Task Offloading and Resource Optimization Based on Predictive Decision Making in a VIoT System

With the exploration of next-generation network technology, visual internet of things (VIoT) systems impose significant computational and transmission demands on mobile edge computing systems that handle large amounts of offloaded video data. Visual users offload specific tasks to cloud or edge computing platforms to meet strict real-time requirements. However, the available scheduling and computational resources for offloading tasks constantly destroy the system's reliability and efficiency. This paper proposes a mechanism for task offloading and resource optimization based on predictive perception. Firstly, we proposed two LSTM-based decision-making prediction methods. In resource-constrained scenarios, we improve resource utilization by encouraging edge devices to participate in task offloading, ensuring the completion of more latency-sensitive request tasks, and enabling predictive decision-making for task offloading. We propose a polynomial time optimal mechanism for pre-emptive decision task offloading in resource-abundant scenarios. We solve the 0-1 knapsack problem of offloading tasks to better meet the demands of low-latency tasks where the system's available resources are not constrained. Finally, we provide numerical results to demonstrate the effectiveness of our scheme.