AI 中文总结
针对安全关键物联网系统的硬峰值信息年龄截止期限问题，提出OCO-PAoI-Hard算法，通过将调度转化为约束在线凸优化，实现零建模状态违规，性能优于多个基准方法。
AI 中文摘要
工业闭环控制、车路协同（V2X）、远程遥操作等安全关键型物联网系统，要求每个传感器的峰值信息年龄（peak Age of Information，简称peak AoI，也缩写为PAoI）需低于每个时隙的硬截止期限，而非仅满足平均约束。现有方法仅在限制性假设下满足该要求：如Whittle指数AoI的随机信道、深度强化学习的模拟器回滚、或长期约束在线凸优化的次线性累积违规。在对抗性系数下，OCO-PAoI-Hard算法在一步可行性保证建模AoI状态的每时隙违规为零，且对任意静态安全基准的遗憾为O(√T)；数据包级安全性需更强的服务假设。核心发现是，分数峰值AoI截止期限恰好可简化为资源分配向量上的仿射半空间约束，将硬实时调度转化为多面体安全集上的时变约束在线凸优化。严格因果的提议-保护-更新循环通过每时隙一次欧氏投影强制可行性，梯度步骤保留无遗憾行为，经典虚拟队列简化为后验证书。研究建立了闭式静态与动态遗憾界、匹配的Ω(√T)极小极大下界、针对执行噪声的边际安全变体、以及截止期限诱导的竞争比。在四传感器对抗性流体模型陷阱信道中，OCO-PAoI-Hard在全部10个随机种子下实现建模状态截止期限违规为零，而四个代表性基准方法的时隙违规率为1.65%至64.0%，且经验归一化遗憾在T的两个数量级范围内均低于理论包络。
英文摘要
Safety-critical IoT systems such as industrial closed-loop control, V2X coordination, and remote teleoperation require every sensor's peak Age of Information (peak AoI, also abbreviated PAoI) to stay below a hard per-slot deadline, not merely an average bound. Existing approaches meet this requirement only under restrictive assumptions: stochastic channels for Whittle-index AoI, simulator rollouts for deep reinforcement learning, or sublinear cumulative violation for long-term constrained online convex optimization. Under adversarial coefficients, OCO-PAoI-Hard guarantees zero per-slot violation of the modeled AoI state under one-step viability and O(sqrt(T)) regret against any static safe comparator; packet-level safety requires stronger service assumptions. Our key observation is that the fractional peak-AoI deadline collapses exactly to an affine half-space constraint on the resource-allocation vector, turning hard real-time scheduling into time-varying constrained online convex optimization over a polyhedral safe set. A strictly causal proposal-shield-update loop enforces feasibility through one Euclidean projection per slot, the gradient step preserves no-regret behavior, and the classical virtual queue is reduced to an a-posteriori certificate. We establish closed-form static and dynamic regret bounds, a matching Omega(sqrt(T)) minimax lower bound, a margin-safe variant against execution noise, and a deadline-induced competitive ratio. On a four-sensor adversarial fluid-model trap channel, OCO-PAoI-Hard attains zero modeled-state deadline violations across all ten seeds, while four representative baselines miss between 1.65 percent and 64.0 percent of slots, and the empirical normalized regret stays below the theoretical envelope across two orders of magnitude in T.
CommentsAccepted to 2026 IEEE Real-Time Systems Symposium (RTSS)
