# Ensemble DNN for Age-of-Information Minimization in UAV-assisted Networks

arXiv: https://arxiv.org/abs/2309.02913 · HTML: https://mlsanity.com/paper/2309.02913/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks](https://mlsanity.com/paper/1910.14648/) (2019)
- [UAV Assisted Data Collection for Internet of Things: A Survey](https://mlsanity.com/paper/2211.09555/) (2022)
- [Average Peak Age-of-Information Minimization in UAV-assisted IoT Networks](https://mlsanity.com/paper/1804.06543/) (2018)
- [Age-of-Updates Optimization for UAV-assisted Networks](https://mlsanity.com/paper/2209.05370/) (2022)
- [Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks](https://mlsanity.com/paper/2303.08680/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning](https://mlsanity.com/paper/2512.21412/) (2025)

## Abstract (arXiv)

This paper addresses the problem of Age-of-Information (AoI) in UAV-assisted networks. Our objective is to minimize the expected AoI across devices by optimizing UAVs' stopping locations and device selection probabilities. To tackle this problem, we first derive a closed-form expression of the expected AoI that involves the probabilities of selection of devices. Then, we formulate the problem as a non-convex minimization subject to quality of service constraints. Since the problem is challenging to solve, we propose an Ensemble Deep Neural Network (EDNN) based approach which takes advantage of the dual formulation of the studied problem. Specifically, the Deep Neural Networks (DNNs) in the ensemble are trained in an unsupervised manner using the Lagrangian function of the studied problem. Our experiments show that the proposed EDNN method outperforms traditional DNNs in reducing the expected AoI, achieving a remarkable reduction of 29.5%.