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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

  • Name

    Hélder Martins Fontes
  • Role

    Area Manager
  • Since

    15th September 2009
013
Publications

2024

Towards truly sustainable IoT systems: the SUPERIOT project

Authors
Katz, M; Paso, T; Mikhaylov, K; Pessoa, L; Fontes, H; Hakola, L; Leppaeniemi, J; Carlos, E; Dolmans, G; Rufo, J; Drzewiecki, M; Sallouha, H; Napier, B; Branquinho, A; Eder, K;

Publication
JOURNAL OF PHYSICS-PHOTONICS

Abstract
This paper provides an overview of the SUPERIOT project, an EU SNS JU (Smart Networks and Services Joint Undertaking) initiative focused on developing truly sustainable IoT systems. The SUPERIOT concept is based on a unique holistic approach to sustainability, proactively developing sustainable solutions considering the design, implementation, usage and disposal/reuse stages. The concept exploits radio and optical technologies to provide dual-mode wireless connectivity and dual-mode energy harvesting as well as dual-mode IoT node positioning. The implementation of the IoT nodes or devices will maximize the use of sustainable printed electronics technologies, including printed components, conductive inks and substrates. The paper describes the SUPERIOT concept, covering the key technical approaches to be used, promising scenarios and applications, project goals and demonstrators which will be developed to the proof-of-concept stage. In addition, the paper briefly discusses some important visions on how this technology may be further developed in the future.

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.

2023

Rate Adaptation Aware Positioning for Flying Gateways using Reinforcement Learning

Authors
Pantaleão, G; Queirós, R; Fontes, H; Campos, R;

Publication
CoRR

Abstract

Supervised
thesis

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

2020

An SDN-based Predictive Orchestrator for Transparent Multi-interface Vehicular Internet Access

Author
Agostinho Filipe de Almeida Coimbra Maia

Institution
UP-FEUP

2020

Acordo Distribuído Aproximado

Author
Joaquim Manuel Gonçalves Oliveira

Institution
UM

2020

Trace-based ns3-gym Reinforcement Learning Environment Framework for Wireless Networks

Author
Gonçalo Regueiras dos Santos

Institution
UP-FEUP

2018

A Decision Support System for Investments in Public Transport Infrastructure

Author
Marcos Paulo Schlickmann

Institution
UP-FEUP