Fundamentals of Fog Computing and the Internet of Things for Smart Healthcare explores the intersection of two transformative technologies, fog computing and the IoT, shedding light on how they are revolutionizing healthcare. The book serves as an essential guide for researchers and graduate students, explaining the underlying concepts, operational benefits, and potential challenges of these technologies. By delving into how fog computing enhances real-time data processing, the book's authors provide invaluable insights into the practical applications of these advancements in the healthcare industry.
Additionally, together with its companion book, Advances in Fog computing and the Internet of Things for Smart Healthcare, the series provides a comprehensive understanding of how these technologies are enabling more efficient, personalized, and accessible healthcare services. By facilitating smart applications and services across various industries, fog computing optimizes performance, latency, privacy, and overall system efficiency, ultimately contributing to the development of more effective and responsive IoT ecosystems.
1. Overview of Fog computing and IoT-based technology in smart healthcare systems
2. Determine the potential benefits and challenges of implementing fog computing and IoT in healthcare
3. The challenges and opportunities of implementing fog computing and IoT in healthcare
4. Artificial intelligence-enabled security Systems for 6G wireless networks: Algorithms, strategies, and applications
5. Methods for characterization and evaluation of Fog computing and IoT-based technology
6. Sustainable of Smart Healthcare based on Fog computing and IoT-based technology
7: Regulatory Frameworks, Services, Policy, and Operations of Fog computing and IoT-based technology
8. Challenges and Opportunities for smart healthcare systems
9. Fog computing and IoT-based technology security and privacy in smart healthcare systems
10. Artificial Intelligence (AI)-enabled Schemes for fog computing and IoT-based technology
11. Privacy protection for fog computing and IoT-based technology in smart healthcare systems
12. Cryptography based for fog computing and IoT-based technology in smart healthcare systems
13. Centralized fog computing security platform for IoT and cloud in healthcare system
Dr. Joseph Bamidele Awotunde is a Lecturer in the Department of Computer Science, Faculty of Communication and Information Sciences, University of Ilorin, Ilorin, Nigeria. His area of research interest cut across Artificial Intelligence, Internet of Things, Cybersecurity, Information Security, Social Computing, Bioinformatics, and Biometrics. He has to his credit over one hundred and fifty publications in reputable outlets like Elsevier, Springer, Hindawi among others covering journals, edited conference proceedings and chapters in books. He was part of the team that won the Artificial Intelligence for Females in Science Technology, Engineering and Mathematics (AI4FS) Grant sponsored by Royal Academy of Engineering (Higher Education Partnerships in sub-Saharan Africa (HEP SSA) 22/24). He is an Associate Editor of the PLOS ONE, and IEEE Transactions on Neural Systems & Rehabilitation Engineering. He is also a member of many professional bodies within and outside Nigeria such as International Association of Engineers and Computer Scientist (MIAENG), Computer Professional Registration Council of Nigeria (MCPN), and Nigeria Computer Society (MNCS). Internet Society.
Dr. Akash Kumar Bhoi accomplished academic and researcher with over a decade of experience in Computer Science and Engineering, Artificial Intelligence, Machine Learning, Data Science, and Biomedical Computing. Recognized globally for research excellence, ranked in the Top 0.5% of Scholars worldwide (ScholarGPS, 2024 & 2025) and listed among the Worlds Top 2% Scientists (Stanford University & Elsevier, 2022, 2023 & 2025). Prolific contributor with 200+ Scopus-indexed publications, multiple edited books with Springer/Elsevier, and an h-index of 39 (Google Scholar). Dr. Bhoi is currently associated with Graphic Era Deemed to be University, Dehradun. More than a decade of academic & research experience in India, Italy, Australia, and South Korea. Expertise spans computational intelligence, data analytics, healthcare informatics, biomedical signal and image processing, cloud/edge computing, and IoT applications.
Paolo Barsocchi is a researcher at the Information Science and Technologies Institute (ISTI) of the National Research Council (CNR) at Pisa, Italy. He received his M.Sc. and Ph.D. degrees in information engineering from the University of Pisa in 2003 and 2007, respectively. Since 2017 he is Head of the Wireless Networks Research Laboratory. He is currently involved in several European projects, national and regional projects. The overall amount of attracted and managed funds both at European and national level is about 3M. He has been nominated as a regional competence reference person for advanced manufacturing solutions in Industry 4.0 in 2017, and as a contact person in the Cluster-PON call in 2017 for the CNR Department DIITET, which ISTI belongs to. His research interests are in the areas of internet of things (IoT), wireless sensor networks, cyberphysical systems, machine learning and data analysis techniques, smart environments, ambient assisted living, activity recognition and indoor localization. Victor Hugo C. de Albuquerque [ M17, SM19] is a collaborator Professor and senior researcher at the Graduate Program on Teleinformatics Engineering at the Federal University of Ceará, Brazil, and at the Graduate Program on Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza/CE, Brazil. He has a Ph.D in Mechanical Engineering from the Federal University of Paraíba (UFPB, 2010), an MSc in Teleinformatics Engineering from the Federal University of Ceará (UFC, 2007), and he graduated in Mechatronics Engineering at the Federal Center of Technological Education of Ceará (CEFETCE, 2006). He is a specialist, mainly, in Image Data Science, IoT, Machine/Deep Learning, Pattern Recognition, Robotic.
