A Detailed Analysis of Cloud Storage and Key Management Techniques in IoT Driven Smart Grids

The Internet of Things (IoT) technology has revolutionized smart grid settings, enhancing efficiency and enabling advanced data analytics. However, the integration of IoT devices within smart grids introduces significant challenges related to data storage and security. This survey provides a comprehensive review of the various classifiers and optimization algorithms employed to enhance cloud storage solutions and key management systems within smart grid environments. Moreover, it symmetrically examines different classifiers, like Machine Learning (ML) and Deep Learning (DL) methods, which are designed for managing cryptographic keys, focusing on their ability to secure communications, authenticate devices, and ensure data integrity in the presence of cyber threats. Additionally, it categorizes and evaluates a wide range of optimization methods, including evolutionary algorithms, particle swarm optimization (PSO), gray wolf optimization (GWO) and firefly algorithm (FFA) based techniques for optimizing the classifiers, highlighting their applicability in optimizing storage capacities, data retrieval speeds, and cost-effectiveness. By synthesizing recent advancements and comparing the efficacy of these techniques, the survey identifies the best individuals and emerging trends that promise to strengthen the resilience and efficiency of IoT-enabled smart grids. The findings promote the critical role of integrated classifiers and optimization techniques in achieving scalable, secure, and reliable smart grid infrastructures.