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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.
Inhaltsverzeichnis
AI and Machine Learning Driven Circular Design for Smart Manufacturing and Zero Waste Production.- Architectures and Algorithms for Socially Aware Internet of Things Systems.- IoT Integrated ML framework for Automated Soil Quality Assessment.- Blockchain-Driven Scalable Authentication for Urban IoT Applications.- Demystifying Social IoT Foundation, Features and Emerging Technologies A Scientific Review.- Enhancing Customer Experience in Retail: How Geo Fencing Technology Personalizes Shopping Journeys.- ESP32 C H2OWDT IOT driven based Sustainable real time Water drop tracker System.- Conscious Machines Exploring Maharishi Mahesh Yogi s Principles in the Design of SIoT Learning Frameworks.- Techniques and Algorithms Machine Learning Approach for Crowd Analysis.- IOT AI and Smart Cities Implementation.- Machine Learning Driven Social IoT Advancing Interconnectivity, Intelligence, and Autonomous Systems.
Zusammenfassung
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.
