Deep Reinforcement Learning-Based Solution for Minimizing the Alterable Urgency of Information in UAV- Enabled IIoT System

Timely delivery of fresh data/information is fundamental and critical in the Industrial Internet of Things (1IoT) powered various applications, which face the challenges of how to meet the dynamically changing requirement of heterogeneous information urgency among different moving devices under time-varying channels. In this paper, we investigate a UAV-enabled mobile edge computing IloT system. To combat the above challenge, we firstly design a new metric, namely alterable Urgency of Information (aUol) to quantify the changeable heterogeneous urgency of information across various devices since the conventional Age of Information (Aol) is incapable. Further, we exploit a deep reinforcement learning algorithm to optimize the aU 01 of the system by adaptively determining the optimal user scheduling and UAV trajectory planning strategy. Extensive simulation results demonstrate that the proposed method can effectively reduce the aUol of system and improve heterogeneous information urgency service satisfaction rate by 42.5 % to 87.5 % as compared to other benchmark approaches. Additionally, the proposed approach is more effective than the conventional Aol-oriented method.