Simulador de aplicações de Inteligência Artificial das Coisas para monitoramento em tempo real
Resumo
O avanço das tecnologias de Internet das Coisas (Intelligence of Things – IoT) e Inteligência Artificial (IA) abriu novas possibilidades de aplicações em diversas áreas, incluindo monitoramento em tempo real. Este trabalho apresenta o desenvolvimento de um simulador de aplicações de Inteligência Artificial das Coisas (Artificial Intelligence of Things – AIoT) para monitoramento de áreas rurais utilizando Veículos Aéreos Não Tripulados (VANTs). A proposta integra uma arquitetura edge/fog/cloud, onde VANTs equipados com câmeras e algoritmos de IA realizam a detecção de animais em tempo real. O sistema distribui a carga de processamento entre os dispositivos de borda e o servidor fog, otimizando a eficiência e a precisão das detecções. A interface gráfica desenvolvida permite a visualização e gerenciamento de simulações, facilitando a análise e a tomada de decisões. Os resultados demonstram a viabilidade e eficácia do sistema para monitoramento de ambientes de difícil acesso, contribuindo para uma gestão eficiente de recursos e resposta rápida a eventos da aplicação.
Referências
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Barriga, J. A., Clemente, P. J., Pérez-Toledano, M. A., Jurado-Málaga, E., and Hernández, J. (2023). Design, code generation and simulation of iot environments with mobility devices by using model-driven development: Simulateiot-mobile. Pervasive and Mobile Computing, 89:101751.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers. CoRR, abs/2005.12872.
Del-Pozo-Puñal, E., García-Carballeira, F., and Camarmas-Alonso, D. (2023). A scalable simulator for cloud, fog and edge computing platforms with mobility support. Future Generation Computer Systems, 144:117–130.
Eclipse, F. (2024). Mosquitto Documentation. Accessed: 2024-06-24.
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Kertész, A., Pflanzner, T., and Gyimóthy, T. (2019). A mobile iot device simulator for iot-fog-cloud systems. Journal of Grid Computing, 17:529–551.
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., and Dollár, P. (2015). Microsoft coco: Common objects in context.
Minoli, D., Sohraby, K., and Occhiogrosso, B. (2017). Iot considerations, requirements, and architectures for smart buildings—energy optimization and next-generation building management systems. IEEE Internet of Things Journal, 4(1):269–283.
Myśliwiec, K. (2024). NestJS Documentation. Accessed: 2024-07-10.
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OASIS (2024). MQTT Protocol. Accessed: 2024-07-10.
Obaid, A., Shawkat, A. S., and Abdulhussein, N. S. (2023). Exploring the potential of a-resnet in person-independent face recognition and classification. International Journal of Advanced Network, Monitoring and Controls, 8:12–19.
Pereira, R. S., Prazeres, C. V. S., Barbosa, M. T. M., Barros, E. B. C., and Peixoto, M. L. M. (2021). Iotfogsim: Um simulador orientado a eventos para avaliaçao de aplicaçoes baseadas em iot-fog-cloud. In Anais Estendidos do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 25–32. SBC.
Prisma, I. (2024). Prisma Documentation. Accessed: 2024-07-10.
SmartBear (2024). Swagger Documentation. Accessed: 2024-07-01.
Stewart, J. C., Davis, G. A., and Igoche, D. A. (2020). Ai, iot, and aiot: Definitions and impacts on the artificial intelligence curriculum. Issues in Information Systems, 21(4).
Wan, C., Pang, Y., and Lan, S. (2022). Overview of yolo object detection algorithm. International Journal of Computing and Information Technology.
Wang, C.-Y. and Liao, H.-Y. M. (2024). YOLOv9: Learning what you want to learn using programmable gradient information.
Wang, H., Li, K., and Xu, C. (2022). A new generation of resnet model based on artificial intelligence and few data driven and its construction in image recognition model. Computational Intelligence and Neuroscience, 2022.
Zhu, S., Ota, K., and Dong, M. (2022). Green AI for IIoT: Energy efficient intelligent edge computing for industrial internet of things. IEEE Transactions on Green Communications and Networking, 6:79–88.
Bajaj, K., Sharma, B., and Singh, R. (2022). Comparative analysis of simulators for iot applications in fog/cloud computing. In 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), pages 983–988. IEEE.
Barriga, J. A., Clemente, P. J., Pérez-Toledano, M. A., Jurado-Málaga, E., and Hernández, J. (2023). Design, code generation and simulation of iot environments with mobility devices by using model-driven development: Simulateiot-mobile. Pervasive and Mobile Computing, 89:101751.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers. CoRR, abs/2005.12872.
Del-Pozo-Puñal, E., García-Carballeira, F., and Camarmas-Alonso, D. (2023). A scalable simulator for cloud, fog and edge computing platforms with mobility support. Future Generation Computer Systems, 144:117–130.
Eclipse, F. (2024). Mosquitto Documentation. Accessed: 2024-06-24.
Fielding, R., Gettys, J., Mogul, J., Frystyk, H., Masinter, L., Leach, P., and Berners-Lee, T. (1999). Hypertext transfer protocol – http/1.1. Technical report, IETF. Accessed: 2024-07-10.
Geraci, G., López-Pérez, D., Benzaghta, M., and Chatzinotas, S. (2022). Integrating terrestrial and non-terrestrial networks: 3d opportunities and challenges. ArXiv, abs/2207.10385.
Google (2024). Angular Documentation. Accessed: 2024-07-10.
Group, P. G. D. (2024). PostgreSQL Documentation. Accessed: 2024-07-10.
Kertész, A., Pflanzner, T., and Gyimóthy, T. (2019). A mobile iot device simulator for iot-fog-cloud systems. Journal of Grid Computing, 17:529–551.
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., and Dollár, P. (2015). Microsoft coco: Common objects in context.
Minoli, D., Sohraby, K., and Occhiogrosso, B. (2017). Iot considerations, requirements, and architectures for smart buildings—energy optimization and next-generation building management systems. IEEE Internet of Things Journal, 4(1):269–283.
Myśliwiec, K. (2024). NestJS Documentation. Accessed: 2024-07-10.
Network, M. D. (2024). Web Documentation. Accessed: 2024-07-10.
OASIS (2024). MQTT Protocol. Accessed: 2024-07-10.
Obaid, A., Shawkat, A. S., and Abdulhussein, N. S. (2023). Exploring the potential of a-resnet in person-independent face recognition and classification. International Journal of Advanced Network, Monitoring and Controls, 8:12–19.
Pereira, R. S., Prazeres, C. V. S., Barbosa, M. T. M., Barros, E. B. C., and Peixoto, M. L. M. (2021). Iotfogsim: Um simulador orientado a eventos para avaliaçao de aplicaçoes baseadas em iot-fog-cloud. In Anais Estendidos do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 25–32. SBC.
Prisma, I. (2024). Prisma Documentation. Accessed: 2024-07-10.
SmartBear (2024). Swagger Documentation. Accessed: 2024-07-01.
Stewart, J. C., Davis, G. A., and Igoche, D. A. (2020). Ai, iot, and aiot: Definitions and impacts on the artificial intelligence curriculum. Issues in Information Systems, 21(4).
Wan, C., Pang, Y., and Lan, S. (2022). Overview of yolo object detection algorithm. International Journal of Computing and Information Technology.
Wang, C.-Y. and Liao, H.-Y. M. (2024). YOLOv9: Learning what you want to learn using programmable gradient information.
Wang, H., Li, K., and Xu, C. (2022). A new generation of resnet model based on artificial intelligence and few data driven and its construction in image recognition model. Computational Intelligence and Neuroscience, 2022.
Zhu, S., Ota, K., and Dong, M. (2022). Green AI for IIoT: Energy efficient intelligent edge computing for industrial internet of things. IEEE Transactions on Green Communications and Networking, 6:79–88.
Publicado
11/09/2024
Como Citar
GONÇALVES, Allan Jheyson R.; CASTRO, Ana Beatriz N. de; EVILLY, Brenda; LEÃO, Erico Meneses; TORRES NETO, Jose R.; SILVA, Romuere R. V.; C. FILHO, Antonio Oseas; RABELO, Ricardo A. L..
Simulador de aplicações de Inteligência Artificial das Coisas para monitoramento em tempo real. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 12. , 2024, Parnaíba/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 159-168.
DOI: https://doi.org/10.5753/ercemapi.2024.243722.
