Energy and Service-priority aware Trajectory Design for UAV-BSs using Double Q-Learning

TL;DR

This paper proposes an energy-efficient UAV trajectory design using Double Q-Learning that considers service priorities of IoT nodes, improving energy use and service delay over traditional methods.

Contribution

It introduces a novel Double Q-Learning based approach for UAV trajectory optimization that accounts for service priorities, enhancing energy efficiency and service quality.

Findings

Q-Learning based trajectory reduces UAV energy consumption.

It outperforms greedy algorithms in service delay for high-priority nodes.

The method effectively balances energy use and service requirements.

Abstract

Next-generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes. Despite having advantages of using UAV-BSs, their dependence on the on-board, limited-capacity battery hinders their service continuity. Shorter trajectories can save flying energy, however, UAV-BSs must also serve nodes based on their service priority since nodes' service requirements are not always the same. In this paper, we present an energy-efficient trajectory optimization for a UAV assisted IoT system in which the UAV-BS considers the IoT nodes' service priorities in making its movement decisions. We solve the trajectory optimization problem using Double Q-Learning algorithm. Simulation results reveal that the Q-Learning based optimized trajectory outperforms a benchmark algorithm, namely Greedily-served algorithm, in terms of reducing the average energy consumption of the UAV-BS as well as the service delay for high priority nodes.