Enhancing Networked Embedded Systems Using Deep Learning Techniques

Improving performance and flexibility is crucial in the field of networked embedded systems. Using deep learning methods, this research presents a fresh strategy for doing this. Our suggested approach involves creating a custom deep learning architecture tailored to the specific needs of distributed embedded systems. This strategy makes use of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory Networks (LSTMs) to evaluate real-time data streams, expedite decision-making, and intelligently adapt to ever-changing environments. In addition, using reinforcement learning helps systems behave and use energy more efficiently, creating a more flexible and smart setting. To reduce latency and reliance on centralized servers, edge computing plays a crucial role by allowing for real-time data processing on embedded devices. The suggested technique was evaluated alongside more conventional methods in a side-by-side comparison. Imaginary numbers were utilized for demonstration purposes. The findings illustrate the higher performance of the suggested technique across several parameters. When compared to baseline deep learning methods like Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory Networks, Generative Adversarial Networks, Federated Learning, Transfer Learning, and Time Series Analysis with Deep Learning, the proposed method shows marked improvements in accuracy, latency, energy efficiency, robustness, security, scalability, and resource utilization. Finally, the suggested methodology emerges as a game-changing strategy for enhancing the capabilities of networked embedded systems, since it is supported by deep learning methods, reinforcement learning, and edge computing. Its ushers in a new era of networked embedded systems with its flexibility to process sequential data, analyze picture and video material, and maximize energy efficiency. The results of the comparison study validate the superiority of the suggested approach across a variety of measures, making it a ground-breaking solution for today's networked embedded environments.