Resilient Healthcare Internet of Things Systems: Architectures, Security, and Trust

The integration of Internet of Things (IoT) technologies into healthcare systems revolutionizes patient monitoring, diagnostics, and treatment delivery. However, this growing interconnectivity introduces new challenges related to system resilience, security, and trust. Resilient healthcare IoT systems must ensure continuous and reliable operation in the face of failures or cyber threats while safeguarding sensitive medical data and maintaining user confidence. This calls for robust architecture that withstands disruptions, advanced security mechanisms to mitigate vulnerabilities, and trust models that promote transparency and accountability. As healthcare relies on smart, connected devices, developing resilient, secure, and trustworthy IoT systems becomes critical for ensuring patient safety and system integrity. Resilient Healthcare Internet of Things Systems: Architectures, Security, and Trust explores the design and implementation of robust architectures for healthcare Internet of Things (IoT) systems. It examines the security challenges and trust frameworks necessary to protect sensitive medical data and ensure reliable operation in complex, interconnected healthcare environments. This book covers topics such as medical technologies, cybersecurity, and healthcare systems, and is a useful resource for medical and healthcare professionals, computer engineers, academicians, researchers, and scientists.

Professor Chin-Shiuh Shieh is a distinguished academic and researcher in the field of electronic engineering and computer science, currently serving as a Professor in the Department of Electronic Engineering at National Kaohsiung University of Science and Technology (NKUST), Taiwan. He received his Ph.D. in Computer Science and Engineering from the National Sun Yat-sen University, Taiwan, and has since dedicated his career to pioneering research and teaching in areas including computer networking, wireless communication, information security, computational intelligence, embedded systems, and medical informatics. With a career spanning several decades, Professor Shieh has made significant contributions to both academia and industry. In the past five years alone, he has authored or co-authored 28 journal papers, of which 24 are indexed in SCI (Science Citation Index), and 80 conference papers, encompassing 26 international and 54 domestic conferences. His editorial acumen is evident in his role as editor for two international academic conference proceedings. Professor Shieh has been actively engaged in various research initiatives, participating in 10 Ministry of Science and Technology (MOST) projects, leading five of them. He has also been involved in three Ministry of Education projects, one of which he led, and a campus research project. Furthermore, his strong industry collaboration is reflected in 10 industry-academia cooperation projects, eight of which he led. He has also contributed to one technology transfer authorization. As a recognized figure in the global research community, Professor Shieh is affiliated with four professional societies, and serves as a reviewer for 106 academic journals. He has participated as a committee member for 21 journals and 175 international conferences, underscoring his influence and expertise in his fields of specialization. Among his recent notable publications is the 2024 SCI-indexed paper titled “Open-set recognition in unknown DDoS attacks detection with reciprocal points learning” published in IEEE Access, which highlights his innovative work in cybersecurity and artificial intelligence. He has also played key editorial roles in conferences such as the International Conference on Genetic and Evolutionary Computing (ICGEC) and Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP). His work has earned multiple accolades including Best Paper Awards and Excellent Paper Awards in international conferences. He has co-developed cutting-edge methods in machine learning, optimization algorithms, and adversarial defense mechanisms, particularly in the domain of cybersecurity threats such as DDoS attacks. His academic journey, prolific publication record, leadership in research projects, and commitment to interdisciplinary collaboration have established him as a leading voice in advancing electronic engineering and computing sciences in Taiwan and beyond.

Dr. Pawan Kr. Goel , is a accomplished academician and researcher with 19.5 years of experience, is an Associate Professor at Raj Kumar Goel Institute of Technology, Ghaziabad. He holds a Ph.D. in Computer Science Engineering and is UGC NET qualified. His expertise spans various domains, including wireless sensor networks, cloud computing, and artificial intelligence. Dr. Goel has published extensively in prestigious journals and conferences, with notable papers on topics such as cybersecurity, IoT, and machine learning. He is an active member of numerous professional bodies, including the IEEE, Computer Society of India and the International Association of Engineers. Recognized for his contributions, he has received several awards and certificates of appreciation. Dr. Goel is dedicated to fostering industry-academia collaborations and has organized numerous workshops and seminars. He is also involved in various training programs, MOOCs, and NPTEL certifications, contributing significantly to the advancement of education and research in his field.

Mong-Fong Horng received his B.S., Ms. and Ph.D degrees from National Chiao Tung University and National Cheng Kung University, Taiwan in 1989, 1991 and 2003, respectively. He now is a jointly-appointed professor with the department of Electronics Engineering at National Kaohsiung University of Science and Technology, and Kaohsiung Medical University, Taiwan. He served as the president of Taiwanese Association of Consumer Electronics (TACE), Chair of Tainan Chapter, IEEE Signal Processing Society. His research interest includes Internet of Things, machine learning, computer networks and medical informatics and related industrial cooperation.

Hari Mohan Pandey works in data science and artificial intelligence at school of technology Bournemouth University, UK. They are featured in the 2022 World Ranking list of Top 2% scientist by Sandford University. They specialized in Computer Science & Engineering. My research area includes artificial intelligence, soft computing techniques, natural language processing, language acquisition, machine learning, deep learning and computer vision. They have been an author of various books in computer science engineering (algorithms, programming and evolutionary algorithms). Recently, my book entitle “State of the Art on Grammatical Inference Using Evolutionary Method " has been published in Elsevier. They have published over 120 scientific papers in reputed journals and conferences. They are serving on the editorial board of reputed journals (including Neural Networks Elsevier, Applied Soft Computing Elsevier, Swarm and Evolutionary Computing Elsevier, Neural Computing and Applications Springer, IEEE Transactions of Evolutionary Computation, IEEE Transactions on Industrial Informatics, Neurocomputing Springer, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Neural Networks and Learning Systems and Knowledge Based Systems Elsevier) as action editor, associate editor, and guest editor. They are a reviewer of top international conferences such as GECCO, CEC, IJCNN, BMVC, AAAI etc. They have delivered expert talks as keynote and invited speaker. They are a fellow of the HEA of the UK Professional Standards Framework (UKPSF) and have a rich teaching experience at the higher education level. They have delivered lecturers in the International summer/winter schools. I have been given the prestigious award “The Global Award for the Best Computer Science Faculty of the Year 2015”, award for completing INDO-US project “GENTLE”, award (Certificate of Exceptionalism) from the Prime Minister of India and award for developing innovative teaching and learning models for higher-education. In past, They worked as a sr. lecturer in computer science department at Edge Hill University. They also worked as a research fellow machine learning at the school of technology at Middlesex University London where they worked on a European Commission project- DREAM4CARS. My role was to research and develop advanced machine learning techniques relevant to the project goals and to evaluate these on both project & reference data sets. To lead and manage relevant work packages in support of the project, ensuring appropriate interfacing with partners.