A UoI-Optimal Policy for Timely Status Updates with Resource Constraint

Abstract

Timely status updates are critical in remote control systems such as autonomous driving and the industrial Internet of Things, where timeliness requirements are usually context dependent. Accordingly, the Urgency of Information (UoI) has been proposed beyond the well-known Age of Information (AoI) by further including context-aware weights which indicate whether the monitored process is in an emergency. However, the optimal updating and scheduling strategies in terms of UoI remain open. In this paper, we propose a UoI-optimal updating policy for timely status information with resource constraint. We first formulate the problem in a constrained Markov decision process and prove that the UoI-optimal policy has a threshold structure. When the context-aware weights are known, we propose a numerical method based on linear programming. When the weights are unknown, we further design a reinforcement learning (RL)-based scheduling policy. The simulation reveals that the threshold of the UoI-optimal policy increases as the resource constraint tightens. In addition, the UoI-optimal policy outperforms the AoI-optimal policy in terms of average squared estimation error, and the proposed RL-based updating policy achieves a near-optimal performance without the advanced knowledge of the system model.

Optimization Algorithm for AoI-Based UAV-Assisted Data Collection

“…A content resale problem is discussed in [10] which provides a hybrid multicast/unicast/D2D transmission architecture oriented towards age of information and cache assistance to increase the data transmission rate, reduce the burden of large data traffic, and improve system efficiency, where the problem is decomposed into two subproblems and the subproblems are solved through the Stackelberg game and auction framework, respectively. Reinforcement learning is used in the literature [11] to investigate the best update strategy for the age of information (AoI) and the urgency of information (UoI) of real-time status information based on resource constraints. Urgency of information (UoI) further includes context-aware weights indicating whether the monitored process is in an emergency.…”