EFFICIENT IOT DATA ANALYTICS FOR SCALABLE AND EFFICIENT SMART SMART CITIES USING INTELLIGENT MACHINE LEARNING FRAMEWORKS

Abstract

The rapid growth of Internet of Things (IoT) devices in modern smart cities has led to an unprecedented surge in data generation. This massive increase in data creates significant challenges in terms of efficient management, timely analysis, and informed decision-making. To address these issues, this study explores the role of machine learning (ML) techniques in improving IoT data analytics within smart city environments, particularly focusing on real-time processing, scalability, and predictive performance. This research proposes a comprehensive framework that integrates various machine learning algorithms to enhance the efficiency and intelligence of urban IoT systems. Specifically, the study evaluates and compares the performance of Convolutional Neural Networks (CNN), Feedforward Neural Networks (FNN), and Deep Neural Networks (DNN) using smart city datasets related to traffic control, environmental monitoring, and public safety.The findings indicate that CNN outperforms the other models in terms of accuracy, precision, and recall, making it a strong candidate for real-time smart city applications. The study highlights how optimized IoT data analytics can significantly improve urban management, resource allocation, and overall quality of life for citizens. However, the research also acknowledges ongoing challenges, including data integration complexities, as well as concerns related to security and privacy. Addressing these issues remains essential for future advancements and successful deployment of intelligent smart city solutions.