Abstract
A city may be regarded as an intelligent city when its services to citizens are connected and it is able to obtain data from every aspect of its technology infrastructure to leverage it in real time for resource allocation, monitoring, management, and decision making. Cities around the globe are ambitiously leveraging the latest technologies to transform their infrastructures to better provision and manage the e-services. Although they are setting goals for focusing on the appropriate financing, long-term planning, developing technology stack, and advancing data management, governments need to further encompass all relevant guidelines towards right technology frameworks before commencing their intelligent city projects. This chapter provides a comprehensive introduction to intelligent cities, also known as smart cities, and the associated requirements. It also articulates the evolution of a typical city to a truly integrated, responsive, open, and connected intelligent city and the required underlying technologies.
TopIntroduction
In this first section, we attempt to define an Intelligent City (IC), also known as Smart City or Digital City, and what are the essential elements of such a city. However, before that, we briefly look at the evolution of cities, over the ages.
Cities Over the Ages
Cities can be simply defined as areas that serve as epicenters for commerce, trade routes, politics and culture, that have large, concentrated human settlements. Right from city states of Greece to modern metropolises of today like New York and London; Cities have been a major catalyst in shaping up civilizations. The first cities were formed when mankind discovered agriculture for their livelihood during Neolithic revolution naturally leading to denser human settlements in specific geographies, usually alongside a river with fertile soil.
Depending on the requirements and the extent of development of human race, cities have fulfilled different needs at different times. In ancient times, Egyptian cities on the banks of Nile, Mesopotamian cities on banks of Tigris-Euphrates and Harappan cities close to the Indus river served as cradles of civilization with their agriculture-based economy. At the start of this millennium, city states of Greece laid the foundation for the modern democratic system. In Medieval times, trade and commerce flourished in city states of Venice, Genoa, Milan, and Florence. Furthermore, these city states aided the Renaissance revolution paving the way for great works in the arts and sciences. The industrial revolution led to an explosion of cities across Europe followed by rest of the world. Invention of steam engine, cars and electricity abetted the rapid urbanization across the globe.
In today’s world, currently, the urban areas are home to about 55% of the world population and this is expected to grow to 68% by 2050 (United Nations Department of Economic and Social Affairs, 2018). In that case, urban spaces will not be able to support healthy and sustainable living for their people. This necessarily mandates a need for cities to transform into sustainable, connected and intelligent living spaces. As of 2019, there are 33 Megacities (Ellie Donelly, 2019) across the world with a population of more than 10 million people. catering to numerous industries, necessities and a cosmopolitan crowd. The current megacities are struggling with issues of climate change, rapid urbanization, sustainability, and successfully managing large populations, among others. A connected, engaged, intelligent city collects huge amounts of data, generated by all manner of smart objects and information systems, which then needs to be stored and analyzed in real time for resource allocation, management, and decision making. This, in turns requires mechanisms and data analytics frameworks.
The chapter is organized as follows: The immediate section discusses the definition of, and the need for an Intelligent City. The next section explains, in some depth, technology requirements for an Intelligent City and how the combination of technologies help to create unique value propositions for businesses as well as residents of a Smart City. The subsequent section will discuss Intelligent City frameworks followed by a conclusion.
In this contribution, we will use the Intelligent city and smart city terms interchangeably.
Key Terms in this Chapter
Data Lakes: Data lakes are massive repositories for original, raw and unstructured data which is collected from various sources across a smart city. The data from data lakes can be cleansed and transformed for further analytics and modeling.
Smart City: A city using internet of things (IOT) sensors to collect data and garner insights gained from the data to manage all aspects encompassing the city efficiently and transparently.
Connected City: It refers to a city where all aspects of its infrastructure and different sectors of the society are inter-connected to each other through IoT based sensors via the internet.
Deep Learning: This is also a subset of AI where unstructured data is processed using layers of neural networks to identify, predict and detect patterns. Deep learning is used when there is a large amount of unlabeled data and problem is too complex to be solved using machine learning algorithms. Deep learning algorithms are used in computer vision and facial recognition systems.
Artificial Intelligence: Artificial intelligence (AI) refers to the ability of machines to have cognitive capabilities similar to humans using advanced algorithms and quality data.
Machine Learning: This can be regarded as a subset of AI which refers to analyzing structured data and identifying trends (correlations) for specific outcomes and using that information to predict future values (causation).
Responsive City: A responsive city is a self-regulating and autonomous city space which uses advanced AI and quantum computing techniques to respond to changes and make decisions in real time without human intervention. Responsive cities are “always on” cities.
Real-Time City: This is a smart city where the information highways operate in real-time and data is transmitted and processed instantaneously for further decision making and instant actions.
Reinforcement Learning: Reinforcement learning is also a subset of AI algorithms which creates independent, self-learning systems through trial and error. Any positive action is assigned a reward and any negative action would result in a punishment. Reinforcement learning can be used in training autonomous vehicles where the goal would be obtaining the maximum rewards.