Real-Time Hard Peak Age-of-Information Safety with No-Regret Learning

2026-07-29

AI Summary

This work addresses the challenge of enforcing hard deadline constraints on peak age of information (PAoI) for sensors in safety-critical IoT systems, where each time slot must strictly satisfy real-time requirements. The authors formulate hard real-time scheduling as an online convex optimization problem with time-varying constraints and polyhedral safety sets. They innovatively translate PAoI hard constraints into affine half-space constraints on resource allocation vectors and introduce a “propose-mask-update” mechanism that guarantees zero deadline violations and no-regret learning with only a single Euclidean projection per slot. Theoretical contributions include static and dynamic regret bounds, a minimax lower bound, a noise-robust variant, and a deadline-induced competitive ratio. Experiments on a four-sensor system under adversarial channel conditions demonstrate zero violations across all random seeds—significantly outperforming baselines (violation rates: 1.65%–64.0%)—while empirical regret consistently remains below the theoretical bound.

Abstract

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.

Problem

• Peak Age-of-Information
• Hard Real-Time
• Safety-Critical IoT
• Deadline Constraint
• Adversarial Environment

Innovation

• Peak Age-of-Information
• Hard Real-Time Constraints
• Online Convex Optimization
• No-Regret Learning
• Adversarial Scheduling