# Age-of-Updates Optimization for UAV-assisted Networks

arXiv: https://arxiv.org/abs/2209.05370 · HTML: https://mlsanity.com/paper/2209.05370/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2022.

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

- [FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks](https://mlsanity.com/paper/2405.11079/) (2024)
- [Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks](https://mlsanity.com/paper/2303.08680/) (2023)
- [Ensemble DNN for Age-of-Information Minimization in UAV-assisted Networks](https://mlsanity.com/paper/2309.02913/) (2023)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) have been proposed as a promising technology to collect data from IoT devices and relay it to the network. In this work, we are interested in scenarios where the data is updated periodically, and the collected updates are time-sensitive. In particular, the data updates may lose their value if they are not collected and analyzed timely. To maximize the data freshness, we optimize a new performance metric, namely the Age-of-Updates (AoU). Our objective is to carefully schedule the UAVs hovering positions and the users' association so that the AoU is minimized. Unlike existing works where the association parameters are considered as binary variables, we assume that devices send their updates according to a probability distribution. As a consequence, instead of optimizing a deterministic objective function, the objective function is replaced by an expectation over the probability distribution. The expected AoU is therefore optimized under quality of service and energy constraints. The original problem being non-convex, we propose an equivalent convex optimization that we solve using an interior-point method. Our simulation results show the performance of the proposed approach against a binary association.