IoT-driven smart grid monitoring with enhanced energy routing and advanced encryption techniques

Integration of Distributed Energy Resources (DERs) such as Photovoltaic (PV) system and wind into power grids has become crucial to achieve effective energy systems. Most conventional approaches either focus primarily on optimizing communication paths without ensuring strong data encryption, or they prioritize security mechanisms that significantly increase latency and energy consumption due to computational complexity. This paper proposes a robust IoT-based smart grid system in which a novel Harmonic Search Optimized Recurrent Neural Network (HSO-RNN) approach improves the shortest path routing efficiency. The proposed work incorporates Improved Paillier Homomorphic (IPH) based encryption scheme, which assures integrity and confidentiality of data transmitted. The outcomes of proposed smart grid system using MATLAB and FPGA controller demonstrate significant improvements by providing enhanced security and optimal routing. Future direction is to integrate federated learning along with post-quantum cryptographic techniques to enhance privacy and protect against future quantum attacks.