University of Oulu

E. Eldeeb et al., "A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks," 2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 2022, pp. 172-177, doi: 10.1109/EuCNC/6GSummit54941.2022.9815722.

A learning-based trajectory planning of multiple UAVs for AoI minimization in IoT networks

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Author: Eldeeb, Eslam1; Pérez, Dian Echevarría1; de Souza Sant’Ana, Jean Michel1;
Organizations: 1Centre for Wireless Communications (CWC), University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022082556319
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-08-25
Description:

Abstract

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

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Series: European Conference on Networks and Communications
ISSN: 2475-6490
ISSN-E: 2575-4912
ISSN-L: 2475-6490
ISBN: 978-1-6654-9871-5
ISBN Print: 978-1-6654-9872-2
DOI: 10.1109/eucnc/6gsummit54941.2022.9815722
OADOI: https://oadoi.org/10.1109/eucnc/6gsummit54941.2022.9815722
Host publication: 2022 Joint European conference on networks and communications & 6G summit (EuCNC/6G Summit)
Conference: Joint European Conference on Networks and Communications & 6G Summit
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work is partially supported by Academy of Finland, 6G Flagship program (Grant no. 346208) and FIREMAN (Grant no. 326301), and the European Commission through the Horizon Europe project Hexa-X (Grant Agreement no.101015956).
EU Grant Number: (EU) Hexa-X - 6G vision and intelligent fabric of technology enablers connecting human, physical, and digital worlds
Academy of Finland Grant Number: 346208
326301
Detailed Information: 346208 (Academy of Finland Funding decision)
326301 (Academy of Finland Funding decision)
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