Industrial Internet of Things and Advanced Techniques for Sensor Data Aggregation and Fusion

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. Kanak Kalita, PhD is an accomplished professor and researcher in the field of Computational Engineering with more than ten years of experience. He has published more than 190 articles and five edited book volumes. His research interests include machine learning, fuzzy decision making, metamodeling, process optimization, the finite element method, and composites. S. Vishnu Kumar, PhD is an Assistant Professor in the Department of Electronics and Communication Engineering at the Vel Tech Rangarajan Dr. Sagunthala Research and Development Institute of Science and Technology. He has proven his expertise through publication and industrial consultancy projects, including the publication of five scientific research articles, two book chapters, and six research papers presented at international conferences. His research areas include embedded machine learning, Internet of Things, networking, and embedded system design. M. Niranjanamurthy, PhD is an Assistant Professor in the Department of Artificial Intelligence and Machine Learning at the Bhusanayana Mukundadas Sreenivasaiah Institute of Technology and Management. He has published 25 books and 95 articles in various national and international conferences and journals and filed 30 patents, six of which were granted. His areas of interest are data science, machine learning, e-commerce, and m-commerce.