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About

About

Helder Fontes received the MSc degree in 2010 and Ph.D. degree in 2019, both in Informatics Engineering at the Faculty of Engineering of the University of Porto, Portugal. He is the coordinator of the Wireless Networks (WiN) area at INESC TEC and since 2009 he has participated in multiple national and EU research projects, including SITMe, HiperWireless, FP7 SUNNY, H2020 ResponDrone, DECARBONIZE, FLY.PT and Fed4FIRE+ SIMBED, SIMBED+ and SMART open call projects. He has been advisor of 10+ MSc theses on wireless networking simulation, emulation, and experimentation. His research interests include wireless networking simulation, emulation, and experimentation in the scope of emerging scenarios such as airborne and maritime, with special focus on repeatability and reproducibility of experiments using digital twins of wireless testbeds.

Interest
Topics
Details

Details

009
Publications

2023

On the Analysis of Computational Delays in Reinforcement Learning-based Rate Adaptation Algorithms

Authors
Trancoso, R; Queirós, R; Fontes, H; Campos, R;

Publication
CoRR

Abstract

2023

UAV-Assisted Wireless Communications: An Experimental Analysis of Air-to-Ground and Ground-to-Air Channels in Open Environments

Authors
Shafafi, K; Almeida, EN; Coelho, A; Fontes, H; Ricardo, M; Campos, R;

Publication
CoRR

Abstract

2023

Position-Based Machine Learning Propagation Loss Model Enabling Fast Digital Twins of Wireless Networks in ns-3

Authors
Almeida, EN; Fontes, H; Campos, R; Ricardo, M;

Publication
PROCEEDINGS OF THE 2023 WORKSHOP ON NS-3, WNS3 2023

Abstract
Digital twins have been emerging as a hybrid approach that combines the benefits of simulators with the realism of experimental testbeds. The accurate and repeatable set-ups replicating the dynamic conditions of physical environments, enable digital twins of wireless networks to be used to evaluate the performance of next-generation networks. In this paper, we propose the Position-based Machine Learning Propagation Loss Model (P-MLPL), enabling the creation of fast and more precise digital twins of wireless networks in ns-3. Based on network traces collected in an experimental testbed, the P-MLPL model estimates the propagation loss suffered by packets exchanged between a transmitter and a receiver, considering the absolute node's positions and the traffic direction. The P-MLPL model is validated with a test suite. The results show that the P-MLPL model can predict the propagation loss with a median error of 2.5 dB, which corresponds to 0.5x the error of existing models in ns-3. Moreover, ns-3 simulations with the P-MLPL model estimated the throughput with an error up to 2.5 Mbit/s, when compared to the real values measured in the testbed.

2022

Machine Learning Based Propagation Loss Module for Enabling Digital Twins of Wireless Networks in ns-3

Authors
Almeida, EN; Rushad, M; Kota, SR; Nambiar, A; Harti, HL; Gupta, C; Waseem, D; Santos, G; Fontes, H; Campos, R; Tahiliani, MP;

Publication
PROCEEDING OF THE 2022 WORKSHOP ON NS-3, WNS3 2022

Abstract
The creation of digital twins of experimental testbeds allows the validation of novel wireless networking solutions and the evaluation of their performance in realistic conditions, without the cost, complexity and limited availability of experimental testbeds. Current trace-based simulation approaches for ns-3 enable the repetition and reproduction of the same exact conditions observed in past experiments. However, they are limited by the fact that the simulation setup must exactly match the original experimental setup, including the network topology, the mobility patterns and the number of network nodes. In this paper, we propose the Machine Learning based Propagation Loss (MLPL) module for ns-3. Based on network traces collected in an experimental testbed, the MLPL module estimates the propagation loss as the sum of a deterministic path loss and a stochastic fast-fading loss. The MLPL module is validated with unit tests. Moreover, we test the MLPL module with real network traces, and compare the results obtained with existing propagation loss models in ns-3 and real experimental results. The results obtained show that the MLPL module can accurately predict the propagation loss observed in a real environment and reproduce the experimental conditions of a given testbed, enabling the creation of digital twins of wireless network environments in ns-3.

2022

ResponDrone - A Situation Awareness Platform for First Responders

Authors
Friedrich, M; Lieb, TJ; Temme, A; Almeida, EN; Coelho, A; Fontes, H;

Publication
AIAA/IEEE Digital Avionics Systems Conference - Proceedings

Abstract
Short reaction times are among the most important factors in preventing casualties or providing first assistance to potential victims during large scale natural disasters. Consequently, first response teams must quickly gain a comprehensive overview and thus situation awareness of the disaster situation. To address this challenge, the ResponDrone-platform was developed within the scope of the ResponDrone project. A fleet of unmanned aerial vehicles provides critical information from the disaster site to the first response teams in real-time and can act as a communications relays in areas with disrupted communications infrastructure. The unmanned aerial vehicles are commanded via a web-based multi-mission control system. Data sharing between the individual components is realized via a web-based cloud platform. The ResponDrone platform's capabilities were successfully tested and validated within the scope of several flight and simulation trials. This paper describes the components that were developed, integrated into a system-of-systems and demonstrated during the ResponDrone project and explains how the components work together in order to execute task-based multi-UAV missions. Further, the results of the validation trials are presented and an outlook on the next steps for further exploitation of the ResponDrone platform is given. © 2022 IEEE.

Supervised
thesis

2022

Rate Adaptation Algorithm using Reinforcement Learning for Delay Minimisation in a Wi-Fi Link

Author
José Manuel de Sousa Magalhães

Institution
UP-FEUP

2022

Utilização de Reinforcement Learning para otimização de ligações Wi-Fi no contexto de redes voadoras

Author
Gabriella Fernandes Pantaleão

Institution
UP-FEUP

2022

On the Performance Impact of Computational Delays of RL-Based Networking Algorithms through Improved ns-3 Digital Twins

Author
João Paulo Ferreira Pinto

Institution
UP-FEUP

2022

Data-driven Traffic Generation Model for Digital Twins of Wireless Networks

Author
Catarina Mouro de Sousa

Institution
UP-FEUP

2022

Analysis and Optimisation of Computational Delays in Reinforcement Learning-based Wi-Fi Rate Adaptation

Author
Ricardo Jorge Espirito Santo Trancoso

Institution
UP-FEUP