Publications

2023

• da Silva, R. A., & da Fonseca, N. L., Location of fog nodes mounted on fixed-wing UAVs. Vehicular Communications
• Gonçalves, D. M., Puliafito, C., Mingozzi, E., Bittencourt, L. F., & Madeira, E. R., End-to-end network slicing in vehicular clouds using the MobFogSim simulator. Ad Hoc Networks.
• Rezende, P., Curado, M., & Madeira, E., NS-ENFORCER: Enforcing Network Slicing on Radio Access Networks. Journal of Network and Systems Management.
• Meneguette, R., De Grande, R., Ueyama, J., Filho, G. P. R., & Madeira, E., Vehicular edge computing: architecture, resource management, security, and challenges. ACM Computing Surveys (CSUR).
• Gonçalves, D. M., Puliafito, C., Mingozzi, E., Bittencourt, L. F., & Madeira, E. R., End-to-end network slicing in vehicular clouds using the MobFogSim simulator. Ad Hoc Networks.
• Peixoto, M. L. M., Mota, E., Maia, A. H. O., Lobato, W., Salahuddin, M. A., Boutaba, R., & Villas, L. A, FogJam: A Fog Service for Detecting Traffic Congestion in a Continuous Data Stream VANET. Ad Hoc Networks.

2022

• Chantre, H. D., & da Fonseca, N. L., Design of 5G MEC-Based Networks With 1: N: K Protection Scheme. IEEE Transactions on Network and Service Management.
• Casas-Velasco, D. M., Granelli, F., & da Fonseca, N. L., Impact of Background Traffic on the BBR and CUBIC Variants of the TCP Protocol. IEEE Networking Letters.
• Ciceri, O. J., Astudillo, C. A., Zhu, Z., & da Fonseca, N. L., Federated learning over next-generation Ethernet passive optical networks. IEEE Network.
• Wu, M., Da Fonseca, N. L., & Zhu, Z., Dynamic cross-layer restoration to resolve packet layer outages in flexe-over-EONs. IEEE Transactions on Network and Service Management.
• Villota-Jacome, W. F., Rendon, O. M. C., & da Fonseca, N. L., Admission control for 5G core network slicing based on deep reinforcement learning. IEEE Systems Journal.
• Ordonez, A., Caicedo, O. M., Villota, W., Rodriguez-Vivas, A., & da Fonseca, N. L., Model-Based Reinforcement Learning with Automated Planning for Network Management. Sensors.
• Casas-Velasco, D. M., Rendon, O. M. C., & da Fonseca, N. L., DRSIR: A deep reinforcement learning approach for routing in software-defined networking. IEEE Transactions on Network and Service Management.
• Trindade, S., Bittencourt, L. F., & da Fonseca, N. L., Management of resource at the network edge for federated learning. (accepted for publication).
• Coelho, M. S., Melo, C. A., & Fonseca, N. L. D., An encoding-aware bitrate adaptation mechanism for video streaming over HTTP. Multimedia Tools and Applications.
• Granelli, F., Bassoli, R., Nötzel, J., Fitzek, F. H., Boche, H., & da Fonseca, N. L., A novel architecture for future classical-quantum communication networks. Wireless Communications and Mobile Computing.
• Ciceri, O. J., Astudillo, C. A., & da Fonseca, N. L., Performance evaluation of multi-ONU customers in ethernet passive optical networks. Photonic Network Communications.
• Ciceri, O. J., Astudillo, C. A., & da Fonseca, N. L., DBA algorithm for the support of multi-tiered bandwidth guarantee in Ethernet passive optical networks. Photonic Network Communications.
• Barbieri, L., Madeira, E., Stroeh, K., & van der Aalst, W., A natural language querying interface for process mining. Journal of Intelligent Information Systems.
• Santos, F., Immich, R., & Madeira, E. R., Multimedia services placement algorithm for cloud–fog hierarchical environments. Computer Communications.
• Trindade, S., Bittencourt, L. F., & da Fonseca, N. L., Resource management at the network edge for federated learning. Digital Communications and Networks.
• A. M. de Souza, H. F. Oliveira, Z. Zhao, T. Braun, A. A. F. Loureiro and L. A. Villas., Enhancing Sensing and Decision-Making of Automated Driving Systems With Multi-Access Edge Computing and Machine Learning. IEEE Intelligent Transportation Systems Magazine.
• Jorge, J., Barros, P. H., Yokoyama, R., Guidoni, D., Ramos, H. S., Fonseca, N., & Villas, L., Applying Federated Learning in the detection of Freezing of Gait in Parkinson’s disease. IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC).
• Rodrigues, D. O., Braun, T., Maia, G., & Villas, L., Mobility-aware Software-Defined Service-Centric Networking. International Conference on Computer Communications and Networks (ICCCN)
• da Costa, J. B., Junior, W. V. L., de Souza, A. M., Cerqueira, E., Rosário, D., & Villas, L. A., NEMESIS: Mecanismo para Formaçao de Nuvens Veiculares Baseado em Previsao de Mobilidade. In Anais do XL Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos.
• Ladeira, L. Z., Santos, F., Cléopas, L., Buteneers, P., & Villas, L., Neo-nda: Neo natural language data augmentation. IEEE 16th International Conference on Semantic Computing (ICSC).
• Sanches, M. F., de Sá, J. M., Foerste, H. T., Souza, R. R., Dos Reis, J. C., & Villas, L. A., Textual Datasets For Portuguese-Brazilian Language Models. In Anais do IV Dataset Showcase Workshop.
• Nobre, R. A., Nascimento, K. C. D., Vargas, P. A., Valejo, A. D. B., Pessin, G., Villas, L. A., & Filho, G. P. R., AURORA: an autonomous agent-oriented hybrid trading service. Neural Computing and Applications.

2021

• Lago, D. G., da Silva, R. A., Madeira, E. R., da Fonseca, N. L., & Medhi, D., SinergyCloud: A simulator for evaluation of energy consumption in data centers and hybrid clouds. Simulation Modelling Practice and Theory.
• Trindade, S., Torres, R. D. S., Zhu, Z., & Fonseca, N. L. D., Cognitive control-loop for elastic optical networks with space-division multiplexing. Sensors.
• Forero, F., & da Fonseca, N. L., Distribution of multi-hop latency for probabilistic broadcasting protocols in grid-based Wireless Sensor Networks. Ad Hoc Networks.
• da Silva, R. A., da Fonseca, N. L., & Boutaba, R., Evaluation of the employment of UAVs as fog nodes. IEEE Wireless Communications.
• Astudillo, C. A., Hossain, E., & da Fonseca, N. L., Random Access Based on Maximum Average Distance Code for Massive MTC in Cellular IoT Networks. IEEE Wireless Communications Letters.
• Moura, P. M., & da Fonseca, N. L., Multipath routing in elastic optical networks with space-division multiplexing. IEEE Communications Magazine.
• Liang, H., da Fonseca, N. L., & Zhu, Z., On the cross-layer network planning for flexible Ethernet over elastic optical networks. IEEE Transactions on Network and Service Management.
• Dong, L., da Fonseca, N. L., & Zhu, Z, Application-driven provisioning of service function chains over heterogeneous NFV platforms. IEEE Transactions on Network and Service Management.
• Casas-Velasco, D. M., Rendon, O. M. C., & da Fonseca, N. L., Intelligent routing based on reinforcement learning for software-defined networking. IEEE Transactions on Network and Service Management.
• Trindade, S., & Da Fonseca, N. L., Machine learning for spectrum defragmentation in space-division multiplexing elastic optical networks. IEEE Network.
• Guevara, J. C., & da Fonseca, N. L., Task scheduling in cloud-fog computing systems. Peer-to-Peer Networking and Applications.
• Montoya‐Munoz, A. I., Casas‐Velasco, D., Estrada‐Solano, F., Caicedo Rendon, O. M., & Saldanha da Fonseca, N. L., An approach based on Yet Another Next Generation for software‐defined networking management. International Journal of Communication Systems.
• Santos, F., Aquino, A. L., Madeira, E. R., & Cabral, R. S., Temporal complex networks modeling applied to vehicular ad-hoc networks. Journal of Network and Computer Applications.
• Imani, M., Hasan, M. M., Bittencourt, L. F., McClymont, K., & Kapelan, Z., A novel machine learning application: Water quality resilience prediction Model. Science of the Total Environment.
• Peixoto, M. L. M., Genez, T. A., & Bittencourt, L. F., Hierarchical scheduling mechanisms in multi-level fog computing. IEEE Transactions on services computing.
• Rodrigues, D. O., Maia, G., Braun, T., Loureiro, A. A., Peixoto, M. L., & Villas, L. A., Exploring hybrid-multimodal routing to improve user experience in urban trips. Applied Sciences.
• Campos, G. O., Villas, L. A., & Da Cunha, F. D., Analysis of air pollution utilizing virtual sensor models. IEEE Latin-American Conference on Communications (LATINCOM).
• Sanches, M. F., Oliveira, M. V. R., Ciceri, O. J., Ladeira, L. Z., Garcia, I. C., Da Fonseca, N. L., & Villas, L. A., EFIS-Ecological Fuel-consumption Intelligent System. International Conference on Distributed Computing in Sensor Systems (DCOSS).
• Hadj Mohamed, A., Cesar dos Reis, J., & Villas, L., Forecasting Citywide Crowd Flows with Unbalanced Human Mobility Distributions. In Proceedings of the 19th ACM International Symposium on Mobility Management and Wireless Access.
• Karimzadeh, M., Aebi, R., de Souza, A. M., Zhao, Z., Braun, T., Sargento, S., & Villas, L., Reinforcement learning-designed LSTM for trajectory and traffic flow prediction. IEEE wireless communications and networking conference (WCNC).

Complete Bibliography

TBD…