A novel framework for privacy and data protection in internet of things-based energy management systems

The use of smart Internet of Things (IoT) devices is required to maximize the use of renewable energy, which is becoming more and more prevalent in several industries, including lighting, transportation, and electric power. Fortunately, poor energy management is limited by the supply imbalance and request for renewable energy. The advancement of smart IoT devices is also hampered by concerns about the security of energy data and user privacy. To maintain data security and privacy in intelligent IoT devices, the effort aims to balance and maintain regularity in the supply and demand of renewable energy. In this study, we proposed a novel logistic regression (LR) for privacy and data protection in IoT-based energy management techniques. To accomplish high-efficiency and secure energy use in a smart environment, the suggested strategy makes use of secure cryptographic primitives and artificial intelligence (AI) technology. The strategy's primary goal is to increase energy utilization efficiency in the smart environment's various dimensions. The proposed plan's specific goal is to improve the effectiveness of energy consumption across all smart environment elements. Three possible methodologies for the fine-grained energy administration of smart IoT devices are considered and implemented in the proposed strategy. Additionally, AI technologies are used and incorporated into the strategy for managing energy. According to the analysis, the suggested plan may fully utilize renewable energy in IoT devices. Although privacy rules can restrict data-driven insights and capabilities in smart IoT devices for managing renewable energy, these devices nevertheless improve the use of renewable energy by minimizing waste across several sectors, optimizing the allocation of resources, and dynamically distributing electricity.