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This reference presents information about different facets of IoT and blockchain systems that have been recently proposed for practical situations. Chapters provide knowledge about how these technologies are applied in functions related to trust management, identity management, security threats, access control and privacy. Key Features:- Introduces the reader to fundamental concepts of IoT and blockchain technology- reports advances in the field of IoT, ubiquitous computing and blockchain computing - includes the applications of different frameworks- explains the role of blockchains in improving IT security- provides examples of smart grids, data transmission models, digital business platforms, agronomics and big data solutions- Includes references for further reading Blockchain Applications for Secure IoT Frameworks: Technologies Shaping the Future is a handy reference for information technology professionals and students who want updated information about applications of IoT and blockchains in secure operational and business processes.

Bentham Science

Advances in Computing Communications and Informatics

Blockchain Applications for Secure IoT Frameworks: Technologies Shaping the Future

This reference provides the reader with focused information about microstrip antenna design and applications. Readers are first introduced to the basic design of microstrip antennas. Subsequent chapters explain how microstrip antennas are suitable for practical applications. These chapters cover topics such as fractal and defected ground structure antennas, microstrip antenna evaluation and the use of microstrip antennas in mobile communications and IoT applications. Scholars, researchers and industrial professionals involved in the fields of electronics and electrical engineering as well as instrumentation will benefit from the information given in this book.

Smart Antennas: Recent Trends in Design and Applications

Data Science and Interdisciplinary Research: Recent Trends and Applications is a compelling edited volume that offers a comprehensive exploration of the latest advancements in data science and interdisciplinary research. Through a collection of 10 insightful chapters, this book showcases diverse models of machine learning, communications, signal processing, and data analysis, illustrating their relevance in various fields.

Key Themes:

Advanced Rainfall Prediction: Presents a machine learning model designed to tackle the challenging task of predicting rainfall across multiple countries, showcasing its potential to enhance weather forecasting.
Efficient Cloud Data Clustering: Explains a novel computational approach for clustering large-scale cloud data, addressing the scalability of cloud computing and data analysis.
Secure In-Vehicle Communication: Explores the critical topic of secure communication in in-vehicle networks, emphasizing message authentication and data integrity.
Smart Irrigation 4.0: Details a decision model designed for smart irrigation, integrating agricultural sensor data reliability analysis to optimize water usage in precision agriculture.
Smart Electricity Monitoring: Highlights machine learning-based smart electricity monitoring and fault detection systems, contributing to the development of smart cities.
Enhanced Learning Environments: Investigates the effectiveness of mobile learning in higher education, shedding light on the role of technology in shaping modern learning environments.
Coastal Socio-Economy Study: Presents a case study on the socio-economic conditions of coastal fishing communities, offering insights into the livelihoods and challenges they face.
Signal Noise Removal: Shows filtering techniques for removing noise from ECG signals, enhancing the accuracy of medical data analysis and diagnosis.
Deep Learning in Biomedical Research: Explores deep learning techniques for biomedical research, particularly in the realm of gene identification using Next Generation Sequencing (NGS) data.
Medical Diagnosis through Machine Learning: Concludes with a chapter on breast cancer detection using machine learning concepts, demonstrating the potential of AI-driven diagnostics.

Bentham Science Publishers

Data Science and Interdisciplinary Research: Recent Trends and Applications

This volume explores IoT architectures, their configuration, and operability in wireless sensor networks.

The topics are spread across nine structured chapters covering fundamental and applied knowledge about wireless sensor networks using IoT devices. The book starts with an introduction to the subject, giving readers a quick overview of IoT enabled networks and bio-inspired approaches towards network design. This is followed by chapters explaining optimized routing protocols for accident detection, efficiency and performance analysis. The book concludes with four chapters dedicated to security applications of wireless networks, for homes, urban areas and businesses. Overall, the volume gives a balance of theoretical and practical information for readers.

The book is intended as a resource for graduate and postgraduate students for understanding network design for home and embedded applications, specifically using single board computing devices. It also serves as a guide for networking courses and assessments.

Readership

Graduate and post graduate students and faculty members in the field of networking and IoT.

IoT-enabled Sensor Networks: Architecture, Methodologies, Security, and Futuristic Applications

This book offers in-depth reviews of different techniques and novel approaches of using blockchain and artificial intelligence in smart healthcare services. The volume brings 14 reviews and research articles written by academicians, researchers and industry professionals to give readers a current perspective of smart healthcare solutions for medical and public health services.

The book starts with examples of how blockchain can be applied in healthcare services such as the care of osteoporosis patients and security. Several chapters review AI models for disease detection including breast cancer, colon cancer and anemia. The authors have included model design and parameters for the benefit of professionals who want to implement specific algorithms. Furthermore, the book also includes chapters on IoT frameworks for smart healthcare systems, giving readers a primer on how to utilize the technology in this sector. Additional use cases for machine learning for gesture learning. COVID-19 management, and sentiment analysis.

Readership

Academic, professional, and students affiliated institutions involved in digital transformation in the healthcare sector.