Social Internet of Things (SIoT) and Machine Learning—Enhancing Interconnectivity and Intelligence
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Beschreibung
This book offers readers an innovative perspective on how intelligent networks can evolve beyond simple device-to-device communication, enabling social interaction, adaptive learning, and predictive intelligence across various domains. Interconnected systems that learn, adapt, and collaborate are transforming the way we experience technology. It highlights the practical advantages of integrating machine learning into socially structured networks of devices, opening the door to more brilliant, more responsive digital ecosystems.
A distinctive aspect of this work is its emphasis on convergence. Instead of viewing connectivity and intelligence as separate fields, it explores how devices can function as socially aware entities, capable of reasoning, decision-making, and autonomous interaction. This innovative approach demonstrates how combining social networking principles with machine learning leads to stronger interconnectivity, greater efficiency, and increased adaptability. From healthcare monitoring systems that personalise treatment to transportation networks that self-optimise traffic flows, this book showcases real-world use cases where these technologies converge to make a measurable impact.
This book’s scope encompasses theoretical foundations, emerging frameworks, and practical solutions. It introduces new models that explain how connected systems can be designed for scalability, resilience, and ethical governance, while also presenting case studies illustrating practical implementations. By combining foundational knowledge with application-driven insights, the book offers readers a comprehensive guide and a practical toolkit for navigating this rapidly evolving field.
The intended audience includes academic researchers, graduate students, and professionals working in areas such as computer science, data science, artificial intelligence, IoT, and networked systems. Industry leaders, developers, and technology strategists will likewise benefit from its actionable insights on building and deploying intelligent, socially structured networks. Furthermore, policymakers and decision-makers will find valuable discussions on ethical, security, and governance challenges, which will aid them in framing strategies for responsible adoption.
This book offers readers an innovative perspective on how intelligent networks can evolve beyond simple device-to-device communication, enabling social interaction, adaptive learning, and predictive intelligence across various domains. Interconnected systems that learn, adapt, and collaborate are transforming the way we experience technology. It highlights the practical advantages of integrating machine learning into socially structured networks of devices, opening the door to more brilliant, more responsive digital ecosystems.
A distinctive aspect of this work is its emphasis on convergence. Instead of viewing connectivity and intelligence as separate fields, it explores how devices can function as socially aware entities, capable of reasoning, decision-making, and autonomous interaction. This innovative approach demonstrates how combining social networking principles with machine learning leads to stronger interconnectivity, greater efficiency, and increased adaptability. From healthcare monitoring systems that personalise treatment to transportation networks that self-optimise traffic flows, this book showcases real-world use cases where these technologies converge to make a measurable impact.
This book’s scope encompasses theoretical foundations, emerging frameworks, and practical solutions. It introduces new models that explain how connected systems can be designed for scalability, resilience, and ethical governance, while also presenting case studies illustrating practical implementations. By combining foundational knowledge with application-driven insights, the book offers readers a comprehensive guide and a practical toolkit for navigating this rapidly evolving field.
The intended audience includes academic researchers, graduate students, and professionals working in areas such as computer science, data science, artificial intelligence, IoT, and networked systems. Industry leaders, developers, and technology strategists will likewise benefit from its actionable insights on building and deploying intelligent, socially structured networks. Furthermore, policymakers and decision-makers will find valuable discussions on ethical, security, and governance challenges, which will aid them in framing strategies for responsible adoption.
Focuses on the emerging convergence of the Social Internet of Things (SIoT) paradigm with machine learning technologies Covers the importance of data management in SIoT systems Explains that the SIoT concept draws parallels to human social networks
Autor*in
Rajeev Kumar
Themen in »Social Internet of Things (SIoT) and Machine Learning—Enhancing Interconnectivity and Intelligence«
Social Internet of Things (SIoT) Internet of Things (IoT) Machine Learning Artificial Intelligence (AI) Interconnectivity Smart Devices Autonomous Systems Deep Learning Edge Computing Fog Computing Trust Management Service Discovery Data Analytics Smart Cities Smart Homes