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Timely Information Updating for Mobile Devices Without and With ML Advice

Yu-Pin Hsu, Yi-Hsuan Tseng

TL;DR

The paper addresses timely information updating in mobile systems by balancing AP timeliness (AoI) with device update costs under non-stationary, adversarial uncertainty. It introduces a virtual-queue based LP formulation that enables online scheduling without future knowledge, achieving optimal competitive ratios that scale with the update-cost range. Extending the framework to ML advice, the authors establish a provable consistency–robustness trade-off, with a threshold-like behavior determining whether to fully trust or ignore ML guidance. Numerical results corroborate the theory in stochastic settings and underline the practical viability of the approach for adaptive, energy-aware update scheduling in IoT and mobile networks.

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.

Timely Information Updating for Mobile Devices Without and With ML Advice

TL;DR

The paper addresses timely information updating in mobile systems by balancing AP timeliness (AoI) with device update costs under non-stationary, adversarial uncertainty. It introduces a virtual-queue based LP formulation that enables online scheduling without future knowledge, achieving optimal competitive ratios that scale with the update-cost range. Extending the framework to ML advice, the authors establish a provable consistency–robustness trade-off, with a threshold-like behavior determining whether to fully trust or ignore ML guidance. Numerical results corroborate the theory in stochastic settings and underline the practical viability of the approach for adaptive, energy-aware update scheduling in IoT and mobile networks.

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.

Paper Structure

This paper contains 38 sections, 14 theorems, 70 equations, 12 figures, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. System overview
  4. Age of information
  5. Problem formulation
  6. Scheduling Classification
  7. Linear program formulation
  8. Virtual queueing system
  9. LP formulation
  10. Online scheduling algorithm design without ML
  11. Online LP algorithm
  12. Analysis of Alg. \ref{['lp-alg']}
  13. Randomized online scheduling algorithm
  14. Online scheduling algorithm design with ML
  15. Online LP algorithm with ML
  16. ...and 23 more sections

Key Result

Lemma 4

For a fixed slot $t$, after the virtual packets in $P(t)$ have activated $n$ times since slot $t$, the value computed by Alg. lp-alg satisfies for all $i \in P(t)$.

Figures (12)

  • Figure 1: An example network model: (a) a mobile device updating an AP; (b) the age of information at the AP when the device sends updates in slots 3 and 5.
  • Figure 2: Virtual queueing system.
  • Figure 3: Asymptotic competitive ratio $(e^{1/R})/(e^{1/R}-1)$ for finite values of $R$.
  • Figure 4: Robustness $(e^{ \lambda/R})/(e^{ \lambda/R} - 1)$ (in log scale) versus consistency $(\lambda e^{ \lambda/R})/(e^{ \lambda/R} - 1)$.
  • Figure 5: Performance of online algorithms without ML under a linear aging function, for transition probabilities $\mathrm{tr}_p = 0.2$ and $\mathrm{tr}_q = 0.8$.
  • ...and 7 more figures

Theorems & Definitions (33)

  • Definition 1
  • Definition 2
  • Remark 3
  • Lemma 4
  • proof
  • Lemma 5
  • proof
  • Theorem 6
  • proof
  • Lemma 7
  • ...and 23 more