AI-enhanced multi-stage learning-to-learning approach for secure smart cities load management in IoT networks

Abstract

In the context of rapidly urbanizing smart cities reliant on IoT networks, efficient load management is critical for sustainable energy use. This paper proposes an AI-enhanced Multi-Stage Learning-to-Learning (MSLL) approach tailored for secure load management in IoT networks. The proposed approach leverages MMStransformer, a transformer-based model designed to handle multivariate, correlated data, and to capture long-range dependencies inherent in load forecasting. MMStransformer employs a multi-mask learning-to-learning strategy, optimizing computational efficiency without compromising prediction accuracy. The study addresses the dynamic and complex nature of smart city data by integrating diverse environmental and operational variables. Security and privacy concerns inherent in IoT networks are also addressed, ensuring secure data handling and communication. Experimental results demonstrate the efficacy of the proposed approach, achieving competitive performance compared to traditional methods and baseline models. The findings highlight the potential of AI-driven solutions in enhancing load forecasting accuracy while ensuring robust security measures in smart city infrastructures. This research contributes to advancing the state-of-the-art in AI applications for sustainable urban development and energy management.