Enhancing security in IoT-based photovoltaic monitoring with hybrid key management and deep learning optimized routing

The modern power infrastructure faces significant challenges in ensuring reliable and efficient electricity delivery amid rapidly increasing demand across various sectors. This research proposes an Internet of Things (IoT)-based smart grid system integrating Shuffled Frog Leaping Algorithm Optimized Recurrent Neural Network (SFLA-RNN) routing protocol to find the shortest route to reach end user, with Hybrid Paillier Improved Blow Fish (HPIBF) algorithm for key management, adding an extra degree of data protection. The system’s operational status is visualized using the Adafruit IoT dashboard. The validation of developed system is examined using NS2 software and the outcomes reveals superior results with improved Packet Delivery Ratio (PDR) of 98.95%, reduced consumption of energy to 0.024 mJ (100 nodes), longer network lifetime up to 3881 rounds (500 nodes) and minimized latency to 1.6–4.1 s compared to state of art topologies. Moreover, the proposed HPIBF approach achieves encryption and decryption times of 15 ms and 0.35 ms, respectively, outperforming existing algorithms. This confirms that the proposed research on IoT-based monitoring systems lowers operating expenses by improving energy efficiency through the reduction of power loss during transmission and distribution.