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As the size of the population is growing and the capacity of the planet Earth is limited, human beings are searching for sustainable and technology-enabled solutions to support society, ecology and economy. One of the solutions has been developing smart sustainable cities. Smart sustainable cities are cities as systems, where their infrastructure, different subsystems and different functional domains are virtually connected to the information and communication technologies (ICT) and internet via sensors and devices and the Internet of Things (IoT), to collect and process real-time Big Data and make efficient, effective and sustainable solutions for a democratic and liveable city for its various stakeholders. This chapter explores the concepts and practices of sustainable smart cities across the globe and explores the use of technologies such as IoT, Blockchain technology and Cloud computing, etc. their challenges and then presents a view on business models for sustainable smart cities.

A Digital Twin (DT) is a digital replica of an artefact which is updated on real-time or semi–real-time basis. In 2017, Gartner listed DT as one of the top 10 emerging technologies of the year. Since then, there have been numerous attempts to develop architecture and reference models for DTs, and in some studies, DT construction for real-world case studies is reported. This chapter attempts to provide a contextualised background on DT for smart cities. It also discusses various stakeholders involved in devising and/or employing DTs in a smart city. The chapter concludes with a set of recommendations for the training requirements of final DT users.

Globalization, along with its technological developments, has brought important changes and developments in all sectors and led to innovation and modernization. In the tourism industry, the concept of smart destination, derived from the concept of smart city, has become widespread to meet the changing needs of tourists. In this context, smart technologies have started to be used in destinations, but more than technology in destinations is needed in terms of sustainability. To ensure sustainability in tourism, these smart technologies must also be eco-friendly. Smart destinations and eco-friendly practices are important for the sustainable development of the tourism industry. The use of technology in destinations can increase sustainability and efficiency, while using eco-friendly practices can protect and preserve the natural environment. In this study, first of all, smart destinations and the characteristics of smart destinations are discussed. Then, smart tourism and related eco-friendly practices are argued. Following this, information about the future of smart destinations is given, and assumptions are mentioned. Afterward, problems and solutions are discussed. Finally, the study has been completed with the conclusion part. The findings of the study revealed that the concept of green especially comes to the fore in smart destinations, that there are studies to prevent all kinds of pollution (air, noise, etc.), and applications such as smart parking systems, smart lighting systems, augmented reality (AR), and virtual reality (VR) are given importance.

Cities evolved into quite complex urban systems. The rigid management process must reflect the complexity of the current political, social, and economic environment. With the vast city growth, citizens experience new difficulties – traffic congestion, pollution, immigration, overcrowding, and inadequate services.

In our research, we analyze problems and benefits that occur with the growing complexity and offer a new concept considering every city as a live and constantly developing complex adaptive system of many participants and actors that operate in an uncertain environment. These actors (residents, businesses, transport, energy, water supply providers, entertainment, and others) are the main elements of city life.

The new concept of “Smart City 5.0” is based on a previously developed model of Smart City 4.0 (compared with Industry 4.0) and implements the Urban Digital Ecosystem, where every element can be represented by a smart agent operating on its behalf. It is shown that smart services can interact vertically and horizontally in the proposed ecosystem, supporting competition and cooperation behavior based on specialized network protocols for balancing the conflicting interests of different city actors.

The chapter describes the design principles and the general architecture of the Urban Digital Ecosystem, including the basic agent of smart service, protocols of the agent’s negotiation, the architecture, and basic principles Smart City knowledge base.

The developed evolutionary methodology of implementation will ensure a minimum of disruptions to city services during its transformation into an urban ecosystem to harmoniously balance all spheres of life and the contradictory interests of different city actors.

As the world continues to urbanise, cities face increasing pressure to become more sustainable, efficient and livable. Sustainable smart cities are emerging as a promising solution to this challenge, leveraging technology and data to improve urban systems and services while reducing environmental impact. This chapter provides an overview of the concept of sustainable smart cities and its implications for urban development. It explores the key features of sustainable smart cities, including their focus on technology, data and citizen engagement and the challenges they are facing in terms of infrastructure, data management, social equity, environmental sustainability, governance and regulations. The chapter also highlights the implications of sustainable smart cities for urban planners, policymakers and other stakeholders, emphasising the need for collaborative approaches that engage citizens and stakeholders in the design and implementation of smart city initiatives.

Today, the rapid increase in the population living in cities and new developments in information and communication technologies (ICTs) increase the demand for smart cities day by day. It is thought that limited public resources and crowded cities will be managed better by making more use of the opportunities offered by smart technologies. At this point, many countries around the world are turning to smart city applications. Especially after the Covid-19 pandemic, local governments have started to give importance to smart city projects due to the advantages of smart cities. It is thought that urban planning against epidemics will gain more importance in the coming years.

The world has witnessed three major individual revolutions until now. We are in the fourth industrial revolution, and there are technological breakthroughs that have not been seen before. Responding fast to changing consumer expectations in a competitive climate brought on by globalization has become a global reality, requiring enterprises to alter their manufacturing systems. The incorporation of machines that can interact and make decisions into production has altered the manufacturing processes. The application of the Industry 4.0 revolution to manufacturing processes has paved the way for the development of smart factories. Production may be made 24 hours a day in these factories where productivity grows with applications such as the internet of things (IoT), cyber-physical systems, augmented reality and artificial intelligence. All applications utilized in smart factories boost productivity and reduce costs and human error rates. Countries should undergo change in order to adapt to the competitive climate established by Industry 4.0, in which the entire world lives. Many industrialized countries have taken significant strides in this direction, including this process into their national policies. Turkey's ability to adapt to Industry 4.0 technologies in a digitalized competitive environment, as well as swiftly grow smart factory applications in altering production processes, is critical to its global economic standing.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

One of the most hyped concepts in the manufacturing industry is ‘Industry 4.0’. The ‘Industry 4.0’ concept is grabbing the attention of every manufacturing industry across the globe because of its immense applications. This phenomenon is an advanced version of Industry 3.0, combining manufacturing processes and the latest Internet of Things (IoT) technologies. The main advantage of this paradigm shift is efficiency and efficacy in the manufacturing process with the help of advanced automated technologies. The concept of ‘Industry 4.0’ is contemporary, so it falls under exploratory study. Therefore, the research methodology is thematic narration grounded on secondary data (online) analysis. In this light, this chapter aims to explain ‘Industry 4.0’ in terms of concepts, theories and models based on the Web of Science (WoS) database. The data include research manuscripts, book chapters, blogs, white papers, news items and proceedings. The study details the latest technologies behind the ‘Industry 4.0’ phenomenon, different business intelligence technologies and their practical implications in some manufacturing industries. This chapter mainly elaborates on Industry 4.0 frameworks designed by (1) PwC (2) IBM (3) Frost & Sullivan.