Deep Learning-Based Optimization of Energy Efficiency in D2D Communication for Hospital Logistics

In this paper, we address the crucial issue of energy consumption minimization in device-to-device (D2D) communications in the healthcare logistics domain. The growing demand for real-time communication and data exchange in healthcare environments has led to an increase in the use of D2D technologies, but the associated energy consumption can have a long-term impact on the sustainability of these systems. To tackle this challenge, we propose exploring the use of neural networks to predict the energy consumption of different D2D communication protocols and optimize the parameters to minimize this consumption. The objective of this article is to provide a thorough study on the use of neural networks for energy consumption minimization in D2D communications for healthcare logistics. We will use simulations to evaluate the results of this approach, and we will discuss the implications of our conclusions for the design of sustainable communication systems in healthcare environments.