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About

About

Rui Campos has a PhD degree in Electrical and Computers Engineering in 2011, from University of Porto. Currently, he leads the “Wireless Networks” research area (http://win.inescporto.pt) of the Centre for Telecommunications and Multimedia consisting of 30 researchers, and he is an IEEE Senior Member. He has coordinated several research projects, including: SIMBED in Fed4FIRE+ Open Call 3, UGREEN, BLUECOM+, MareCom, MTGrid, the WiFIX action approved in CONFINE Open Call 1, Mare-Fi, Under-Fi, ReCoop, and HiperWireless. Rui Campos has participated in several research projects, including the following European projects: H2020 Fed4FIRE+, H2020 RAWFIE, FP7 SUNNY, FP7 CONFINE, FP6 Ambient Networks Phase 1, and FP6 Ambient Networks Phase 2. His research interests include medium access control, radio resource management, mobility management, and network auto-configuration in emerging wireless networks, with special focus on flying networks, maritime networks, and underwater networks.

Interest
Topics
Details

Details

  • Name

    Rui Lopes Campos
  • Role

    Centre Coordinator
  • Since

    17th February 2003
  • Nationality

    Portugal
  • Contacts

    +351222094299
    rui.l.campos@inesctec.pt
035
Publications

2023

Traffic-aware gateway placement and queue management in flying networks

Authors
Coelho, A; Campos, R; Ricardo, M;

Publication
AD HOC NETWORKS

Abstract
Unmanned Aerial Vehicles (UAVs) have emerged as adequate platforms to carry communications nodes, including Wi-Fi Access Points and cellular Base Stations. This has led to the concept of flying networks composed of UAVs as a flexible and agile solution to provide on-demand wireless connectivity anytime, anywhere. However, state of the art works have been focused on optimizing the placement of the access network providing connectivity to ground users, overlooking the backhaul network design. In order to improve the overall Quality of Service (QoS) offered to ground users, the placement of Flying Gateways (FGWs) and the size of the queues configured in the UAVs need to be carefully defined to meet strict performance requirements. The main contribution of this article is a traffic-aware gateway placement and queue management (GPQM) algorithm for flying networks. GPQM takes advantage of knowing in advance the positions of the UAVs and their traffic demand to determine the FGW position and the queue size of the UAVs, in order to maximize the aggregate throughput and provide stochastic delay guarantees. GPQM is evaluated by means of ns-3 simulations, considering a realistic wireless channel model. The results demonstrate significant gains in the QoS offered when GPQM is used.

2023

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

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

Publication
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

Wireless technologies towards 6G

Authors
Campos, R; Ricardo, M; Pouttu, A; Correia, LM;

Publication
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING

Abstract
This Special Issue originates from the international conference 2021 Joint EuCNC & 6G Summit (Joint European Conference on Networks and Communications and 6G Summit), which was held in June 2021 in virtual format. The Technical Programme Chairs of the conference selected the best papers and invited authors to submit an extended version of their paper by at least one-third of their length. Only the top ranked papers were invited to this Special Issue, in order to fulfil its purpose. The main target was to collect and present quality research contributions in the most recent activities related to technologies, systems and networks beyond 5G. Through this Special Issue, the state-of-the-art is presented and the new challenges highlighted, regarding the latest advances on systems and network perspectives that are already being positioned beyond 5G, bridging as well with the evolution of 5G, including applications and trials. Therefore, the motivation for this Special Issue is to present the latest and finest results on the evolution of research of mobile and wireless communications, coming, but not exclusively (since Joint EuCNC & 6G Summit is a conference open to the whole research community), from projects co-financed by the European Commission within its R&D programmes.

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

Resource allocation for dataflow applications in FANETs using anypath routing

Authors
Escobar, JJL; Ricardo, M; Campos, R; Gil-Castineira, F; Redondo, RPD;

Publication
INTERNET OF THINGS

Abstract
Management of network resources in advanced IoT applications is a challenging topic due to their distributed nature from the Edge to the Cloud, and the heavy demand of real-time data from many sources to take action in the deployment. FANETs (Flying Ad-hoc Networks) are a clear example of heterogeneous multi-modal use cases, which require strict quality in the network communications, as well as the coordination of the computing capabilities, in order to operate correctly the final service. In this paper, we present a Virtual Network Embedding (VNE) framework designed for the allocation of dataflow applications, composed of nano-services that produce or consume data, in a wireless infrastructure, such as an airborne network. To address the problem, an anypath-based heuristic algorithm that considers the quality demand of the communication between nano-services is proposed, coined as Quality-Revenue Paired Anypath Dataflow VNE (QRPAD-VNE). We also provide a simulation environment for the evaluation of its performance according to the virtual network (VN) request load in the system. Finally, we show the suitability of a multi-parameter framework in conjunction with anypath routing in order to have better performance results that guarantee minimum quality in the wireless communications.

Supervised
thesis

2023

Slicing-Aware Flying Communications Network

Author
João Cristiano Mourão Rodrigues

Institution
UP-FEUP

2023

Energy and Performance Aware Multi-Drone Placement for Sustainable Flying Networks

Author
Pedro Miguel Monteiro Ribeiro

Institution
UP-FEUP

2023

Topology Control of Flying Backhaul Mesh Networks

Author
Eduardo Nuno Moreira Soares de Almeida

Institution
UP-FEUP

2023

Context-aware Wireless Underwater Communications using a Multimodal Approach

Author
João Pedro Teixeira Loureiro

Institution
UP-FEUP

2023

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

Author
Ricardo Jorge Espirito Santo Trancoso

Institution
UP-FEUP