This book presents the result of an innovative challenge, to create a systematic literature overview driven by machine-generated content. This machine-generated volume, with chapter introductions by the human expert, of summaries of the existing studies furthers our understanding of the Internet of Things in the Era of Machine Learning. This book illuminates the opportunities that machine learning (ML) offers to IoT and provides a glimpse of the state-of-the-art in this area. The book is organized into ten chapters with each chapter gathering recent publications that tackle how ML has served a specific constituent of the IoT platform. The main objective of the book is to shed light on ML-driven research for the design and development of IoT technologies.

Questions and related keywords were prepared for the machine to query, discover, collate, and structure by Artificial Intelligence (AI) clustering. The AI-based approach seemed especially suitable to provide an innovative perspective as the topics are indeed both complex, interdisciplinary and multidisciplinary. Springer Nature has published much on these topics in its journals over the years, so the challenge was for the machine to identify the most relevant content and present it in a structured way that the reader would find useful. The automatically generated literature summaries in this book are intended as a springboard to further discoverability. They are particularly useful to readers with limited time, looking to learn more about the subject quickly and especially if they are new to the topics. Springer Nature seeks to support anyone who needs a fast and effective start in their content discovery journey, from the undergraduate student exploring interdisciplinary content to Master- or PhD-thesis developing research questions, to the practitioner seeking support materials, this book can serve as an inspiration, to name a few examples.

It is important to us as a publisher to make advances in technology easily accessible to our authors and find new ways of AI-based author services that allow human-machine interaction to generate readable, usable, collated, research content.

Mounib Khanafer is a Professor of Electrical and Computer Engineering and currently serves as Associate Dean of the College of Engineering and Applied Sciences at the American University of Kuwait. He received his B.Sc. degree (with honors) in Electrical Engineering from Kuwait University in 2002, and his M.A.Sc. and Ph.D. degrees in Electrical and Computer Engineering from the University of Ottawa, Canada, in 2007 and 2012, respectively. He has gained valuable research experience at internationally recognized institutions, including the Interuniversity Microelectronics Centre (IMEC), Belgium, and the Communications Research Centre (CRC), Canada, along with three years of industry experience at Nortel Networks, Canada. He also completed a postdoctoral fellowship at the University of Ottawa (2012–2013), where he conducted research in wireless sensor networks. His research interests include wireless sensor networks and the Internet of Things (IoT), with particular emphasis on communication protocols, applications, privacy, and security. Dr. Khanafer has authored over 40 peer-reviewed journal and conference publications and has secured several research grants in these areas. He is a recipient of the Dartmouth–AUK Fellowship (2022), held at Dartmouth College, USA. In addition, he is a Senior Member of IEEE, an ABET Program Evaluator, and a licensed Professional Engineer (P.Eng.) in Ontario, Canada.

Mohammed El-Abd is a Professor of Computer Engineering at the American University of Kuwait (AUK), where he currently serves as Dean of the College of Engineering and Applied Sciences. He is a Senior Member of IEEE and an ABET Program Evaluator. Since 2020, Prof. El-Abd has been named among the top 2% of researchers in Artificial Intelligence as per the Stanford University – Elsevier list. He earned his Ph.D. in Electrical and Computer Engineering from the University of Waterloo, Canada, in 2008. He received his B.Eng. and M.Sc. degrees in Electrical and Computer Engineering from Ain Shams University, Egypt, in 1998 and 2003, respectively. He was awarded the AUK–Dartmouth Fellowship in 2012. Prof. El-Abd has an extensive research portfolio, with over 100 publications, including journal articles, conference papers, book chapters, and abstracts. He serves as an Associate Editor for several leading journals, including IEEE Transactions on Artificial Intelligence (TAI), IEEE Transactions on Learning Technologies (IEEE-TLT), Swarm and Evolutionary Computation (SWEVO), and Computers & Electrical Engineering. He has also served as a Guest Editor for the IEEE Transactions on Education (ToE). He is the founding chair of the IEEE Symposium on Cooperative Metaheuristics (IEEE-SCM) and served as General Co-Chair of the 2023 IEEE Global Engineering Education Conference (EDUCON). His research interests include metaheuristics, evolutionary computation, swarm intelligence, cooperative algorithms, continuous and large-scale optimization, real-world applications, the Internet of Things (IoT), smart cities, and engineering education.