Preface

A smart city is a city that uses technology to provide services and solve city problems. The main goals of a smart city are to improve policy efficiency, reduce waste and inconvenience, improve social and economic quality, and maximize social inclusion. Due to the breadth of technologies that have been implemented under the smart city label, it is difficult to distill a precise definition of a smart city. As the world’s population continues to urbanize – by 2050, 66% of the world’s population is expected to be urban – there is a global trend toward the creation of smart cities. This tendency not only causes many physical, social, behavioural, economic, and infrastructure issues, but it also creates many opportunities. Increased understandings of how to design, adapt, and operate smart cities intelligently and effectively is required to solve these obstacles in implementing smart cities. This endeavour “Facets of a Smart City: Computational and Experimental Techniques for Sustainable Urban Development”, seeks to collect a coherent whole of studies aimed at the best computational and experimental techniques developed for building smart cities.

This book aims to complement technical journal articles that require advanced knowledge of the subject matters on smart cities and application from their readers and aims to bridge the knowledge gap by providing background information via case studies that recent graduates and new practitioners usually lack.

This book is divided into six major domains, which include (i) information modelling, (ii) internet of things, (iii) intelligent transportation systems, (iv) water supply, (v) waste management and (vi) sustainable environment. The editors hope this book will offer a ‘quick-start background’ on computational and experimental techniques for sustainable urban development for smart cities via case studies for recent graduates, early-career practitioners or experts who want to dabble into a new sub-field of computation and its diverse applications. This book also covers computational techniques, including artificial neural networks, stochastic models, particle swarm optimization, machine learning, adaptive neuro-fuzzy Inference System, etc. Goals of the case studies presented in this book using these computational techniques to offer readers examples of supervised, unsupervised and reinforcement learning strategies in the context of smart city applications.