Investigation of IoT and Deep Learning Techniques Integration for Smart City Applications

Purpose: The purpose of this article is the integration of Internet of Things (IoT) devices and deep learning techniques have been investigated to enhance smart city applications. This investigation addresses a critical challenge: the absence of standardized methods for data collection, processing, and analysis that optimize the interplay between these technologies. Materials and Methods: The research design employed in this study is a qualitative approach. Using extensive data acquisition from IoT sensors, urban infrastructure metrics, and evaluations of deep sustainability outcomes. This data is processed using deep learning algorithms to provide actionable insights. This study underscores the importance of interdisciplinary collaboration in the advancement of smart city solutions, as it facilitates more responsive and adaptive healthcare services in smart cities. Findings: The findings of this research reveal that the synergistic application of IoT and deep learning streamlines data-driven decision-making processes and increases operational efficiencies within urban healthcare systems. The study identified that the deep-learning models utilized large datasets, which holds promise for real-time analytics in urban environments. Implications to Theory, Practice and Policy: Moreover, it contributes to theoretical frameworks that elucidate the integration pathways for IoT and deep learning in smart cities, thus filling key gaps. Key findings revealed that by leveraging IoT for data acquisition and utilizing deep learning for data analysis, cities can improve urban management functions, such as traffic control, public safety, and resource allocation.