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Sobre

Sobre

Rui Campos tem doutoramento em Engenharia Electrotécnica e de Computadores pela Universidade do Porto desde 2011. Atualmente, é coordenador da área de redes sem fios (http://win.inesctec.pt) no Centro de Telecomunicações e Multimédia composta por 30 investigadores, e é membro sénior do IEEE. Rui Campos tem vindo a coordenadar vários projetos de I&D+i, incluindo: SIMBED, UGREEN, BLUECOM+, MareCom, MTGrid, a ação WiFIX dentro do projeto FP7 CONFINE, Mare-Fi, Under-Fi, ReCoop e HiperWireless. Rui Campos tem igualmente participado em múltiplos projetos de I&D, incluindo os seguintes projetos europeus: H2020 RAWFIE, FP7 SUNNY, FP7 CONFINE, FP6 Ambient Networks Phase 1 e FP6 Ambient Networks Phase 2. Os seus interesses de investigação incluem os aspetos de controlo de acesso ao meio, gestão de recursos rádio, gestão de mobilidade e auto-configuração em redes emergentes, com especial foco nas redes formadas por plataformas voadoras, redes marítimas e redes subaquáticas. 

Tópicos
de interesse
Detalhes

Detalhes

034
Publicações

2022

Joint Energy and Performance Aware Relay Positioning in Flying Networks

Autores
Rodrigues, H; Coelho, A; Ricardo, M; Campos, R;

Publicação
IEEE ACCESS

Abstract
<p>Unmanned Aerial Vehicles (UAVs) have emerged as suitable platforms for transporting and positioning communications nodes on demand, including Wi-Fi Access Points and cellular Base Stations. This paved the way for the deployment of flying networks capable of temporarily providing wireless connectivity and reinforcing the coverage and capacity of existing networks anywhere, anytime. Several solutions have been proposed in the literature for the positioning of UAVs that act as Flying Access Points (FAPs). Yet, the positioning of Flying Communications Relays (FCRs) in charge of forwarding the traffic to/from the Internet has not received equal attention. A major challenge in flying networks is the UAVs endurance. Since the UAVs are typically powered by on-board batteries with limited capacity, whose energy is used for communications and propulsion, the UAVs need to land frequently for recharging or replacing their batteries, limiting the flying network availability. State of the art works are focused on optimizing both the flying network performance and the energy-efficiency from the communications point of view, but do not consider the energy spent for the UAV propulsion. Yet, the energy spent for communications is typically negligible when compared with the energy spent for the UAV propulsion.</p><p>In order to address the FCR UAV positioning and energy-efficiency challenges, we have proposed the Energy-aware RElay Positioning (EREP) algorithm. EREP defines the trajectory and speed of the FCR UAV that minimize the energy spent for the UAV propulsion. However, since EREP considers a theoretical radio propagation model for computing the minimum Signal-to-Noise Radio (SNR) values that allow to meet the FAPs traffic demand, this may lead to network performance degradation in real-world networking scenarios, especially due to the FCR UAV movement. In this article, we propose the Energy and Performance Aware relay Positioning (EPAP) algorithm. Built upon the EREP algorithm, EPAP defines target performance-aware SNR values for the wireless links established between the FCR UAV and the FAPs and, based on that, computes the trajectory to be completed by the FCR UAV, so that the energy spent for the UAV propulsion is minimized. EPAP was evaluated in terms of both the flying network performance and the FCR UAV endurance, considering multiple networking scenarios. Simulation results show gains up to 25% in the FCR UAV endurance, while not compromising the Quality of Service offered by the flying network.</p>

2022

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

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

Publicação
WNS3 2022: 2022 Workshop on ns-3, Virtual Event, USA, June 22 - 23, 2022

Abstract

2022

Traffic-Aware UAV Placement Using a Generalizable Deep Reinforcement Learning Methodology

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

Publicação
IEEE Symposium on Computers and Communications, ISCC 2022, Rhodes, Greece, June 30 - July 3, 2022

Abstract

2022

Wi-Fi Rate Adaptation using a Simple Deep Reinforcement Learning Approach

Autores
Queirós, R; Almeida, EN; Fontes, H; Ruela, J; Campos, R;

Publicação
IEEE Symposium on Computers and Communications, ISCC 2022, Rhodes, Greece, June 30 - July 3, 2022

Abstract

2022

Energy-aware Relay Positioning in Flying Networks

Autores
Rodrigues, H; Coelho, A; Ricardo, M; Campos, R;

Publicação
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS

Abstract

Teses
supervisionadas

2021

Connect the dots: automation in investigative jornalism workflow

Autor
Joana Rodrigues da Silva

Instituição
UP-FEUP

2021

High Definition Wireless Video Streaming using Underwater Data Mules

Autor
João Pedro Teixeira Loureiro

Instituição
UP-FEUP

2021

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

Autor
Gonçalo Regueiras dos Santos

Instituição
UP-FEUP

2021

An SDN-based Overlay Networking Solution for Transparent Multi-homed Vehicular Communications

Autor
Agostinho Filipe de Almeida Coimbra Maia

Instituição
UP-FEUP

2020

A MAC Layer for Underwater Radio Communications

Autor
Filipe Borges Teixeira

Instituição
UP-FEUP