Um Método para Avaliação de Resiliência de Firmwares IoT por Injeção Sistemática de Falhas Sensoriais Compostas
Resumo
Nós IoT operando em campo encontram-se suscetíveis a processos progressivos de degradação sensorial, contudo inexistem métodos sistemáticos para verificar se o firmware embarcado é capaz de tolerá-los antes da implantação. Este artigo propõe um método de co-simulação determinística para avaliar a resiliência de firmwares reais por meio da injeção sistemática de falhas unitárias e compostas. A avaliação integra uma taxonomia de cinco modos de falha com métricas de dependabilidade para avaliar a resiliência dos nós de forma sistemática. Aplicado ao Golioth Air Quality Monitor, o método demonstrou sua eficácia ao distinguir com precisão os modos de falha detectáveis dos indetectáveis, validando sua capacidade de verificar a resiliência de firmwares antes da implantação em campo.Referências
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Birchler, C., Khatiri, S., Rani, P., Kehrer, T., and Panichella, S. (2025). A roadmap for simulation-based testing of autonomous cyber-physical systems: Challenges and future direction. ACM Transactions on Software Engineering and Methodology, 34(5):152:1–152:9.
Clements, A. A., Gustafson, E., Scharnowski, T., Grosen, P., Fritz, D., Kruegel, C., Vigna, G., Bagchi, S., and Payer, M. (2020). HALucinator: Firmware re-hosting through abstraction layer emulation. In Proc. 29th USENIX Security Symposium, pages 1201–1218. USENIX Association.
Cotroneo, D., De Simone, L., Liguori, P., and Natella, R. (2022). Fault injection analytics: A novel approach to discover failure modes in cloud-computing systems. IEEE Transactions on Dependable and Secure Computing, 19(3):1476–1491.
Delavernhe, F., Lecoeuche, S., and Karray, M. H. (2025). Kalman-based anomaly detection for IoT sensor streams in smart buildings. In Proc. IEEE Int. Conf. on Industrial Informatics (INDIN), pages 1–6. IEEE.
Erhan, L., Ndubuaku, M. U., Di Mauro, M., Song, W., Chen, M., Fortino, G., Bagdasar, O., and Liotta, A. (2021). Smart anomaly detection in sensor systems: A multi-perspective review. Information Fusion, 67:64–79.
Feng, B., Mera, A., and Lu, L. (2020). P2IM: Scalable and hardware-independent firmware testing via automatic peripheral interface modeling. In Proc. 29th USENIX Security Symposium, pages 1237–1254. USENIX Association.
Firouzi, F., Farahani, B. J., and Marinsek, A. (2022). The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT). Information Systems, 107:101840.
Gaddam, A., Wilkin, T., Angelova, M., and Gaddam, J. (2020). Detecting sensor faults, anomalies and outliers in the Internet of Things: A survey on the challenges and solutions. Electronics, 9(3):511.
Golioth, Inc. (2026). Air quality monitor reference design. [link]. Firmware open-source sobre Zephyr RTOS. Acesso em: mar. 2026.
He, S., Shi, K., Liu, C., Guo, B., Chen, J., and Shi, Z. (2022). Collaborative sensing in Internet of Things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 24(3):1435–1474.
Isermann, R. (2005). Model-based fault-detection and diagnosis – status and applications. Annual Reviews in Control, 29(1):71–85.
Jones, M. and Connor, S. (2025). Digital twins for predictive maintenance in cyber-physical systems: A modern perspective. ACM Transactions on Cyber-Physical Systems, 9(2):15:1–15:28.
Minerva, R., Lee, G. M., and Crespi, N. (2020). Digital twin in the IoT context: A survey on technical features, scenarios, and architectural models. Proceedings of the IEEE, 108(10):1785–1824.
Natella, R., Cotroneo, D., and Madeira, H. (2016). Assessing dependability with software fault injection: A survey. ACM Computing Surveys, 48(3):44:1–44:55.
Ni, K., Ramanathan, N., Chehade, M. N. H., Balzano, L., Nair, S., Zahedi, S., Kohler, E., Pottie, G., Hansen, M., and Srivastava, M. (2009). Sensor network data fault types. ACM Transactions on Sensor Networks, 5(3):1–29.
Scilab Enterprises (2026). Scilab/xcos – open source software for numerical computation. [link]. Acesso em: mar. 2026.
Ward, P., Smith, A., and Chen, D. (2024). Advances in TinyML: Empowering on-device intelligence for IoT sensorial resiliency. IEEE Internet of Things Journal, 11(4):2300–2315.
Zephyr Project (2026). Zephyr real-time operating system. [link]. Acesso em: maio 2026.
Avizienis, A., Laprie, J.-C., Randell, B., and Landwehr, C. (2004). Basic concepts and taxonomy of dependable and secure computing. IEEE Transactions on Dependable and Secure Computing, 1(1):11–33.
Birchler, C., Khatiri, S., Rani, P., Kehrer, T., and Panichella, S. (2025). A roadmap for simulation-based testing of autonomous cyber-physical systems: Challenges and future direction. ACM Transactions on Software Engineering and Methodology, 34(5):152:1–152:9.
Clements, A. A., Gustafson, E., Scharnowski, T., Grosen, P., Fritz, D., Kruegel, C., Vigna, G., Bagchi, S., and Payer, M. (2020). HALucinator: Firmware re-hosting through abstraction layer emulation. In Proc. 29th USENIX Security Symposium, pages 1201–1218. USENIX Association.
Cotroneo, D., De Simone, L., Liguori, P., and Natella, R. (2022). Fault injection analytics: A novel approach to discover failure modes in cloud-computing systems. IEEE Transactions on Dependable and Secure Computing, 19(3):1476–1491.
Delavernhe, F., Lecoeuche, S., and Karray, M. H. (2025). Kalman-based anomaly detection for IoT sensor streams in smart buildings. In Proc. IEEE Int. Conf. on Industrial Informatics (INDIN), pages 1–6. IEEE.
Erhan, L., Ndubuaku, M. U., Di Mauro, M., Song, W., Chen, M., Fortino, G., Bagdasar, O., and Liotta, A. (2021). Smart anomaly detection in sensor systems: A multi-perspective review. Information Fusion, 67:64–79.
Feng, B., Mera, A., and Lu, L. (2020). P2IM: Scalable and hardware-independent firmware testing via automatic peripheral interface modeling. In Proc. 29th USENIX Security Symposium, pages 1237–1254. USENIX Association.
Firouzi, F., Farahani, B. J., and Marinsek, A. (2022). The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT). Information Systems, 107:101840.
Gaddam, A., Wilkin, T., Angelova, M., and Gaddam, J. (2020). Detecting sensor faults, anomalies and outliers in the Internet of Things: A survey on the challenges and solutions. Electronics, 9(3):511.
Golioth, Inc. (2026). Air quality monitor reference design. [link]. Firmware open-source sobre Zephyr RTOS. Acesso em: mar. 2026.
He, S., Shi, K., Liu, C., Guo, B., Chen, J., and Shi, Z. (2022). Collaborative sensing in Internet of Things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 24(3):1435–1474.
Isermann, R. (2005). Model-based fault-detection and diagnosis – status and applications. Annual Reviews in Control, 29(1):71–85.
Jones, M. and Connor, S. (2025). Digital twins for predictive maintenance in cyber-physical systems: A modern perspective. ACM Transactions on Cyber-Physical Systems, 9(2):15:1–15:28.
Minerva, R., Lee, G. M., and Crespi, N. (2020). Digital twin in the IoT context: A survey on technical features, scenarios, and architectural models. Proceedings of the IEEE, 108(10):1785–1824.
Natella, R., Cotroneo, D., and Madeira, H. (2016). Assessing dependability with software fault injection: A survey. ACM Computing Surveys, 48(3):44:1–44:55.
Ni, K., Ramanathan, N., Chehade, M. N. H., Balzano, L., Nair, S., Zahedi, S., Kohler, E., Pottie, G., Hansen, M., and Srivastava, M. (2009). Sensor network data fault types. ACM Transactions on Sensor Networks, 5(3):1–29.
Scilab Enterprises (2026). Scilab/xcos – open source software for numerical computation. [link]. Acesso em: mar. 2026.
Ward, P., Smith, A., and Chen, D. (2024). Advances in TinyML: Empowering on-device intelligence for IoT sensorial resiliency. IEEE Internet of Things Journal, 11(4):2300–2315.
Zephyr Project (2026). Zephyr real-time operating system. [link]. Acesso em: maio 2026.
Publicado
19/07/2026
Como Citar
PETER, Cleber S.; SOUZA, Alexandre R. R. de; SANTOS, Hélida; LUCCA, Giancarlo; REISER, Renata; YAMIN, Adenauer C..
Um Método para Avaliação de Resiliência de Firmwares IoT por Injeção Sistemática de Falhas Sensoriais Compostas. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 18. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 398-409.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2026.23432.
