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

TL;DR

This paper introduces a dual-AoI model and an MDP-based scheduling policy to optimize information freshness in wireless IoT systems with random data arrivals and unreliable channels.

Contribution

It proposes a novel dual-AoI model and develops a low-complexity, channel-state-dependent scheduling policy with proven optimality properties.

Findings

The proposed policy outperforms existing methods in simulations.

A necessary and sufficient condition for AoI stability is established.

The policy exhibits a threshold structure based on channel states.

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.