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A Dual-AoI-based Approach for Optimal Transmission Scheduling in Wireless Monitoring Systems with Random Data Arrivals

Yuchong Zhang, Yi Cao, Xianghui Cao

TL;DR

A dual-AoI model that captures asynchronous AoI dynamics and formulate the problem as minimizing a long-term time-average AoI function is proposed, and a scheduling policy based on Markov decision process (MDP) is developed which outperforms existing approaches.

Abstract

In Internet of Things (IoTs), the freshness of system status information is crucial for real-time monitoring and decision-making. This paper studies the transmission scheduling problem in wireless monitoring systems, where information freshness -- typically quantified by the Age of Information (AoI) -- is heavily constrained by limited channel resources and influenced by factors such as the randomness of data arrivals and unreliable wireless channel. Such randomness leads to asynchronous AoI evolution at local sensors and the monitoring center, rendering conventional scheduling policies that rely solely on the monitoring center's AoI inefficient. To this end, we propose a dual-AoI model that captures asynchronous AoI dynamics and formulate the problem as minimizing a long-term time-average AoI function. We develop a scheduling policy based on Markov decision process (MDP) to solve the problem, and analyze the existence and monotonicity of a deterministic stationary optimal policy. Moreover, we derive a low-complexity scheduling policy which exhibits a channel-state-dependent threshold structure. In addition, we establish a necessary and sufficient condition for the stability of the AoI objective. Simulation results demonstrate that the proposed policy outperforms existing approaches.

A Dual-AoI-based Approach for Optimal Transmission Scheduling in Wireless Monitoring Systems with Random Data Arrivals

TL;DR

A dual-AoI model that captures asynchronous AoI dynamics and formulate the problem as minimizing a long-term time-average AoI function is proposed, and a scheduling policy based on Markov decision process (MDP) is developed which outperforms existing approaches.

Abstract

In Internet of Things (IoTs), the freshness of system status information is crucial for real-time monitoring and decision-making. This paper studies the transmission scheduling problem in wireless monitoring systems, where information freshness -- typically quantified by the Age of Information (AoI) -- is heavily constrained by limited channel resources and influenced by factors such as the randomness of data arrivals and unreliable wireless channel. Such randomness leads to asynchronous AoI evolution at local sensors and the monitoring center, rendering conventional scheduling policies that rely solely on the monitoring center's AoI inefficient. To this end, we propose a dual-AoI model that captures asynchronous AoI dynamics and formulate the problem as minimizing a long-term time-average AoI function. We develop a scheduling policy based on Markov decision process (MDP) to solve the problem, and analyze the existence and monotonicity of a deterministic stationary optimal policy. Moreover, we derive a low-complexity scheduling policy which exhibits a channel-state-dependent threshold structure. In addition, we establish a necessary and sufficient condition for the stability of the AoI objective. Simulation results demonstrate that the proposed policy outperforms existing approaches.
Paper Structure (21 sections, 7 theorems, 49 equations, 9 figures, 1 table, 1 algorithm)

This paper contains 21 sections, 7 theorems, 49 equations, 9 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. System Model and Problem Formulation
  4. Memory-based Channel Model
  5. AoI Function Regarding Monitoring Performance
  6. Problem Formulation
  7. The Proposed Scheduling Policy
  8. MDP Formulation
  9. Existence and Property of Deterministic Stationary Optimal Policy
  10. Structure-Informed Scheduling Policy
  11. Stability Analysis of the Monitoring Performance
  12. Simulation Study
  13. Demonstration of State Evolution and Scheduling Structure
  14. Performance Comparison with Different Policies
  15. Conclusion
  16. ...and 6 more sections

Key Result

Theorem 1

If Problem prob2 is feasible, i.e., time-average cost 23 is bounded, there exist a constant $\phi^*$ and a function $Q(\cdot)$ such that the deterministic stationary optimal policy $\pi^*$ can be obtained by solving Bellman equation eq26.

Figures (9)

  • Figure 1: The structure of wireless monitoring.
  • Figure 2: Illustration of AoLI and AoRI.
  • Figure 3: The evolution of AoI function, AoLI and AoRI for two sensors.
  • Figure 4: Scheduling structure of SISP in the two sensor system under different cases.
  • Figure 5: Comparison of the time-average cost for myopic model and dual-stage AoI model.
  • ...and 4 more figures

Theorems & Definitions (18)

  • Example 1
  • Example 2
  • Theorem 1
  • proof
  • Remark 1
  • Lemma 1
  • proof
  • Theorem 2
  • proof
  • Remark 2
  • ...and 8 more