Timely Information Updating for Mobile Devices Without and With ML Advice

TL;DR

This paper develops online algorithms for mobile device information updates that balance timeliness and cost, incorporating robust machine learning advice to optimize performance even under adversarial uncertainties.

Contribution

It introduces an asymptotically optimal online algorithm and an ML-augmented version that effectively manage the trade-off between update timeliness and cost in adversarial environments.

Findings

The optimal competitive ratio scales linearly with update costs.

ML advice is either fully trusted or ignored, with no partial trust improving robustness.

Simulations confirm theoretical results in stochastic and adversarial settings.

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, 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.