Konsep Inti
The author proposes an optimization control system based on deep reinforcement learning and edge computing to enhance real-time monitoring and control in IoT environments.
Abstrak
The content introduces a system that combines deep reinforcement learning and edge computing to optimize real-time monitoring and control in industrial IoT settings. By leveraging cloud-edge collaboration, lightweight policy networks at the edge, and dynamic resource allocation, the system predicts system states, outputs controls at high frequency, reduces latency, accelerates response to abnormal situations, lowers failure rates, extends equipment operating time, and saves costs. The architecture includes sensor acquisition layer, edge computing layer, cloud computing layer with service-oriented components for flexibility and scalability. Key technologies like lightweight deep reinforcement learning algorithms and dynamic collaborative distributed optimization algorithms are discussed. Experimental results show significant improvements in communication time reduction between cloud and edge layers, lower control latency, improved resource utilization, enhanced control stability, action accuracy, and overall performance compared to traditional cloud-centered systems.
Statistik
Results demonstrate that this approach reduces cloud-edge communication latency.
The lightweight model contains about 1 million parameters.
CPU usage at the edge layer increased from 53% to 67%.
The DRL method scored an average reward of 3820 points over a month.
The DRL controller reduced water and temperature anomalies by predictive models.
Kutipan
"The architecture is service-oriented and modular."
"Experimental results show significant improvements in communication time reduction between cloud and edge layers."
"The DRL method showed significant improvements over traditional PID control."