Industrial Internet Of Things And Advanced Techniques For Sensor Data Aggregation And Fusion (Kanak Kalita, S. Vishnu Kumar, M. Niranjanamurthy)

Catergory: Computer Technology, Nonfiction

Master the complexity of modern networks with this essential guide, which provides the state-of-the-art AI and machine learning techniques needed to execute seamless sensor data fusion and energy-efficient aggregation across Industrial IoT and smart city environments.

The use of artificial intelligence and machine learning techniques for data aggregation and fusion is becoming increasingly important, as these technologies can help extract important features and knowledge from data. Sensor data aggregation and fusion are essential components of IoT and Industrial IoT systems, as they enable the combination of data from multiple sources to provide a more comprehensive view of the system being monitored. This book is a comprehensive guide to the state-of-the-art techniques and methods used for sensor data aggregation and fusion in IoT and Industrial IoT environments, covering the fundamental principles of data aggregation and fusion, as well as the latest advancements and applications in the field. The book takes a practical approach to the subject matter, providing a deeper understanding of the challenges and opportunities associated with sensor data aggregation and fusion in IoT and Industrial IoT environments. It covers topics such as machine learning-based data aggregation, intelligent multi-sensor fusion, data aggregation and fusion in smart cities, and energy-efficient data aggregation and fusion. Written by leading experts in the field, the book will provide a comprehensive overview of the latest advancements in sensor data aggregation and fusion in IoT and Industrial IoT environments.

Contents of Download:

📌 Industrial Internet Of Things And Advanced.epub (Kanak Kalita, S. Vishnu Kumar, M. Niranjanamurthy) (2026) (39.55 MB)

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⭐️ Industrial Internet Of Things And Advanced Techniques For Sensor Data Aggregation And Fusion ✅ (40.56 MB)
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