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This research carried out a thorough data-driven examination of user behaviour, adoption rates, satisfaction, and energy efficiency in the context of IoT in home automation, within the quickly changing environment of smart homes and Internet of Things (IoT) technologies. The study found that users interacted with various kinds of IoT devices in diverse ways. Smart security systems and thermostats, for example, were quickly adopted and received high levels of satisfaction. The potential for significant energy savings demonstrated the contribution of IoT devices to sustainability. These results highlight the significance of making well-informed decisions when using IoT technology to create smarter, more efficient, and greener living environments. They also provide useful insights for manufacturers, legislators, and homeowners.

This research compares and contrasts the evolution of smart cities using a comparative analysis based on the Data-Driven Smart City Index. The study includes four important tables: the Digital City Development Index, which shows City D as a model smart city because of its high Infrastructure, Data Utilization, and Connectivity Scores, offers a thorough summary of the development of smart cities. The three components of the Data-Driven Smart City Index are Environmental Sustainability, Governance, and Quality of Life. City D excels in all three areas. The importance of big data analytics, IoT adoption, and open data usage—all of which City D leads—is emphasized in Data Utilization in Digital City Development. Lastly, Connectivity Infrastructure in Digital Cities emphasizes the significance of cutting-edge technology, with City D leading the way in terms of availability of public Wi-Fi, 5G network connectivity, and fiber broadband coverage. These results provide insightful information that will help stakeholders, politicians, and urban planners advance cities into the digital age and improve the quality of life for citizens.

Data from a variety of sample cities was evaluated as part of a research looking into the integration of big data analytics into urban planning. The goals were to evaluate the impact of data analytics infrastructure, data volume and processing time, urban development initiatives, and data analytics efficiency. The results showed significant differences in data analytics resources across cities, indicating different levels of investment and preparedness for data-driven decision making. It was clear that cities could handle large amounts of data efficiently thanks to their strong data processing skills. Data analytics have an impact on urban development initiatives, highlighting the revolutionary potential of data-driven urban planning. The outcomes of efficiency tests demonstrated how data analytics procedures are useful for improving urban services and for making well-informed judgments. This study offers important new insights into the mechanics of data-driven urban planning and how it can influence how cities develop in the future.