Internet of Things (IoTs) are now being integrated at a large scale in fast-developing applications such as healthcare, transportation, education, finance, insurance and retail. The next generation of automated applications will command machines to do tasks better and more efficiently. Both industry and academic researchers are looking at transforming applications using machine learning and deep learning to build better models and by taking advantage of the decentralized nature of Blockchain. But the advent of these new technologies also brings very high expectations to industries, organisations and users. The decrease of computing costs, the improvement of data integrity in Blockchain, and the verification of transactions using Machine Learning are becoming essential goals.
This edited book covers the challenges, opportunities, innovations, new concepts and emerging trends related to the use of machine learning, Blockchain and Big Data analytics for IoTs. The book is aimed at a broad audience of ICTs, data science, machine learning and cybersecurity researchers interested in the integration of these disruptive technologies and their applications for IoTs.
Internet of Things (IoTs) are now being integrated at a large scale in fast-developing applications such as healthcare, transportation, education, finance, insurance and retail. The next generation of automated applications will command machines to do tasks better and more efficiently. Both industry and academic researchers are looking at transforming applications using machine learning and deep learning to build better models and by taking advantage of the decentralized nature of Blockchain. But the advent of these new technologies also brings very high expectations to industries, organisations and users. The decrease of computing costs, the improvement of data integrity in Blockchain, and the verification of transactions using Machine Learning are becoming essential goals.
This edited book covers the challenges, opportunities, innovations, new concepts and emerging trends related to the use of machine learning, Blockchain and Big Data analytics for IoTs. The book is aimed at a broad audience of ICTs, data science, machine learning and cybersecurity researchers interested in the integration of these disruptive technologies and their applications for IoTs.
Amit Kumar Tyagi is an assistant professor and senior researcher at
the School of Computer Science and Engineering, Vellore Institute
of Technology (VIT), Chennai Campus, Chennai, Tamil Nadu, India.
His current research focuses on machine learning with big data,
blockchain technology, data science, cyber physical systems, smart
& secure computing and privacy. He has contributed to several
projects such as "AARIN" and "P3-Block" to address some of the open
issues related to the privacy breaches in vehicular applications
(such as parking) and medical cyber physical systems. He is a
member of the IEEE. He received his PhD from Pondicherry Central
University, India.
Ajith Abraham is the director of Machine Intelligence Research Labs
(MIR Labs), Australia. MIR Labs are a not-for-profit scientific
network for innovation and research excellence connecting industry
and academia. His research focuses on real world problems in the
fields of machine intelligence, cyber-physical systems, Internet of
things, network security, sensor networks, Web intelligence, Web
services, and data mining. He is the Chair of the IEEE Systems Man
and Cybernetics Society Technical Committee on Soft Computing. He
is editor-in-chief of Engineering Applications of Artificial
Intelligence (EAAI) and serves on the editorial board of several
international journals. He received his PhD in Computer Science
from Monash University, Melbourne, Australia.
Farookh Khadeer Hussain is an associate professor with the School
of Software, University of Technology Sydney, Australia. He is also
an associate member of the Advanced Analytics Institute and a core
member of the Centre for Artificial Intelligence. His key research
interests include trust-based computing, cloud of things,
blockchains, and machine learning. He has published widely in these
areas in top journals, such as FGCS, the Computer Journal, JCSS,
IEEE Transactions on Industrial Informatics, and IEEE Transactions
on Industrial Electronics. He holds a PhD from Curtin University,
Perth, Australia.
Arturas Kaklauskas is a professor at Vilnius Gediminas Technical
University, Lithuania. His areas of interest include affective
computing, Internet of Things, Big Data and text analytics,
intelligent event prediction, opinion mining, intelligent decision
support systems, neuro-marketing, intelligent tutoring systems,
massive open online courses (MOOCS), smart built environment,
energy and resilience management. He is editor-in-chief of the
Journal of Civil Engineering and Management, editor of Engineering
Applications of Artificial Intelligence, and associate editor of
Ecological Indicators Journal. His publications include nine books.
The Belarusian State Technological University (Minsk, Belarus) has
awarded him an Honorary Doctorate.
R. Jagadeesh Kannan is a professor at the School of Computer
Science and Engineering, Vellore Institute of Technology, Chennai,
Tamil Nadu, India. His research focuses on semantic web, network
security, software engineering, and artificial intelligence. He is
an active member of the Indian Society for Technical Education
(ISTE), Computer Society of India (CSI), Software Process
Improvement Network (SPIN), International Association of Engineers
(IAENG), Association of Computer Electronics & Electrical &
Engineers (ACEEE), International Association of Computer Science &
Information Technology (IACSIT), Research GATE - Scientific
Network, Society of Digital Information and Wireless Communications
(SDIWC), Computer Science Teachers Association (CSTA), and the
International forum of Researchers Students and Academician
(IFRSA). He received a PhD from Anna University, Tamil Nadu, India
in 2011.
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