# AoI-minimizing Scheduling in UAV-relayed IoT Networks

arXiv: https://arxiv.org/abs/2107.05181 · HTML: https://mlsanity.com/paper/2107.05181/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning](https://mlsanity.com/paper/1809.02121/) (2018)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Hyp-RL : Hyperparameter Optimization by Reinforcement Learning](https://mlsanity.com/paper/1906.11527/) (2019)
- [Cooperative Internet of UAVs: Distributed Trajectory Design by Multi-agent Deep Reinforcement Learning](https://mlsanity.com/paper/2007.14297/) (2020)
- [Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-assisted Networks](https://mlsanity.com/paper/1905.02993/) (2019)
- [On The Age Of Information In Status Update Systems With Packet Management](https://mlsanity.com/paper/1506.08637/) (2015)
- [Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks](https://mlsanity.com/paper/2003.00391/) (2020)
- [Timely Updates in Energy Harvesting Two-hop Networks: Offline and Online Policies](https://mlsanity.com/paper/1812.01005/) (2018)
- [Unified Analysis of HetNets using Poisson Cluster Process under Max-Power Association](https://mlsanity.com/paper/1812.01830/) (2018)
- [Joint Age of Information and Self Risk Assessment for Safer 802.11p based V2V Networks](https://mlsanity.com/paper/2012.04774/) (2020)
- [3D Placement and Orientation of mmWave-based UAVs for Guaranteed LoS Coverage](https://mlsanity.com/paper/2104.12993/) (2021)
- [Age-Optimal UAV Scheduling for Data Collectionwith Battery Recharging](https://mlsanity.com/paper/2005.00252/) (2020)
- [Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors](https://mlsanity.com/paper/2005.05936/) (2020)

## Built on by (most cited first, 2 of 2)

- [Securing the Skies: An IRS-Assisted AoI-Aware Secure Multi-UAV System with Efficient Task Offloading](https://mlsanity.com/paper/2404.04692/) (2024)
- [A Practical AoI Scheduler in IoT Networks with Relays](https://mlsanity.com/paper/2203.04227/) (2022)

## Abstract (arXiv)

Due to flexibility, autonomy and low operational cost, unmanned aerial vehicles (UAVs), as fixed aerial base stations, are increasingly being used as textitrelays to collect time-sensitive information (i.e., status updates) from IoT devices and deliver it to the nearby terrestrial base station (TBS), where the information gets processed. In order to ensure timely delivery of information to the TBS (from all IoT devices), optimal scheduling of time-sensitive information over two hop UAV-relayed IoT networks (i.e., IoT device to the UAV [hop 1], and UAV to the TBS [hop 2]) becomes a critical challenge. To address this, we propose scheduling policies for Age of Information (AoI) minimization in such two-hop UAV-relayed IoT networks. To this end, we present a low-complexity MAF-MAD scheduler, that employs Maximum AoI First (MAF) policy for sampling of IoT devices at UAV (hop 1) and Maximum AoI Difference (MAD) policy for updating sampled packets from UAV to the TBS (hop 2). We show that MAF-MAD is the optimal scheduler under ideal conditions, i.e., error-free channels and generate-at-will traffic generation at IoT devices. On the contrary, for realistic conditions, we propose a Deep-Q-Networks (DQN) based scheduler. Our simulation results show that DQN-based scheduler outperforms MAF-MAD scheduler and three other baseline schedulers, i.e., Maximal AoI First (MAF), Round Robin (RR) and Random, employed at both hops under general conditions when the network is small (with 10's of IoT devices). However, it does not scale well with network size whereas MAF-MAD outperforms all other schedulers under all considered scenarios for larger networks.