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

2026-03-06

AI Summary

This work addresses the challenge of information freshness in wireless monitoring systems, where stochastic data arrivals and unreliable channels cause asynchronous evolution of Age of Information (AoI) at the local sensor and the central monitor. Recognizing that conventional scheduling strategies relying solely on the central AoI are suboptimal, the paper proposes a dual-AoI model to capture this asynchrony and formulates the scheduling problem as one of minimizing the long-term average AoI. By modeling the system as a Markov decision process (MDP), an optimal transmission policy is derived. The study establishes, for the first time, the monotonicity and threshold structure of the optimal policy under the dual-AoI framework and provides necessary and sufficient conditions for AoI stability. Simulation results demonstrate that the proposed low-complexity policy significantly outperforms existing approaches in enhancing overall information freshness.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• Age of Information
• transmission scheduling
• wireless monitoring systems
• random data arrivals
• information freshness

Innovation

• Dual-AoI
• Markov decision process
• Age of Information
• threshold-based scheduling
• asynchronous AoI dynamics