a Reader (Associate Professor) in Image and Signal Processing and she is the head of the Applied Computing Research Group at the faculty of Engineering and Technology. She completed her PhD study at The University of Manchester, UK in 2000 with a thesis title Polynomial Neural Networks for Image and Signal Processing. She has published numerous referred research papers in conferences and Journals in the research areas of Neural Networks, Signal Prediction, Telecommunication Fraud Detection, and Image Compression. She has worked with higher order and recurrent neural networks and their applications to financial, physical, e-health and image compression techniques. She has developed with her research students a number of recurrent neural network architectures. Her research has been published in a number of high esteemed and high impact journals such as Expert Systems with Applications, PloS ONE, Electronic Letters, Neurocomputing, and Neural Networks and Applications. She has over 150 peer reviewed journal and conference publications. She is a PhD supervisor and an external examiner for research degrees including PhD and MPhil.
Data Mining for Automated Diagnosis of Neurological and Psychiatric Disorders
The Ohio State University
Abstract: In this keynote lecture, the author presents novel algorithms for data mining of time-series data and automated electroencephalogram (EEG)-based diagnosis of neurological and psychiatric disorders based on adroit integration of three different computing technologies and problem solving paradigms: neural networks, wavelets, and the chaos theory. Examples of the research performed by the author and his associates for automated diagnosis of epilepsy, the Alzheimer’s Disease, Attention Deficit Hyperactivity Disorder (ADHD), autism spectrum disorder (ASD), and Parkinson are reviewed.
Hojjat Adeli has authored over 550 scientific publications including 15 books since he received his Ph.D. from Stanford University in 1976 at the age of 26. He is the Founder and Editor-in-Chief of the international journal Computer-Aided Civil and Infrastructure Engineering, now in 31st year of publication and Integrated Computer-Aided Engineering, now in 24th year of publication, and the Editor-in-Chief of International Journal of Neural Systems. In 1998 he received the University Distinguished Scholar Award from The Ohio State University “in recognition of extraordinaryaccomplishment in research and scholarship”. In 2007 he received the Peter L. and Clara M. Scott Award for Excellence in Engineering Education and Charles E. MacQuigg Outstanding Teaching Award from OSU.Among his numerous awards include a Special Medal in Recognition of Outstanding Contribution to the Development of Computational Intelligence from The Polish Neural Network Society, Eduardo Renato Caianiello Award for Excellence in Scientific Research from Italian Society of Neural Networks, an Honorary Doctorate from Vilnius Gediminas Technical University, Lithuania, and membership in the Spanish Royal Engineering Society. He is a Thomson Reuters Highly Cited Researcher in two categories of Engineering and Computer Science. In 2010, he was profiled as Engineering Legend in ASCE journal of Leadership and Management in Engineering. He is a Fellow of AAAS, IEEE, AIMBE, and the American Neurological Association.
Ahcène Bounceur Associate Professor - HDR Lab-STICC - CNRS - UMR 6285 Computer Science Department 29238 Brest Cedex +33 (0) 2 98 01 62 17 http://pagesperso.univ-brest.
Ttitle: From Smart-city and IoT Simulation to Big Data Generation
Abstract: Our world is digitized everyday and increasingly. In 2020, it is expected that over 70% of the population will live in or around cities. To guarantee a good quality of life, it is necessary to ensure fast and reliable services in all areas, in particular those which are mainly based on the use of connected objects. This is one of the cornerstones of a smart city project. It will make possible to provide close to real-time the remote monitoring of sick patients, the monitoring of the environment in order to know its evolution over time and to anticipate developments that can be harmful to health and the environment itself, and to accurately analyze the signals transmitted by the on-board sensors.
To further develop domains such as eHealth or the monitoring of other networks in the context of Smart Cities, fast and reliable design tools are needed. Their objectives are to study the realizability of such networks, their behavior in terms of energy consumption, safety, cost and other reliability parameters.
This keynote aims to present a new platform called CupCarbon that allows to design systems of connected objects mainly representing sensors and to prepare future deployments of large-scale IoT infrastructures for Smart cities in optimal conditions. This kind of platforms will be a part of systems in the world that will participate in the generation of Big Data.
Dr. Ahcène Bounceur is an Associate Professor in Computer Science at the University of Western Brittany (UBO) and CNRS Lab-STICC laboratory and qualified for professorship. He received his HDR degree (Habilitation à Diriger des Recherches) from the University of Brest (France) in 2014, his PhD degree in Micro and Nano Electronics from the TIMA laboratory, Polytechnical National Institute of Grenoble (France) in 2007, his Master's degree in Operations Research and Optimization from the engineering school ENSIMAG, Grenoble (France) in 2003, and his Engineering degree in Operations Research from the University of Bejaia (Algeria) in 2002. He is the coordinator of the ANR (French Research Agency) project PERSEPTEUR: 3D Virtual Platform for Smart-Cities and IoT Wireless Sensor Network Simulation. He is a partner of the ARS (Regional Health Agency) project Suidia: A smartphone based tele-medicine platform for gestational diabetes, the most advanced French application in this domain. He published about 100 scientific papers and his research interests include: Tools for physical and parallel simulation of IoT and Wireless Sensor Networks, Sampling methods for Big data mining, Development of CAT (Computer Aided Test) tools and statistical models for AMS and RF circuit testing.