Low-Latency Task Offloading Optimization for IoT Consumer Electronics Scenarios

In latency-sensitive IoT Consumer Electronics (CE) scenarios, effective task offloading is pivotal for ensuring interaction quality. However, existing approaches often overlook fine-grained task urgency, while standard Deep Reinforcement Learning (DRL) suffers from slow convergence and high inference latency. To address these challenges, this paper proposes a semantic-aware optimization framework grounded in the Holographic Counterpart (HC) model. First, the Historical-Experience-Driven Dynamic Priority Task Allocation and Offloading (HE-DPTAO) method is developed, which leverages semantic priorities and edge caching to minimize queuing delays and task drop rates. Subsequently, the Offloading Decision Method based on MDPRL (ODM-MDPRL) is introduced, integrating self-attention mechanisms and asynchronous prioritized experience replay to accelerate training efficiency and decision precision. Simulation results demonstrate that this coordinated framework significantly outperforms representative baselines, achieving a superior balance between timeliness, stability, and resource utilization.