Adaptive Industrial Edge Intelligence: A Reinforcement Learning Approach

As intelligent factories evolve, the need to process large volumes of real-time data and manage complex computational tasks places increasing demands on resource-limited devices, while traditional cloud computing struggles to meet low-latency and real-time processing requirements. Multi-access Edge Computing (MEC) helps reduce latency and energy consumption by intelligently offloading tasks to edge nodes. However, in industrial scenarios where task requests and network conditions frequently fluctuate, traditional static methods fail to meet real-time demands. Static resource allocation in task offloading often leads to mismatches between allocated resources and dynamic service requirements. To address this issue, we propose an adaptive industrial edge intelligence algorithm to optimize resource management for task offloading in dynamic environments. By performing real-time task analysis and making adjustments during execution, this approach enhances the efficiency of resource utilization. Simulations demonstrate that the proposed strategy significantly improves resource allocation and system performance in dynamic environments.