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Fishing effort and enforcement in the Azores Marine Protected Areas
Publication . Moura, Ricardo; Santos, Nuno Pessanha; Catarino, Maria Eduarda; CMA - Centro de Matemática e Aplicações; KeAi Communications Co.
Fishing is a significant global food source, providing protein for millions of people. The Food and Agriculture Organization (FAO) is committed to ensuring access to high-quality food, reducing hunger, and promoting sustainable fisheries to address global population growth and hunger. However, illegal, unreported, and unregulated fishing poses a significant challenge, threatening marine biodiversity and food security. Portugal has the 10th largest Exclusive Economic Zone (EEZ), with waters around mainland Portugal, the Azores, and Madeira. This research focuses on the Azores region, known for its traditional multispecific fishery around the island slopes and seamounts. The region's fisheries face data scarcity issues and complicating effective management. By combining Vessel Monitoring System (VMS) records from 2016 to 2022 and Portuguese Navy (PoN) Fiscalization Reports (FISCREP) from 2015 to 2022, it was possible to use appropriate metrics to characterize the fishing effort and analyze the effectiveness of the inspections conducted in the Azores EEZ. The Total Boat-Meter (TBM) metric combines the number and length of boats to quantify the fishing effort better. The analysis shows that the fishing effort in the protected areas is very high, highlighting the pressure on the protected ecosystems. The findings aim to assist regulatory institutions and researchers in assessing fishing pressure and promoting sustainable fisheries management in the Azores to preserve marine ecosystems.
Multi-Eavesdropper Detection Through PHY-Aware Cell-Free AP Selection
Publication . Martins, João; Conceição, Filipe; Gomes, Marco; Silva, Vitor; Dinis, Rui; Faculdade de Ciências e Tecnologia (FCT); Institute of Electrical and Electronics Engineers (IEEE)
The ability to provide reliable data rates across several coverage areas establishes massive multiple-input multiple-output (m-MIMO) cell-free (CF) systems as a pivotal technology for future sixth-generation (6G) systems. CF networks do, however, introduce additional security and network integrity vulnerabilities. For that, to complement the traditional cryptographic algorithms, physical layer security (PLS) can be an effective strategy for acquiring essential wireless network information in order to develop authentication methods against impersonation attacks. To prevent these spoofing attacks, we propose leveraging the wireless channel and the access point selection (APS) allocation schemes as authentication mechanisms. Our approach begins with a threshold-based analysis of spectral efficiency (SE) losses across different APS schemes. We then propose an algorithm capable of estimating the number of eavesdroppers executing active attacks while identifying the targeted user equipment (UE). Finally, we test the robustness of our detection scheme by examining how SE loss and achievable secrecy SE change when a single attacker is positioned at various locations relative to the targeted UE. Results demonstrate that monitoring these parameters provides critical insights into network performance and the impact of active eavesdropping. These findings highlight the potential of integrating PLS with upper-layer authentication protocols to significantly enhance wireless network security.
Survey on Context-Aware Radio Frequency-Based Sensing
Publication . Casmin, Eugene; Oliveira, Rodolfo; DEE - Departamento de Engenharia Electrotécnica e de Computadores; MDPI - Multidisciplinary Digital Publishing Institute
Radio frequency (RF) spectrum sensing is critical for applications requiring precise object and posture detection and classification. This survey aims to provide a focused review of context-aware RF-based sensing, emphasizing its principles, advancements, and challenges. It specifically examines state-of-the-art techniques such as phased array radar, synthetic aperture radar, and passive RF sensing, highlighting their methodologies, data input domains, and spatial diversity strategies. The paper evaluates feature extraction methods and machine learning approaches used for detection and classification, presenting their accuracy metrics across various applications. Additionally, it investigates the integration of RF sensing with other modalities, such as inertial sensors, to enhance context awareness and improve performance. Challenges like environmental interference, scalability, and regulatory constraints are addressed, with insights into real-world mitigation strategies. The survey concludes by identifying emerging trends, practical applications, and future directions for advancing RF sensing technologies.
Receiver Design for OFDM Schemes With Low-Resolution ADCs
Publication . Madeira, João; Mokhtari, Zahra; Guerreiro, João; Dinis, Rui; Faculdade de Ciências e Tecnologia (FCT); Institute of Electrical and Electronics Engineers (IEEE)
A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML Algorithms
Publication . Farkhari, Hamed; Viana, Joseanne; Kahvazadeh, Sarang; Sebastião, Pedro; Jimenez, Victor P.Gil; Dinis, Rui; Faculdade de Ciências e Tecnologia (FCT); Institute of Electrical and Electronics Engineers (IEEE)
Deep Neural Networks (DNNs) have gained prominence due to their remarkable accomplishments across various domains, including telecommunications and security. Their integration into decision-making processes within 5G telecommunication systems and UAV security is noteworthy. However, the iterative nature of DNN data processing can introduce uncertainties in classification decisions, impacting their reliability. This paper presents novel combined preprocessing and post-processing techniques designed to enhance the accuracy and reliability of binary classification DNNs by managing uncertainty levels. The study evaluates these methods through calibration error metrics, confidence values, and the Reliability Score (RS), which quantifies the disparity between Mean Accuracy (MA) and Mean Confidence (MC). Additionally, the effectiveness of these methods is demonstrated by applying them to simulated real-world scenarios to improve jamming detection reliability in UAV communications. The proposed algorithms' impact is compared against baseline DNNs and DNNs augmented with the eXtreme Gradient Boosting (XGB) classifier, as well as the latest research to validate our approach. This paper comprehensively overviews the experimental setup, dataset, deep network architecture, preprocessing and post-processing techniques, evaluation metrics, and results. By addressing uncertainty in XGB and DNN outputs, this study improves the trustworthiness of ML-DNN-based decision-making processes in 5G UAV security scenarios.
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Fundação para a Ciência e a Tecnologia
Programa de financiamento
Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
Número da atribuição
UIDB/50008/2020