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Empowering IoT with Big Data Analytics Intelligent Data-Centric Systems Series

Langue : Anglais

Coordonnateurs : Serhani Mohamed Adel, Xu Yang, Maamar Zakaria

Couverture de l’ouvrage Empowering IoT with Big Data Analytics

Empowering IoT with Big Data Analytics provides comprehensive coverage of major topics, tools, and techniques related to empowering IoT with big data technologies and big data analytics solutions, thus allowing for better processing, analysis, protection, distribution, and visualization of data for the benefit of IoT applications and second, a better deployment of IoT applications on the ground. This book covers big data in the IoT era, its application domains, current state-of-the-art in big data and IoT technologies, standards, platforms, and solutions. This book provides a holistic view of the big data value-chain for IoT, including storage, processing, protection, distribution, analytics, and visualization. Big data is a multi-disciplinary topic involving handling intensive, continuous, and heterogeneous data retrieved from different sources including sensors, social media, and embedded systems. The emergence of Internet of Things (IoT) and its application to many domains has led to the generation of huge amounts of both structured and unstructured data often referred to as big data.

1. Foundations of IoT and Big data 2. IoT technologies and architectures 3 .Big data architectures for IoT 4. Big data analytics for IoT 5. Machine learning models for sensory data analytics 6. Real time big data Analytics 7. Applications of Big data analytics in IoT 8. Security and privacy of IoT applications 9. Big data Quality Assessment in the IoT era 10. Cognitive IoT applications 11. Cloud/edge provisioning to support big data and IoT 12. Challenges of Big data an IoT 13. New trends in big data and IoT applications and solutions 14. Case studies of Big Data applications for IoT

Mohamed Adel Serhani is a full professor at the College of Computing and Informatics, Sharjah University, Sharjah, United Arab Emirates. He holds a PhD in Electrical and Computer Engineering from Concordia University and MSc in Software Engineering from University of Montreal, Canada. He has extensive experience earned throughout his involvement and management of different research and development projects. He has served on several organizing and technical program committees for many international conferences, and workshops (e.g., ICWS, SERVICES, IIT, IWCMC). He has published more than 150 refereed publications, including conferences, journals, a book, and book chapters. His research interests include Federated Learning, Cloud for data intensive e-health applications, and services; SLA enforcement in cloud data centers, and big data value chain; Cloud federation and monitoring, and non-invasive smart health monitoring; management of communities of web services; and web services applications and security.
Yang Xu is the Yaoshihua Chair Professor at the School of Computer Science in Fudan University, China. His research interests include software-defined networks, data center networks, distributed machine learning, edge computing, network function virtualization, and network security. Dr. Xu has published more than 100 journal and conference papers and holds more than 10 U.S. and international granted patents on various aspects of networking and computing. He served as a TPC member for many international conferences, as an editor for the Journal of Network and Computer Applications (Elsevier), and as a guest editor for the IEEE Journal on Selected Areas in Communications–Special Series on Network Softwarization & Enablers and Wiley Security and Communication Networks Journal–Special Issue on Network Security and Management in SDN.
Prior to joining Fudan University, he was a research associate professor in the Department of Electrical and Computer Engineering, N
  • Introduces fundamental concepts of big data analytics and their applications to IoT
  • Helps readers learn to leverage big data storage, processing and analysis tools, and techniques to promote IoT applications for better decision-making
  • Explores federated learning in big data to ensure data privacy and handle data heterogeneity