Timely Information Updating for Mobile Devices Without and With ML Advice
This addresses the problem of efficient information updating for mobile devices in adversarial environments, offering theoretical guarantees but is incremental as it builds on existing online algorithm frameworks.
The paper tackles the trade-off between information timeliness and update cost in mobile device monitoring systems, proposing an online algorithm that achieves the optimal competitive ratio against adversarial uncertainties and an ML-augmented version that attains the optimal consistency-robustness trade-off, with the competitive ratio scaling linearly with update cost range.
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, information staleness, update cost, and 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 sources of uncertainty. 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.