A PREDICTIVE HIGH EFFICIENT IOT DATA ANALYTICS MODEL-USING MACHINE LEARNING FOR SMART CITY APPLICATIONS

Abstract

With the rapid expansion of Internet of Things (IoT) devices in smart cities, massive amounts of data are being generated every second. This explosive growth creates serious challenges in terms of managing, analyzing, and extracting meaningful insights for effective decision-making. This study investigates how machine learning (ML) techniques can enhance IoT data analytics in smart city environments, particularly focusing on real-time processing, scalability, and predictive intelligence.

The research presents a comprehensive ML-based framework designed to improve the performance and efficiency of IoT systems deployed in urban settings. Several machine learning models—including Convolutional Neural Networks (CNN), Feedforward Neural Networks (FNN), and Deep Neural Networks (DNN)—are evaluated using smart-city datasets related to traffic management, environmental monitoring, and public safety. Experimental results show that the CNN model achieves superior accuracy, precision, and recall compared to other models, making it a strong candidate for real-time smart-city applications. By optimizing IoT data analytics, the proposed approach supports better urban management, improved resource allocation, and enhanced quality of life for citizens. Despite these promising results, challenges such as data integration, privacy, and security remain key concerns. These issues highlight essential directions for future work aimed at building secure, reliable, and fully scalable smart-city ecosystems.

Keywords

IoT Data Analytics, Smart Cities, Machine Learning, Convolutional Neural Networks, Real-time Data, Predictive Maintenance, Data Security, Urban Management