A Dual-AoI-based Approach for Optimal Transmission Scheduling in Wireless Monitoring Systems with Random Data Arrivals
This work provides an incremental improvement in information freshness for real-time wireless monitoring systems, which is crucial for decision-making in IoT applications.
This paper addresses the problem of optimizing information freshness in wireless monitoring systems by minimizing the long-term time-average Age of Information (AoI), considering random data arrivals and unreliable channels. The proposed dual-AoI model and MDP-based scheduling policy, along with a low-complexity threshold policy, are shown to outperform existing approaches in simulations.
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.