Age of Information Optimization with Preemption Strategies for Correlated Systems

2025-02-11

AI Summary

We address the Age of Information (AoI) minimization problem in multi-sensor monitoring systems where sensors share a common communication channel to track multiple dynamic processes. Specifically, we investigate the hitherto unexplored impact of inter-process correlation on the efficacy of preemption policies. We propose a randomized preemption strategy guided by a sensor-state correlation matrix and establish— for the first time—that inter-process correlation, rather than update frequency, fundamentally governs preemption priority. We derive a closed-form AoI expression, formulate the optimization as a linear-fractional program, and design a branch-and-bound algorithm with linear-logarithmic time complexity, providing a theoretical upper bound on its iteration count. Experiments demonstrate that exploiting correlation structure yields decisive improvements in preemption decisions, significantly outperforming conventional baselines—including rate-based and random preemption schemes—in AoI reduction.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Heuristic Search

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

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.

Problem

• Optimize Age of Information
• Multi-sensor correlated systems
• Probabilistic preemption policy effectiveness

Innovation

• Probabilistic preemption policy
• Branch-and-bound algorithm
• Linear scalability analysis