DESIGNED TECHNIQUES FOR SMART CITIES USING MACHINE LEARNING IN THE INTERNET OF THINGS

Abstract

This article delves into the notion of smart cities, as well as the significance of the Network of Things (also known as IoT) and machine learning (ML), particularly in achieving a cantered around data smart ecosystem. Smart cities use technology and data to improve inhabitants' quality of life and enhance the effectiveness of urban services. IoT research has transformed services, particularly in smart cities. IoT applications are used in smart cities requiring the participation of individuals. The concept of the smart city has been used to deal with the usage of computer technology and serves as a response to the economic, social, and political issues that post-industrial governments face around the turn of this century. The study and method generated reveal that the random forest approach was the best option for analysing information in the self-configuration of electronics and communications networks, and that edge computing has a boost in terms of energy constraint and latency. The primary emphasis is on coping with urban society's difficulties, such as pollution, a changing demographic expanding populations, and healthcare, as well as the economic collapse or shortage of supplies. On the other hand, the IoT expansion has considerably spawned different study avenues for smart cities. With smart city use cases in mind, the study that is suggested introduces the analytical networking process (ANP) for analysing smart cities. The ANP method works effectively when the environment is of complexity, and there is some confusion among the available possibilities. The intended approach's testing findings suggest that it is useful for assessing smart city initiatives for IoT using use cases.