3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning

TL;DR

This paper proposes a deep reinforcement learning approach to optimize UAV trajectories for data collection in IoT networks, balancing coverage, data throughput, and resource efficiency.

Contribution

It introduces a novel DRL-based method for autonomous UAV trajectory optimization to maximize data collection and network performance in IoT environments.

Findings

Significant increase in total sum-rate after training

Effective autonomous UAV data collection with minimal resources

Balanced trade-off between throughput, trajectory, and time

Abstract

Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. However, due to the limitation of their on-board power and flight time, it is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT). In this paper, we design a new UAV-assisted IoT systems relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. Then, a deep reinforcement learning-based technique is conceived for finding the optimal trajectory and throughput in a specific coverage area. After training, the UAV has the ability to autonomously collect all the data from user nodes at a significant total sum-rate improvement while minimising the associated resources used. Numerical results are provided to highlight how our techniques strike a balance between the throughput attained, trajectory, and the time spent. More explicitly, we characterise the attainable performance in terms of the UAV trajectory, the expected reward and the total sum-rate.