Mário Alves
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Full Professor
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About
Professor Mário Alves is a Full Professor at Instituto Politécnico do Porto Instituto Superior de Engenharia do Porto. His research focuses on educational technologies, particularly in the areas of web-based applications for learning, natural language processing for programming education, and mobility management in IoT networks. His recent publications include work on user experience in e-learning tools and optimized resource management for low-power networks.
Recent Grants
Grant: Close
MASQOTS - Mobility mAnagement in wireless Sensor networks under Quality-of-service constraints using standard and Off-The-Shelf technologies
Open Date: 2011-02-01
Close Date: 2014-07-31
Grant: Close
MASQOTS - Mobility mAnagement in wireless Sensor networks under Quality-of-service constraints using standard and Off-The-Shelf technologies
Open Date: 2011-02-01
Close Date: 2014-07-01
Grant: Close
MASQOTS - gestão de mobilidade em redes sensoriais com restrições de qualidade de serviço e usando tecnologias normalizadas e comerciais
Open Date: 2011-02-01
Close Date: 2014-07-31
Grant: Close
Cyber-Physical Systems Technologies for Energy-Optimized Data Centers
Open Date: 2010-10-01
Close Date: 2013-12-31
Grant: Close
Cyber-Physical Systems Technologies for Energy-Optimized Data Centers
Open Date: 2010-10-01
Close Date: 2013-12-01
Articles (7)
Year:
2025
RPL—Routing Protocol for Low-Power and Lossy Networks (usually pronounced “ripple”)—is the de facto standard for IoT networks. However, it neglects to exploit IoT devices’ full capacity to optimize their transmission power, mainly because it is quite challenging to do so in parallel with the routing strategy, given the dynamic nature of wireless links and the typically constrained resources of IoT devices. Adapting the transmission power requires dynamically assessing many parameters, such as the probability of packet collisions, energy consumption, the number of hops, and interference. This paper introduces Adaptive Control of Transmission Power for RPL (ACTOR) for the dynamic optimization of transmission power. ACTOR aims to improve throughput in dense networks by passively exploring different transmission power levels. The classic solutions of bandit theory, including the Upper Confidence Bound (UCB) and Discounted UCB, accelerate the convergence of the exploration and guarantee its optimality. ACTOR is also enhanced via mechanisms to blacklist undesirable transmission power levels and stabilize the topology of parent–child negotiations. The results of the experiments conducted on our 40-node, 12-node testbed demonstrate that ACTOR achieves a higher packet delivery ratio by almost 20%, reduces the transmission power of nodes by up to 10 dBm, and maintains a stable topology with significantly fewer parent switches compared to the standard RPL and the selected benchmarks. These findings are consistent with simulations conducted across 7 different scenarios, where improvements in end-to-end delay, packet delivery, and energy consumption were observed by up to 50%.
Year:
2024
Year:
2024
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2022
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2021
Year:
2021
Year:
2020
Collaborators (6)
Francisco Pereira
Professor Coordenador
Instituto Politécnico do Porto Instituto Superior de Engenharia do Porto
Nuno Pereira
Professor (Professor Adjunto)
Instituto Politécnico do Porto Instituto Superior de Engenharia do Porto
Paulo Oliveira
Professor Adjunto
Instituto Politécnico do Porto Instituto Superior de Engenharia do Porto
Hossein Fotouhi
Mälardalens universitet
Mats Björkman
Mälardalen University
Armando Sousa
Auxiliary Professor
Universidade do Porto Faculdade de Engenharia
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