Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Age of Information Optimization with Preemption Strategies for Correlated Systems

Egemen Erbayat, Ali Maatouk, Peng Zou, Suresh Subramaniam

TL;DR

This work tackles AoI minimization in a multi-sensor, multi-process network where updates across processes are correlated. It introduces a probabilistic preemption mechanism and analyzes AoI via a stochastic hybrid system, yielding closed-form per-process AoI expressions and formulating the total AoI as a sum of linear-ratio objectives. The optimization is NP-hard, but the authors reformulate it as an equivalent problem and derive an outer-bound on branch-and-bound iterations to guarantee a global optimum with a finite number of steps. Key finding: the correlation pattern among processes largely dictates the optimal preemption strategy, with information diversity sometimes more valuable than sheer update count. Numerical results with two sensors and two processes confirm the theory and illustrate how correlation shapes preemption priorities and AoI outcomes, demonstrating scalable and effective optimization in correlated settings.

Abstract

In this paper, we examine a multi-sensor system where each sensor monitors multiple dynamic information processes and transmits updates over a shared communication channel. These updates may include correlated information across the various processes. In this type of system, we analyze the impact of preemption, where ongoing transmissions are replaced by newer updates, on minimizing the Age of Information (AoI). While preemption is optimal in some scenarios, its effectiveness in multi-sensor correlated systems remains an open question. To address this, we introduce a probabilistic preemption policy, where the source sensor preemption decision is stochastic. We derive closed-form expressions for the AoI and frame its optimization as a sum of linear ratios problem, a well-known NP-hard problem. To navigate this complexity, we establish an upper bound on the iterations using a branch-and-bound algorithm by leveraging a reformulation of the problem. This analysis reveals linear scalability with the number of processes and a logarithmic dependency on the reciprocal of the error that shows the optimal solution can be efficiently found. Building on these findings, we show how different correlation matrices can lead to distinct optimal preemption strategies. Interestingly, we demonstrate that the diversity of processes within the sensors' packets, as captured by the correlation matrix, plays a more significant role in preemption priority than the number of updates.

Age of Information Optimization with Preemption Strategies for Correlated Systems

TL;DR

This work tackles AoI minimization in a multi-sensor, multi-process network where updates across processes are correlated. It introduces a probabilistic preemption mechanism and analyzes AoI via a stochastic hybrid system, yielding closed-form per-process AoI expressions and formulating the total AoI as a sum of linear-ratio objectives. The optimization is NP-hard, but the authors reformulate it as an equivalent problem and derive an outer-bound on branch-and-bound iterations to guarantee a global optimum with a finite number of steps. Key finding: the correlation pattern among processes largely dictates the optimal preemption strategy, with information diversity sometimes more valuable than sheer update count. Numerical results with two sensors and two processes confirm the theory and illustrate how correlation shapes preemption priorities and AoI outcomes, demonstrating scalable and effective optimization in correlated settings.

Abstract

In this paper, we examine a multi-sensor system where each sensor monitors multiple dynamic information processes and transmits updates over a shared communication channel. These updates may include correlated information across the various processes. In this type of system, we analyze the impact of preemption, where ongoing transmissions are replaced by newer updates, on minimizing the Age of Information (AoI). While preemption is optimal in some scenarios, its effectiveness in multi-sensor correlated systems remains an open question. To address this, we introduce a probabilistic preemption policy, where the source sensor preemption decision is stochastic. We derive closed-form expressions for the AoI and frame its optimization as a sum of linear ratios problem, a well-known NP-hard problem. To navigate this complexity, we establish an upper bound on the iterations using a branch-and-bound algorithm by leveraging a reformulation of the problem. This analysis reveals linear scalability with the number of processes and a logarithmic dependency on the reciprocal of the error that shows the optimal solution can be efficiently found. Building on these findings, we show how different correlation matrices can lead to distinct optimal preemption strategies. Interestingly, we demonstrate that the diversity of processes within the sensors' packets, as captured by the correlation matrix, plays a more significant role in preemption priority than the number of updates.

Paper Structure

This paper contains 6 sections, 3 theorems, 37 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Age of Information Analysis
  4. Average Age Optimization
  5. Numerical Results
  6. Conclusion

Key Result

Lemma 1

The stationary distribution of the states ($0$, $1$, $2$) can be derived as follows:

Figures (6)

  • Figure 1: Illustration of our system model.
  • Figure 2: Equivalent system model from process $j$'s perspective.
  • Figure 3: Simulation results vs theoretical findings.
  • Figure 4: Optimal preemption probabilities under varying $\lambda_1$ to show the effect of correlation matrix.
  • Figure 5: Optimal preemption probabilities under different conditions.
  • ...and 1 more figures

Theorems & Definitions (6)

  • Lemma 1
  • proof
  • Theorem 1
  • proof
  • Theorem 2
  • proof