NEXT-GENERATION 5G SMART GRIDS: A DEEP NEURAL NETWORK AND IOT-ENABLED SECURE DATA-SHARING FRAMEWORK FOR INTELLIGENT AND RESILIENT ENERGY SYSTEMS

Abstract

The rapid evolution of next-generation communication technologies and the Internet of Things (IoT) has transformed traditional power networks into intelligent cyber-physical ecosystems known as smart grids. However, the massive interconnection of IoT devices, sensors, and distributed energy resources (DERs) introduces unprecedented challenges in data security, privacy, and interoperability. To address these challenges, this study proposes a Deep Neural Network (DNN) and IoT-enabled secure data-sharing framework integrated within a 5G communication environment for next-generation smart grids. The framework combines the ultra-low latency and high bandwidth of 5G networks with the adaptive learning capabilities of DNNs to enable intelligent, real-time energy data analytics, fault detection, and predictive control. The proposed system architecture is structured into three functional layers: the IoT perception layer for distributed data acquisition, the 5G communication layer for ultra-reliable low-latency transmission, and the DNN-driven intelligence layer for energy prediction, anomaly detection, and system optimization. A blockchain-based data-sharing mechanism is embedded to ensure immutability, decentralization, and trust among heterogeneous grid nodes, thereby eliminating the risks of single-point failures and unauthorized data manipulation. Smart contracts are utilized to automate peer-to-peer data validation and secure access control among participating entities, enhancing transparency and traceability within energy transactions. The DNN module employs hybrid learning combining convolutional and recurrent neural networks to process multi-dimensional grid data, including voltage, current, frequency, and load demand patterns, enabling high-accuracy forecasting and resilience assessment. Extensive simulations demonstrate that the integration of blockchain consensus mechanisms with deep learning-based decision intelligence achieves a 35–40% improvement in data integrity and 30% reduction in latency compared to conventional centralized systems. Furthermore, the synergy of 5G connectivity and IoT sensing supports massive machine-type communications (mMTC) and real-time monitoring of distributed assets across wide geographical regions. This research highlights a holistic paradigm for secure, intelligent, and autonomous smart grid operations, where 5G-enabled IoT networks, DNN-based analytics, and blockchain-driven trust mechanisms collectively strengthen energy resilience, cyber-defense, and operational efficiency. The proposed framework sets a foundation for future integration of federated learning, edge computing, and quantum-resistant encryption to achieve sustainable, secure, and self-optimizing power infrastructures.