Foreword

I first met one of the editors of this book at an event on signal processing and machine learning on sensor networks in Cambridge. The topic caught my attention immediately since it really matched my vision for my current and future research. I am a Senior Researcher in distributed intelligent systems at the Institute for Manufacturing at the University of Cambridge (UK). I am a fellow of the Royal Statistical Society, where I also serve as a committee member of the special interest group in statistical engineering. In 2021, I was the recipient of the Frank Hansford-Miller fellowship in applied statistics, awarded by the West area branch of the Statistical Society of Australia. It was not a coincidence that the main part of the invited lecture at receiving this fellow was about time series mining and dynamic networks as research trends in engineering statistics. Both topics are closely related to decentralised learning on complex systems. Hence, writing a foreword for this book is a great pleasure to me, as it revolves around disruptive technologies and methods associated with the application of AI and IoT to human lives, societies, and industries. The role of AI in IoT is playing an essential role in the emergence of the resilient and ubiquitous internet connection today via 5G and beyond, along with the development of the so-called cyber-physical systems present almost everywhere. This, in principle, is an exceptional combination of AI and IoT, sometimes also named AIoT, is poised to be a norm in the technology for the coming years.

A book in AIoT is well-timed and of the highest interest to researchers, engineers and decision-makers. This is because AIoT provides a complete range of scalable solutions for system operations and automation. Such scalability is based on an AI algorithm development through a decentralised approach that enables edge-computing, a key step ahead towards the digitalisation of industry and society. For instance, in areas as important as the health sector, AIoT brings the possibility of tracking the health of individual patients as well as monitoring larger cohorts if working with algorithms at a coarser scale. This is of the highest importance in controlling both individual and population disease evolution. We all have in mind the recent COVID-19 pandemic and can immediately ascertain the myriad of solutions related to AIoT. This book shows the basis for this solution and many more since it also covers topics as disparate as intelligent asset management or smart manufacturing.

The depth and mathematical beauty of a theoretical algorithm development behind AIoT can only be overcome its appeal to many researchers and engineers by the plethora of solutions and the usefulness of their associated applications. We are in front of a really disruptive way of addressing further operations and management of truly intelligent, cognitive, and adaptive systems. The opportunity of getting involved in this formidable journey to change the world starts today. Let’s make it happen!