Abstract
Over the past decade, intelligent cities have undergone rapid transformation. The definition of an intelligent city may vary based on the context and the purpose served. However, the next generation of intelligent cities will have unique characteristics that will set them apart from the existing intelligent cities. They will be more people-centered, and they will be formed through the fusion of technology, government, organizations, and people. This chapter explores four intelligent cities in Europe that are setting examples for innovation, ingenuity, technology, public policy making, and sustainable development: London, Amsterdam, Vienna, and Stockholm. With growing emphasis on people involvement in decision making, the intelligent city ecosystem is continuously evolving. However, technology continues to play a prominent role in shaping the intelligent city paradigm. In this contribution, the authors also examine different emerging technologies such as quantum computing, autonomous vehicles, AI, ML, etc. that could potentially impact the next generation of such cities.
TopLiterature Review
Although extensive research and experimentation have been carried out on the concept of intelligent cities with numerous pilot projects around the globe, there is no standard definition of a smart city. Many cities around the world have adopted intelligent projects and smart way of solving specific problems. Hence generalizing the term ‘smart city’ or ‘intelligent city’ to apply to all cities has been difficult. This has paved way for companies, governments and authors to adopt their own definitions of a smart city, depending on the context. A Smart City according to Giffinger et al. (2007) is “A City performing in a forward-looking way in economy, people, governance, mobility, environment, and living, built on the smart combination of endowments and activities of self-decisive independent and aware citizens”. According to Caragliu and Nijkamp (2009), “A city can be defined as 'smart' when investments in human and social capital and traditional (transport) and modern communication infrastructure fuel sustainable economic development and a high quality of life, with a wise management of natural resources, through participatory action and engagement”.
Key Terms in this Chapter
Industrial Internet of Things (IIoT): It refers to an interconnected network of sensors, IoT devices, IoT gateways and instruments in the manufacturing and industrial sector. This connectivity enables the digitalization of the industry by facilitating data collection, data storage and analysis leading to improvement in efficiency and productivity.
Quadratic Unconstrained Binary Optimization (QUBO): This is a pattern matching technique that is used in machine learning, especially with quantum annealing. It can be formulated as minimizing a quadratic polynomial over binary variables.
PV Cell: This a Photo Voltaic cell, also called a solar cell, that generates electricity from the energy of light using the photovoltaic effect. Series of PV cells are combined together to form solar panels.
Anthropic Climate Change: It refers to the impact of human activities on the Earth’s climatic conditions. Also termed as Global warming, it alludes to the increase in global surface temperature and melting glaciers due to emission of greenhouse gases. The frequency and severity of extreme weather events such as super cyclones, floods, heat waves and droughts are on rise around the world, as a direct consequence of man-made climate change.
Convolutional Neural Networks (CNN): They are a class of deep neural networks that are generally used to analyze image data. They use convolution instead of simple matrix multiplication in a few layers of the network. They have shared weights architecture and have translation invariant characteristics.
Breeder Nuclear Reactor: These are a class of reactors that generate more radioactive fissile material than what it needs to use. It works by irradiating a fissile material such as Thorium-232 that is loaded along with the fissile fuel and thereby creating more neutrons.
Capacity Factor: This is defined as the ratio of the electrical energy output over a given period of time to the maximum possible electrical energy output over the same period.
Quantum Annealing: Quantum annealing is used to optimize discrete binary optimization problems by finding the global minima of a given function. It is useful in solving problems where there are large numbers of local minima present. D-Wave Systems introduced the first quantum annealer in 2011, named D-Wave One.
Autonomous Vehicles (AV): Autonomous vehicles or self-driving vehicles or driverless vehicles are vehicles that operate without the need of a human driver. These vehicles are driven by advanced inter-connected systems of cameras, sensors, instruments and integrated circuits. They are powered by advanced technologies such as artificial intelligence, machine learning and deep neural networks.
Generative Adversarial Network (GAN): It refers to a type of neural network that consists of a generative and a discriminative network that contest with each other especially in a game scenario. They are used to generate new data that are statistically similar to the training data.
Single Shot MultiBox Detector (SSD): It is a method used to detect objects in images using a deep neural network. It is especially powerful when used to detect multiple objects in the same image, providing excellent speed and accuracy.