A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks

TL;DR

This paper introduces a deep reinforcement learning approach for planning UAV trajectories to minimize the Age of Information in IoT networks, significantly improving freshness and energy efficiency.

Contribution

It presents a novel DRL-based trajectory planning method for UAVs that effectively minimizes AoI in IoT sensor networks, outperforming baseline schemes.

Findings

Reduces average AoI by approximately 25%.

Requires up to 50% less energy than baseline schemes.

Converges quickly and outperforms random walk schemes.

Abstract

Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion. \textit{Age of Information} (AoI) is a metric that quantifies information timeliness, i.e., the freshness of the received information or status update. This work considers a setup of deployed sensors in an IoT network, where multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages. This ensures that the received information at the base station is as fresh as possible. The complex optimization problem is efficiently solved using a deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower AoI than the random walk scheme. Our proposed algorithm reduces the average age by approximately $25\%$ and requires down to $50\%$ less energy when compared to the baseline scheme.