Optimizing Age of Information in Random Access Networks: A Second-Order Approach for Active/Passive Users

TL;DR

This work tackles minimizing AoI moments in a distributed random-access network with active sensors and passive observers. It introduces a second-order modeling framework that expresses AoI moments as functions of the delivery mean and temporal variance, enabling closed-form approximations via a two-state Markov model and a novel Wait-and-Go (WaG) model. The authors derive explicit mean/variance formulas, prove how optimal parameters can be found by simple line searches, and validate the approach through simulations showing substantial gains over baselines and near-optimality of the proposed WaG strategy. The results offer scalable, distributed methods to maintain fresh information in interference-laden networks, with potential impact on IoT, surveillance, and sensing applications.

Abstract

In this paper, we study the moments of the Age of Information (AoI) for both active and passive users in a random access network. In this network, active users broadcast sensing data, while passive users detect in-band radio activities from out-of-network devices, such as jammers. Collisions occur when multiple active users transmit simultaneously. Passive users can detect radio activities only when no active user transmits. Each active user's transmission behavior follows a Markov process. We aim to minimize the weighted sum of any moments of AoI for both user types. To achieve this, we employ a second-order analysis of system behavior. Specifically, we characterize an active user's transmission Markov process using its mean and temporal variance. We show that any moment of the AoI can be approximated by a function of these two parameters. This insight enables us to analyze and optimize the transmission Markov process for active users. We apply this strategy to two different random access models. Simulation results show that policies derived from this strategy outperform other baseline policies.

Figures (12)

• Figure 1: An example of active users (drones) and passive users (detectors).
• Figure 2: The two-state model.
• Figure 3: Two-state model approximation mismatches when $C=1$ and $N=1$
• Figure 4: Two-state model approximation mismatches when $C=2$ and $N=4$
• Figure 5: Optimality Validation

Theorems & Definitions (9)

• Theorem 1
• Theorem 2
• Theorem 3
• Lemma 1
• Lemma 2
• Lemma 3
• Lemma 4
• Lemma 5
• Theorem 4