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IoT Network Optimization for Smart Cities Energy Management Using Advanced Neural Networks

Sam M. K.Hakan KhanNajaad OubeBlika
Volume 4

Abstract

The growing complexity of energy systems in smart cities demands efficient, adaptive, and scalable network architectures to ensure optimal energy distribution and utilization. This study explores the integration of Internet of Things (IoT) networks with advanced neural network models for energy management optimization in smart urban environments. IoT devices are employed to collect real-time data on energy consumption, generation, and environmental parameters across residential, commercial, and industrial sectors. Advanced neural networks—including deep and recurrent architectures—are implemented to analyze temporal and spatial energy patterns, predict demand fluctuations, and dynamically adjust resource allocation. The proposed framework enhances the responsiveness and resilience of energy management systems by enabling autonomous decision-making and fault detection. Experimental results highlight significant improvements in energy efficiency, load balancing, and system reliability, demonstrating the potential of neural network-optimized IoT frameworks to support sustainable smart city development.

Keywords

IoT, smart cities, neural networks, energy management, network optimization, deep learning

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