The internet of things (IoT) has emerged to address the need for connectivity and seamless integration with other devices as well as big data platforms for analytics. However, there are challenges that IoT-based applications face including design and implementation issues; connectivity problems; data gathering, storing, and analyzing in cloud-based environments; and IoT security and privacy issues. Emerging Trends in IoT and Integration with Data Science, Cloud Computing, and Big Data Analytics is a critical reference source that provides theoretical frameworks and research findings on IoT and big data integration. Highlighting topics that include wearable sensors, machine learning, machine intelligence, and mobile computing, this book serves professionals who want to improve their understanding of the strategic role of trust at different levels of the information and knowledge society. It is therefore of most value to data scientists, computer scientists, data analysts, IT specialists, academicians, professionals, researchers, and students working in the field of information and knowledge management in various disciplines that include but are not limited to information and communication sciences, administrative sciences and management, education, sociology, computer science, etc. Moreover, the book provides insights and supports executives concerned with the management of expertise, knowledge, information, and organizational development in different types of work communities and environments. Title Page Copyright Page Book Series Table of Contents Detailed Table of Contents Preface Acknowledgment Chapter 1: A Comprehensive Evaluation of Internet-of-Things Platforms Chapter 2: An Overview of IoT Infrastructure Architecture, Enabling Technologies, Issues, Integration of Cloud, and Simulation Tools Chapter 3: Location-Based Internationalization and Localization With Mobile Computing Chapter 4: Interoperability in Internet of Media Things and Integration Big Media Chapter 5: An Architecture and Reference Implementation for WSN-Based IoT Systems Chapter 6: Connectivity Estimation Approaches for Internet of Things-Enabled Wireless Sensor Networks Chapter 7: Machine Learning Techniques for IoT-Based Indoor Tracking and Localization Chapter 8: XHAC Chapter 9: Animal Activity Recognition From Sensor Data Using Ensemble Learning Chapter 10: Essentials, Challenges, and Future Directions of Agricultural IoT Chapter 11: Optimization of Consensus Mechanism for IoT Blockchain Chapter 12: Critical Nodes Detection in IoT-Based Cyber-Physical Systems Chapter 13: Mesh Network of eHealth Intelligent Agents for Visually Impaired and Blind People Chapter 14: Prototype Implementation of Innovative Braille Translator for the Visually Impaired With Hearing Deficiency Compilation of References About the Contributors Index Provides theoretical frameworks and research findings on IoT and big data integration. Highlighting topics that include wearable sensors, machine learning and intelligence, and mobile computing, this book serves professionals who want to improve their understanding of the role of trust at different levels of the information and knowledge society. "This book contains research on emerging trends in the internet of things and integration with data science"-- Provided by publisher
