Enhancing Information Freshness in Heterogeneous Random Access Networks with Correlated Status Updates

2025-03-05

AI Summary

In heterogeneous random access networks, sensor association degrades information freshness, leading to increased Age of Information (AoI). Method: This paper models and optimizes the average AoI across all nodes. We first establish a dynamic AoI evolution model under sensor association, uncovering its intrinsic coupling with transmission probability and state correlation. We prove that the optimal transmission policy exhibits a threshold structure and propose the Multi-Start Projected Adam (MSP-Adam) algorithm for rapid convergence to the global average AoI minimum. Contribution/Results: Theoretically, our approach achieves the AoI lower bound of 1/N in homogeneous networks and significantly mitigates performance degradation caused by high-density contention in heterogeneous settings. Numerical experiments demonstrate substantial reductions in average AoI and improved update efficiency under correlation-aware scheduling.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

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
• Security and Privacy: Large-scale security measurements

Abstract

This article focuses on the characterization and optimization of the Age of Information (AoI) in heterogeneous random access networks with correlated sensors, where each sensor has a unique transmission probability and correlation with other sensors. Specifically, we propose an analytical model to analyze the AoI dynamics and further derive the long-term average AoI for each sensor. Furthermore, under the assumption that only one sensor is time-sensitive, we derive the optimal transmission probability for the single sensor in both homogeneous and heterogeneous cases. The optimal transmission probability in homogeneous networks is equal to the inverse of the number of nodes and the optimal AoI, compared with the uncorrelated networks, is significantly improved. Additionally, the optimal transmission probability in the heterogeneous network is a threshold-type indicator function, where the specific threshold is determined by the correlation structure and the transmission probability of the rest time-insensitive sensors. Moreover, when all sensors are time-sensitive, we propose an iterative algorithm based on the Multi-Start Projected Adaptive Moment Estimation (MSP-Adam) method to optimize the network average AoI. The algorithm effectively and rapidly converges to the minimum network average AoI while providing the optimal transmission probability vector. The numerical results of the network AoI optimization under the MSP-Adam algorithm reveal that there exists a harmonious transmission strategy that can mitigate performance degradation caused by severe contention in high-density distributed access networks.

Problem

• Optimize Age of Information in heterogeneous random access networks.
• Derive optimal transmission probabilities for time-sensitive sensors.
• Propose MSP-Adam algorithm to minimize network average AoI.

Innovation

• Analytical model for AoI dynamics analysis
• Optimal transmission probability derivation for sensors
• MSP-Adam algorithm for network AoI optimization