Arquitetura Distribuída para Monitoramento de Incêndios: Uma Abordagem com Redes de Petri Estocásticas
Resumo
Os incêndios florestais representam uma séria ameaça ambiental, climática e social, exigindo sistemas de monitoramento com alta capacidade de resposta e resiliência. Este trabalho apresenta a modelagem e avaliação de desempenho de um sistema real de monitoramento de incêndios, baseado em Redes de Petri Estocásticas (SPN) e em uma arquitetura distribuída orientada a microsserviços escaláveis. O modelo SPN captura o comportamento dinâmico de componentes como Frame Producer, Frame Consumer, Rules Manager, Event Manager e Mosquitto, permitindo a análise detalhada de métricas críticas como tempo médio de resposta, throughput, utilização de recursos e probabilidade de descarte. A abordagem proposta evidencia como diferentes configurações impactam o desempenho, demonstrando, por exemplo, que a adoção de 4 instâncias no Frame Consumer reduz significativamente o MRT, além de evitar sobrecarga e permitir uma reserva de recursos. Esses resultados fornecem subsídios técnicos para a otimização e o dimensionamento de infraestruturas de monitoramento em tempo real.Referências
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Mukhia, R., Sarambage Jayarathna, K. G., and Lertsinsrubtavee, A. (2023). Performance evaluation of lorawan forest fire monitoring network in the wild. In Proceedings of the 18th Asian Internet Engineering Conference, pages 96–104.
Papaioannou, A., Verikios, P., Kouzinopoulos, C. S., Ioannidis, D., and Tzovaras, D. (2021). A low-power embedded system for fire monitoring and detection using a multilayer perceptron. In 2021 IEEE Sensors Applications Symposium (SAS), pages 1–6. IEEE.
Reddy, P. D. K., Margala, M., Shankar, S. S., and Chakrabarti, P. (2024). Early fire danger monitoring system in smart cities using optimization-based deep learning techniques with artificial intelligence. Journal of Reliable Intelligent Environments, 10(2):197–210.
Sabino, A., Lima, L. N., Brito, C., Feitosa, L., Caetano, M. F., Barreto, P. S., and Silva, F. A. (2024). Forest fire monitoring system supported by unmanned aerial vehicles and edge computing: a performance evaluation using petri nets. Cluster Computing, pages 1–21.
Shri, A. L., Swathiha, R., Ismail, M. M., Krithiga, M., Meenakshi, B., and Kathir, M. (2024). Iot-enhanced forest fire monitoring and notification system. In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN), pages 860–865. IEEE.
Silva, B., Matos, R., Callou, G., Figueiredo, J., Oliveira, D., Ferreira, J., Dantas, J., Lobo, A., Alves, V., and Maciel, P. (2015). Mercury: An integrated environment for performance and dependability evaluation of general systems. In Proceedings of industrial track at 45th dependable systems and networks conference, DSN, pages 1–4.
Talaat, F. M. and Zain, H. (2023). An improved fire detection approach based on yolo-v8 for smart cities. Neural Computing and Applications, 35(28):20939–20954.
Zhai, Y. (2024). Design and optimization of smart fire iot cloud platform based on big data technology. In 2024 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE), pages 843–846. IEEE.
Zhang, W., Zhao, B., Gao, S., Zheng, Y., Zhou, L., and Liu, S. (2023). Development of cotton picker fire monitoring system based on ga-bp algorithm. Sensors, 23(12):5553.
Al-Dhief, F. T., Muniyandi, R. C., Sabri, N., Hamdan, M., Latiff, N. M. A., Albadr, M. A. A., Khairi, M. H. H., Hamzah, M., and Khan, S. (2022). Forest fire detection using new routing protocol. Sensors, 22(20):7745.
Chen, L. and Ha, W. (2018). Reliability prediction and qos selection for web service composition. International Journal of Computational Science and Engineering, 16(2):202–211.
Dinesh, M., Dalei, J., and Ray, K. C. (2024). An optimized lightweight convolutional neural network framework and hardware system for forest fire monitoring. Available at SSRN 4913945.
Hoover, K. and Hanson, L. A. (2023). Wildfire statistics. Congressional Research Service.
Jones, M. W., Kelley, D. I., Burton, C. A., Di Giuseppe, F., Barbosa, M. L. F., Brambleby, E., Hartley, A. J., Lombardi, A., Mataveli, G., McNorton, J. R., et al. (2024). State of wildfires 2023–24. Earth System Science Data Discussions, 2024:1–124.
Maciel, P. R. M. (2023). Performance, reliability, and availability evaluation of computational systems, volume I: performance and background. Chapman and Hall/CRC.
Mohammed, M. S., Abbas, A. H., and Abdullah, N. A. (2024). Intelligent surveillance system for fire detection using yolov8. Iraqi Journal for Computers and Informatics, 50(1):105–122.
Mukhia, R., Sarambage Jayarathna, K. G., and Lertsinsrubtavee, A. (2023). Performance evaluation of lorawan forest fire monitoring network in the wild. In Proceedings of the 18th Asian Internet Engineering Conference, pages 96–104.
Papaioannou, A., Verikios, P., Kouzinopoulos, C. S., Ioannidis, D., and Tzovaras, D. (2021). A low-power embedded system for fire monitoring and detection using a multilayer perceptron. In 2021 IEEE Sensors Applications Symposium (SAS), pages 1–6. IEEE.
Reddy, P. D. K., Margala, M., Shankar, S. S., and Chakrabarti, P. (2024). Early fire danger monitoring system in smart cities using optimization-based deep learning techniques with artificial intelligence. Journal of Reliable Intelligent Environments, 10(2):197–210.
Sabino, A., Lima, L. N., Brito, C., Feitosa, L., Caetano, M. F., Barreto, P. S., and Silva, F. A. (2024). Forest fire monitoring system supported by unmanned aerial vehicles and edge computing: a performance evaluation using petri nets. Cluster Computing, pages 1–21.
Shri, A. L., Swathiha, R., Ismail, M. M., Krithiga, M., Meenakshi, B., and Kathir, M. (2024). Iot-enhanced forest fire monitoring and notification system. In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN), pages 860–865. IEEE.
Silva, B., Matos, R., Callou, G., Figueiredo, J., Oliveira, D., Ferreira, J., Dantas, J., Lobo, A., Alves, V., and Maciel, P. (2015). Mercury: An integrated environment for performance and dependability evaluation of general systems. In Proceedings of industrial track at 45th dependable systems and networks conference, DSN, pages 1–4.
Talaat, F. M. and Zain, H. (2023). An improved fire detection approach based on yolo-v8 for smart cities. Neural Computing and Applications, 35(28):20939–20954.
Zhai, Y. (2024). Design and optimization of smart fire iot cloud platform based on big data technology. In 2024 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE), pages 843–846. IEEE.
Zhang, W., Zhao, B., Gao, S., Zheng, Y., Zhou, L., and Liu, S. (2023). Development of cotton picker fire monitoring system based on ga-bp algorithm. Sensors, 23(12):5553.
Publicado
20/07/2025
Como Citar
SABINO, Arthur; LIMA, Luiz Nelson; BARBOSA, Vandirleya; FEITOSA, Leonel; FREITAS, Leonardo; CAETANO, Marcos F.; BARRETO, Priscila Solis; SILVA, Francisco Airton.
Arquitetura Distribuída para Monitoramento de Incêndios: Uma Abordagem com Redes de Petri Estocásticas. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 16. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 137-146.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2025.8401.
