Читать книгу Machine Learning Paradigm for Internet of Things Applications - Группа авторов - Страница 51

In our modern world, many cities are facing big obstacles, such as a rising population, a shortage of physical and social resources, environmental, and regulatory standards, diminishing tax bases and budgets, and higher prices. They need to learn how to recognize innovative and intelligent ways of handling urban life’s complexities and challenges ranging from congestion, overcrowding, and urban sprawl to insufficient infrastructure, high unemployment, resource utilization, conservation of the environment, and increasing crime rates.

Cost efficiencies, resilient networks, and an increased local environment result from the use of smart city technology [31]. When it comes to designing the cities of the future, “smart cities” is the new term. To bring a new brand and distinctive appeal to the lifestyle, smart cities are supposed to be the cornerstone to balancing a prosperous future with sustained economic development and job production.

Cloud-based output and storage face common obstacles to smart city applications. For example, the complexities of cloud-based smart grids include cost-effective provisioning without replacing ageing infrastructure and stable integration of modern capabilities with existing networks [33]. Although the ML has both added advantage and disadvantages, it leads to the deep learning process which enhances development in the technologies like the data visualization in real-time modern world.

Development accomplished by cities is tied to their desire to holistically solve urbanization-based problems and related social, environmental, and economic issues, while at the same time making the most of potential opportunities [30]. It is possible to interpret the smart city idea as a paradigm for incorporating this vision of advanced and modern urbanization. In future, vision is the urban center of the future, making sustainable, safe, eco-friendly, and competitive as all buildings are designed, built, and controlled using new, manufactured materials, sensors, electronics, and networks integrated with computerized systems consisting of databases, surveillance, and de-connected networks.