Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment

Abstract

This paper explores the concept of smart cities and the role of the Internet of Things (IoT) and machine learning (ML) in realizing a data-centric smart environment. Smart cities leverage technology and data to improve the quality of life for citizens and enhance the efficiency of urban services. IoT and machine learning have emerged as key technologies for enabling smart city solutions that rely on large-scale data collection, analysis, and decision-making. This paper presents an overview of smart cities’ various applications and discusses the challenges associated with implementing IoT and machine learning in urban environments. The paper also compares different case studies of successful smart city implementations utilizing IoT and machine learning technologies. The findings suggest that these technologies have the potential to transform urban environments and enable the creation of more livable, sustainable, and efficient cities. However, significant challenges remain regarding data privacy, security, and ethical considerations, which must be addressed to realize the full potential of smart cities.

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

“…AI encompasses a wide variety of models and technologies, including ML, DL, CV, NLP, Evolutionary Computing (EC), and robotics. These models have been applied to various urban planning and design functions [ [1] , [2] , [3] , [4] , [5] , [6] ] in the context of environmental sustainability in sustainable smart cities [ 7 , 8 , 21 , 22 , [24] , [27] , [66] , [67] ]. The integration of AI technologies into urban planning and design processes underscores the pivotal role of AI in advancing the development of sustainable smart cities.…”

Proactive Edge Computing for Smart City: A Novel Case for ML-Powered IoT

“…Several recent advancements in edge computing technologies [5] have contributed significantly to the exploration and understanding of proactive edge computing for IoT devices in the context of smart cities. Ullah et al [9] explores the concept of smart cities and the role of the Internet of Things (IoT) and machine learning (ML) in realizing a data-centric smart environment.…”

Energy efficient reliable data transmission for optimizing IoT data transmission in smart city

“…The adjusted interconnecting area 𝜔(𝑑) for IoT sensor node 𝑑 can be obtained by utilizing (6) with the condition 𝑟 = 𝑃 ̅ 2𝑃 ⁄ . According to the mathematical expression denoted in (7), the standardized active region of sensing can be determined using (12). From the preceding equation, the probability that an IoT sensor node will serve as CH increases for the lower values of 𝜇(𝑑), and decreases for greater values of 𝜇(𝑑).…”

“…However, this integration also poses challenges, such as concerns related to data privacy and latency issues, necessitating careful consideration in the design and implementation of smart city frameworks [6]. The success of a smart city depends significantly on its ability to harness the potential of the data generated 1979 by IoT sensor nodes [7]. Efficient data collection and analysis are crucial for obtaining actionable insights into urban processes, optimizing resource utilization, and enhancing the overall quality of life for citizens [8].…”