ToI-Based Data Utility Maximization for UAV-Assisted Wireless Sensor Networks

Using Unmanned Aerial Vehicles as mobile base stations is a promising way to collect data from sensor nodes, especially for large-scale wireless sensor networks. Previous works mainly focus on improving the freshness of the collected data or the energy efficiency by scheduling UAVs. Considering the fact that the sensing data in some applications is time-sensitive, that is, the value of the sensing data is based on its Timeliness of Information (ToI), which decays over time. Therefore, in this paper, we investigate the UAV Trajectory optimization problem for Maximizing the ToI-based data utility (TMT). We propose an improved deep reinforcement learning-based algorithm to address the problem, and the experience results demonstrate the effectiveness of our designs.