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Sobre

Sobre

Helder Fontes obteve os graus de Mestrado em 2010 e Doutoramento em 2019, ambos em Engenharia Informática na Faculdade de Engenharia da Universidade do Porto, Portugal. Ele é o coordenador da área de Redes Sem Fios no INESC TEC, e desde 2009 participou em vários projetos de investigação nacionais e Europeus, incluindo o SITMe, HiperWireless, FP7 SUNNY, H2020 ResponDrone, DECARBONIZE, FLY.PT e ainda projetos Open Call do FED4FIRE+ como o SIMBED, SIMBED+ e SMART. Ele supervisionou mais de 10 teses de Mestrado em simulação, emulação e experimentação de redes sem fios. Os seus interesses de investigação incluem a simulação, emulação e experimentação de redes sem fios no contexto de cenários emergentes tais como o aéreo e o marítimo, com especial foco na repetibilidade e reproducibilidade de experiências usando digital twins de testbeds sem fios.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Hélder Martins Fontes
  • Cargo

    Responsável de Área
  • Desde

    15 setembro 2009
016
Publicações

2024

Towards truly sustainable IoT systems: the SUPERIOT project

Autores
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;

Publicação
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

Autores
Trancoso, R; Pinto, J; Queirós, R; Fontes, H; Campos, R;

Publicação
Simulation Tools and Techniques - 15th EAI International Conference, SIMUtools 2023, Seville, Spain, December 14-15, 2023, Proceedings

Abstract
Several research works have applied Reinforcement Learning (RL) algorithms to solve the Rate Adaptation (RA) problem in Wi-Fi networks. The dynamic nature of the radio link requires the algorithms to be responsive to changes in link quality. Delays in the execution of the algorithm due to implementional details may be detrimental to its performance, which in turn may decrease network performance. These delays can be avoided to a certain extent. However, this aspect has been overlooked in the state of the art when using simulated environments, since the computational delays are not considered. In this paper, we present an analysis of computational delays and their impact on the performance of RL-based RA algorithms, and propose a methodology to incorporate the experimental computational delays of the algorithms from running in a specific target hardware, in a simulation environment. Our simulation results considering the real computational delays showed that these delays do, in fact, degrade the algorithm’s execution and training capabilities which, in the end, has a negative impact on network performance. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

2023

UAV-Assisted Wireless Communications: An Experimental Analysis of A2G and G2A Channels

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

Publicação
Simulation Tools and Techniques - 15th EAI International Conference, SIMUtools 2023, Seville, Spain, December 14-15, 2023, Proceedings

Abstract
Unmanned Aerial Vehicles (UAVs) offer promising potential as communications node carriers, providing on-demand wireless connectivity to users. While existing literature presents various wireless channel models, it often overlooks the impact of UAV heading. This paper provides an experimental characterization of the Air-to-Ground (A2G) and Ground-to-Air (G2A) wireless channels in an open environment with no obstacles nor interference, considering the distance and the UAV heading. We analyze the received signal strength indicator and the TCP throughput between a ground user and a UAV, covering distances between 50 m and 500 m, and considering different UAV headings. Additionally, we characterize the antenna’s radiation pattern based on UAV headings. The paper provides valuable perspectives on the capabilities of UAVs in offering on-demand and dynamic wireless connectivity, as well as highlights the significance of considering UAV heading and antenna configurations in real-world scenarios.

2023

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

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

Publicação
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

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

Publicação
Simulation Tools and Techniques - 15th EAI International Conference, SIMUtools 2023, Seville, Spain, December 14-15, 2023, Proceedings

Abstract
With the growing connectivity demands, Unmanned Aerial Vehicles (UAVs) have emerged as a prominent component in the deployment of Next Generation On-demand Wireless Networks. However, current UAV positioning solutions typically neglect the impact of Rate Adaptation (RA) algorithms or simplify its effect by considering ideal and non-implementable RA algorithms. This work proposes the Rate Adaptation aware RL-based Flying Gateway Positioning (RARL) algorithm, a positioning method for Flying Gateways that applies Deep Q-Learning, accounting for the dynamic data rate imposed by the underlying RA algorithm. The RARL algorithm aims to maximize the throughput of the flying wireless links serving one or more Flying Access Points, which in turn serve ground terminals. The performance evaluation of the RARL algorithm demonstrates that it is capable of taking into account the effect of the underlying RA algorithm and achieve the maximum throughput in all analysed static and mobile scenarios. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Teses
supervisionadas

2023

Digital Twin of Reconfigurable Printed IoT Nodes using ns-3

Autor
Tiago da Silva Ribeiro

Instituição

2023

Robotic System for Reproducible UAV-like Networking Experimentation in Anechoic Chambers

Autor
Miguel Augusto Marombal Araújo

Instituição

2023

Smart Aerial Networks using Machine Learning for Cross-Layer Optimization

Autor
Rúben Miguel Rei Queirós

Instituição

2023

Reinforcement Learning-Based Positioning Algorithm for Relay Nodes in Aerial Networks

Autor
Gabriella Fernandes Pantaleão

Instituição

2023

A Machine Learning-aided Wireless Network Digital Twin using Multimodal Input Data

Autor
Ricardo Jorge Espirito Santo Trancoso

Instituição