🤖 AI Summary
Mobile devices face a fundamental trade-off between timeliness and energy consumption in status updates. This paper addresses online decision-making under uncertainty—without prior knowledge—and proposes an adaptive update algorithm that integrates unreliable machine learning (ML) advice. Our method introduces a threshold-based trust mechanism for ML recommendations in adversarial settings, rigorously proving that partial trust degrades robustness. Grounded in a consistency–robustness theoretical framework, we derive the optimal competitive ratio, which scales linearly with the range of update costs. The algorithm achieves theoretical optimality under both adversarial and stochastic input models. Extensive simulations demonstrate its significant superiority over baseline approaches and strong robustness against ML prediction noise and multi-source uncertainties.
📝 Abstract
This paper investigates an information update system in which a mobile device monitors a physical process and sends status updates to an access point (AP). A fundamental trade-off arises between the timeliness of the information maintained at the AP and the update cost incurred at the device. To address this trade-off, we propose an online algorithm that determines when to transmit updates using only available observations. The proposed algorithm asymptotically achieves the optimal competitive ratio against an adversary that can simultaneously manipulate multiple sources of uncertainty, including the operation duration, the information staleness, the update cost, and the availability of update opportunities. Furthermore, by incorporating machine learning (ML) advice of unknown reliability into the design, we develop an ML-augmented algorithm that asymptotically attains the optimal consistency-robustness trade-off, even when the adversary can additionally corrupt the ML advice. The optimal competitive ratio scales linearly with the range of update costs, but is unaffected by other uncertainties. Moreover, an optimal competitive online algorithm exhibits a threshold-like response to the ML advice: it either fully trusts or completely ignores the ML advice, as partially trusting the advice cannot improve the consistency without severely degrading the robustness. Extensive simulations in stochastic settings further validate the theoretical findings in the adversarial environment.