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Sinopsis
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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Acerca del autor
Uzair Bhatti (Senior Member, IEEE) was born in 1986. He received his Ph.D. degree in Information and Communication Engineering from Hainan University, Haikou, China. In 2021, he completed his postdoctoral research in Earth Observation and Remote Sensing at Nanjing, China. He is currently serving as an Associate Professor in the School of Information and Communication Engineering, Hainan University. Dr. Bhatti has published more than 300 research papers in internationally recognized journals and conferences. In addition, he is the inventor of 22 patents and has served as both Principal Investigator (PI) and participant in several national and provincial research-funded projects in China. He has also edited eight books and conference proceedings published by Springer Nature, Taylor & Francis, and IGI Global. His research interests include Artificial Intelligence, Intelligent Systems, Remote Sensing, Medical Image Analysis, Geographic Information Systems (GIS), and Machine Learning. He has served as a Session Chair at numerous IEEE conferences, including PRAI, DLCV, and IEEE FIT, and has also acted as an Editor and Guest Editor for several SCI- and EI-indexed journals
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