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

TL;DR

This paper proposes an ensemble deep neural network approach to optimize UAV-assisted networks for minimizing Age-of-Information, achieving significant reductions in information freshness across devices.

Contribution

It introduces a novel EDNN-based method that leverages dual formulation and unsupervised training to effectively minimize AoI in UAV networks.

Findings

EDNN outperforms traditional DNNs in AoI reduction

Achieves a 29.5% decrease in expected AoI

Provides a closed-form expression for expected AoI

Abstract

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 $29.5\%$.