A Smart and Energy-Efficient Framework for Micro Electric IoT Applications Leveraging Deep Learning

This research introduces an innovative and energy-efficient framework that leverages advanced deep learning techniques, specifically Bayesian Neural Networks (BNN), and the Dragonfly Algorithm to optimize energy usage in micro electric IoT environments. The framework integrates BNNs, known for their probabilistic modeling capabilities, to enhance predictive analytics and decision-making processes. By infusing uncertainty estimates into the model predictions, the system achieves more informed and adaptive responses, thereby reducing unnecessary energy expenditures during periods of low activity. The Dragonfly Algorithm is employed for dynamic resource allocation, allowing devices to intelligently adapt to varying workloads in real-time. Through extensive experiments, we observe a notable reduction of 0.17 in mean-square error (MSE) and 0.8 reduction in MAE and 0.7 reduction in RMSE and 8.8 error reduction in MAPE.