Making Smart Cities Smarter: Role of AI in Smart Cities Application

Introduction

In the past century, there has been a persistent increase in the population of expansive urban centres and metropolitan areas. The United Nations population survey conducted in 2021 revealed that a majority (exceeding 60%) of the global population lives in these urban regions (Karim et al., 2020). Remarkably, despite the land surface occupying less than 6% of the total area of the Earth, the usage of global resources is predominantly attributed to major urban centers, accounting for approximately 75% of the total, as documented by the World Population Index survey.

By the year 2030, it is projected that there will be approximately 41 megacities and 662 city areas, with most of these urban centers located in emerging regions (Affairs, 2019). Rapid urbanization will have a significant influence on several areas of city life, including the environment, governance, education, energy, healthcare, and security.

Rapid urbanization has prompted several nations to adopt the notion of “smart cities” to reduce wasteful energy use and better manage resources (Dong et al., 2016). The implementation of smart city projects aims to address environmental concerns by promoting the use of low-carbon emission technologies. Countries such as the US, EU, Japan, India, and others are actively proposing and implementing smart city initiatives to tackle the potential challenges that lie ahead. Modern smart cities leverage a range of technologies and advancements to support their long-term socioeconomic goals and prospects (Petrolo et al. 2017).

Information and communication technologies (ICTs) are crucial for achieving a smart city vision. These technologies play a vital role in managing data analysis, communication, and effective complex strategy implementation, ensuring the seamless and secure operation of smart cities, as evident in numerous studies, thereby creating a diverse array of urban visions for the future (Al‐Turjman et al., 2019).

ICT is essential to the idea of smart cities, not just for developing policies and making decisions, but also for putting them into practice and throughout all stages of the strategy. With global expansion in various fields, Machine Learning (ML) and Artificial Intelligence (AI) have become crucial areas of research. These technologies are applied in diverse domains and smart cities, including digital learning machines, medical science, archaeology, agriculture, sports, and business (Mondal et al., 2023).

The integration of AI in cities can significantly enhance the efficiency of various aspects of urban life, such as energy management, temperature regulation, education, healthcare, water and air quality management, traffic control, payment and finance systems, parking using IoT technology, and waste management. By leveraging AI, a smart city can optimize energy utilization and resources, protect the environment, improve the well-being of its citizens, and foster faster adoption of current ICT solutions.

In addition, in the evolution of smart cities, data plays a crucial role, as it can be processed and analyzed to extract valuable insights (Hassan et al., 2022). To achieve this, ML-DL technologies were employed for in-depth data analysis and predictions across several domains, including the realm of smart cities.

However, certain challenges remain in the development of AI-driven smart cities. These include:

• 1.

  Availability and reliability of technology and data.

• 2.

  Dependence on third-party services and a lack of skilled personnel.

• 3.

  Ethical concerns related to AI usage can be complex, and there are also regulatory issues to address when it comes to interconnecting infrastructure and managing data in a smart city environment.