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Inhaltsangabe
The Internet of Things (IoT) is transforming the way people, devices, and systems interact by creating highly connected environments that generate and exchange vast amounts of data in real time. From smart cities and healthcare to manufacturing, transportation, and industrial automation, IoT technologies are enabling more intelligent, responsive, and data-driven operations across nearly every sector. As these connected ecosystems continue to grow in scale and complexity, there is an increasing need for advanced technologies capable of extracting meaningful insights, supporting autonomous decision-making, and adapting to dynamic environments. Deep learning has emerged as a powerful solution, enabling IoT systems to perform sophisticated tasks such as prediction, classification, optimization, and intelligent control. At the same time, the convergence of cloud and edge computing is redefining how these capabilities are delivered, creating architectures that balance computational power, scalability, efficiency, and real-time responsiveness. Deep Learning in Edge and Cloud-Based IoT Systems examines the integration of deep learning with edge and cloud computing to support the next generation of intelligent IoT applications. This book explores the theoretical foundations, deployment strategies, optimization techniques, privacy and security considerations, and emerging applications of deep learning across distributed computing environments. Covering topics such as heterogeneous and real-time IoT data analytics, privacy preservation, and device-edge-cloud continuum, this book is a fundamental academic resource for graduate and doctoral students, IoT architects, software engineers, cloud engineers, technology developers, systems engineers, policymakers, and more.
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Über die Autorin bzw. den Autor
Uzair Aslam Bhatti was born in 1986. He received the Ph.D. degree in information and communication engineering, Hainan University, Haikou, Hainan, in 2019. He is pursuing the Postdoctoral degree in implementing Clifford algebra algorithms in analyzing the geospatial data using artificial intelligence (AI) with Nanjing Normal University, Nanjing, China. His areas of specialty include AI, machine learning, and image processing.
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Hardcover. Zustand: new. Hardcover. The Internet of Things (IoT) is transforming the way people, devices, and systems interact by creating highly connected environments that generate and exchange vast amounts of data in real time. From smart cities and healthcare to manufacturing, transportation, and industrial automation, IoT technologies are enabling more intelligent, responsive, and data-driven operations across nearly every sector. As these connected ecosystems continue to grow in scale and complexity, there is an increasing need for advanced technologies capable of extracting meaningful insights, supporting autonomous decision-making, and adapting to dynamic environments. Deep learning has emerged as a powerful solution, enabling IoT systems to perform sophisticated tasks such as prediction, classification, optimization, and intelligent control. At the same time, the convergence of cloud and edge computing is redefining how these capabilities are delivered, creating architectures that balance computational power, scalability, efficiency, and real-time responsiveness. Deep Learning in Edge and Cloud-Based IoT Systems examines the integration of deep learning with edge and cloud computing to support the next generation of intelligent IoT applications. This book explores the theoretical foundations, deployment strategies, optimization techniques, privacy and security considerations, and emerging applications of deep learning across distributed computing environments. Covering topics such as heterogeneous and real-time IoT data analytics, privacy preservation, and device-edge-cloud continuum, this book is a fundamental academic resource for graduate and doctoral students, IoT architects, software engineers, cloud engineers, technology developers, systems engineers, policymakers, and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798260006153
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Hardcover. Zustand: new. Hardcover. The Internet of Things (IoT) is transforming the way people, devices, and systems interact by creating highly connected environments that generate and exchange vast amounts of data in real time. From smart cities and healthcare to manufacturing, transportation, and industrial automation, IoT technologies are enabling more intelligent, responsive, and data-driven operations across nearly every sector. As these connected ecosystems continue to grow in scale and complexity, there is an increasing need for advanced technologies capable of extracting meaningful insights, supporting autonomous decision-making, and adapting to dynamic environments. Deep learning has emerged as a powerful solution, enabling IoT systems to perform sophisticated tasks such as prediction, classification, optimization, and intelligent control. At the same time, the convergence of cloud and edge computing is redefining how these capabilities are delivered, creating architectures that balance computational power, scalability, efficiency, and real-time responsiveness. Deep Learning in Edge and Cloud-Based IoT Systems examines the integration of deep learning with edge and cloud computing to support the next generation of intelligent IoT applications. This book explores the theoretical foundations, deployment strategies, optimization techniques, privacy and security considerations, and emerging applications of deep learning across distributed computing environments. Covering topics such as heterogeneous and real-time IoT data analytics, privacy preservation, and device-edge-cloud continuum, this book is a fundamental academic resource for graduate and doctoral students, IoT architects, software engineers, cloud engineers, technology developers, systems engineers, policymakers, and more. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9798260006153
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