Abstract

Industrial Internet of Things (IIoT) edge computing supports latency-sensitive services through task offloading to distributed edge resources. However, large-scale emergencies such as node failures and traffic surges may trigger cascading failures, leading to severe performance degradation and poor post-crisis recovery. Existing offloading methods mainly optimize operational efficiency under normal conditions while overlooking resilience against cascading disruptions. To address this issue, we propose Cascade-Emergency Resilient Offloading (CERO), an adversarial deep reinforcement learning framework for resilient task offloading in IIoT edge computing. Distinct from existing works, CERO introduces a structure-aware shared node encoder to capture heterogeneous topological roles of edge nodes, providing critical structural information for cascade-aware decision making, and incorporates cascade-oriented adversarial training to enhance robustness against compound disturbances. CERO integrates structure-aware state representation, minimax adversarial training, and potential-based reward shaping to learn resource-allocation policies balancing task efficiency and system resilience. By interacting with dynamically generated crisis scenarios, the agent learns resilient offloading policies and achieves high post-crisis recovery performance after cascading disruptions. All performance evaluations are conducted via discrete-event simulation experiments. Simulation results for normal, single-crisis, and compound-crisis scenarios show that CERO achieves comparable task efficiency under normal conditions and significantly superior post-crisis recovery performance compared to conventional rule-based strategies. In the hardest compound-crisis case involving simultaneous node failures and load surges, CERO achieves a post-recovery task-completion rate of 97.8%, surpassing the best rule-based baseline by more than 63 percentage points. Statistical significance is confirmed by the Wilcoxon signed-rank test with Bonferroni correction over 10 independent runs. These results demonstrate that CERO effectively improves the robustness and recoverability of IIoT edge-computing systems under cascading emergency scenarios.

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