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

arXiv: https://arxiv.org/abs/2209.09206 · HTML: https://mlsanity.com/paper/2209.09206/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks](https://mlsanity.com/paper/1801.01803/) (2018)
- [Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-assisted Networks](https://mlsanity.com/paper/1905.02993/) (2019)
- [On the Role of Age of Information in the Internet of Things](https://mlsanity.com/paper/1812.08286/) (2018)
- [Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks](https://mlsanity.com/paper/2003.00391/) (2020)
- [AoI Minimization in Status Update Control with Energy Harvesting Sensors](https://mlsanity.com/paper/2009.04224/) (2020)

## Built on by (most cited first, 3 of 3)

- [Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.08421/) (2024)
- [Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach](https://mlsanity.com/paper/2309.14757/) (2023)
- [Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models](https://mlsanity.com/paper/2401.13827/) (2024)

## Abstract (arXiv)

Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion. textitAge 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.