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Book synopsis

The Internet of Things (IoT) is a transformative technology reshaping the way data is collected, processed, and analyzed. By seamlessly connecting devices, edge platforms, and cloud infrastructures, IoT creates data-driven “ecosystems” that enable intelligent decision-making, advanced automation, and enhanced operational efficiency. IoT ecosystems, however, present significant challenges in integration, data management, cybersecurity, and resource optimization, thereby necessitating innovative frameworks to ensure reliability, privacy, and performance across these complex systems. Data-Driven IoT Ecosystems: Integrating Device, Edge, and Cloud explores advances in IoT technology and emphasizes the role of data science and machine learning in bridging device, edge, and cloud layers. The book covers a wide range of applications, including industrial robotic systems, health IoT analytics, sustainable green IoT solutions, environmental control and safety management, and cyber-physical systems. It also presents strategies for integration, security and deploying intelligent IoT applications in real-world environments. By integrating theoretical perspectives with applied research, the book is a resource for academics, researchers, practitioners, and students seeking to harness the full potential of data-driven IoT ecosystems. It examines how intelligent integration across devices and edge and cloud platforms can enhance performance, optimize resource management, strengthen cybersecurity, and support sustainable development. The book charts a pathway toward the next generation of IoT ecosystems by demonstrating how data-driven intelligence can enable smarter, safer, and more resilient technological infrastructures.

Book details

• Full title: Data-Driven IoT Ecosystems
• Subtitle: Integrating Device, Edge, and Cloud Paradigms
• Author: J. Naskath, Subrata Chowdhury, Soumi Dutta, Kathrin Kind-Trueller, Joanna Rosak-Szyrocka
• Series: Advances in Computational Collective Intelligence,
• Language: English
• Binding: Hardback
• Number of pages: 256
• EAN: 9781003863427
• ID: 52448486
• Publisher: Taylor & Francis Ltd
• Dimensions: 234 × 156 mm
• Date of publishing: 09. October 2026

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