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JCSIS

Internet of Things-Enabled Predictive Analytics Using Deep Learning for Integrated Smart City Infrastructure Management.

Narcisa Zlatan
Volume 5

Abstract

Smart city infrastructure management requires continuous monitoring, intelligent decision-making, and proactive maintenance across highly interconnected urban systems, including transportation networks, energy grids, water distribution systems, waste management, public safety services, environmental monitoring, and building operations. The rapid expansion of Internet of Things technologies has enabled large-scale collection of real-time urban data from sensors, cameras, meters, vehicles, mobile devices, and cyber-physical infrastructure; however, transforming these heterogeneous, high-volume, and dynamic data streams into reliable predictive insights remains a major challenge. This paper proposes an Internet of Things-enabled predictive analytics framework using deep learning for integrated smart city infrastructure management. The proposed framework combines distributed IoT sensing, edge-cloud data processing, and advanced deep learning models, including Convolutional Neural Networks, Long Short-Term Memory networks, Gated Recurrent Units, Temporal Convolutional Networks, Graph Neural Networks, and Transformer-based architectures, to predict infrastructure conditions, detect anomalies, and support early intervention across multiple urban domains. The system analyzes multimodal data sources such as traffic flow, energy consumption, air quality, weather conditions, water pressure, equipment health indicators, public transport activity, surveillance streams, and citizen service requests. A comprehensive preprocessing pipeline was applied, including data cleaning, missing value imputation, noise filtering, temporal synchronization, sensor fusion, normalization, spatial-temporal feature extraction, and real-time stream segmentation. The proposed deep learning framework enables multi-task predictive analytics, including traffic congestion forecasting, energy demand prediction, water leakage detection, air pollution estimation, equipment failure prediction, and infrastructure risk assessment. Experimental results demonstrate that the proposed framework achieves superior predictive performance compared with traditional statistical models, rule-based monitoring systems, and standalone machine learning approaches, recording higher accuracy, precision, recall, F1-score, and lower RMSE, MAE, and MAPE across diverse smart city applications. Furthermore, the integration of edge computing reduces latency and communication overhead, while explainable AI techniques provide interpretable insights into the key sensor variables and spatial-temporal patterns influencing infrastructure risks. The findings indicate that IoT-enabled deep learning provides a scalable, adaptive, and intelligent solution for integrated smart city infrastructure management, enabling municipal authorities, urban planners, utility providers, and emergency response agencies to improve operational efficiency, reduce maintenance costs, enhance sustainability, and increase urban resilience.

Keywords

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