SMART SENSORS FOR ANOMALY DETECTION DRIVEN BY TINYML IN IOT ARCHITECTURE

SMART SENSORS FOR ANOMALY DETECTION DRIVEN BY TINYML IN IOT ARCHITECTURE

Pereira, Eduardo dos Santos; Marcondes, Leonardo S.; Silva, Josemar M.

Full article:

In general, the architecture of Internet of Things (IoT) systems is organized in layers, starting with the perception layer, or physical computing, which involves the sensing process, followed by communication, processing, or middleware layer, application, and business layers. In this paper, we propose a new middleware layer positioned just above the perception layer, by integrating more powerful microcontrollers capable of running artificial intelligence algorithms, known as TinyML. We have developed an algorithm based on extreme value theory for real-time anomaly detection. The results indicate the feasibility of implementing the proposed architecture for real-time monitoring and automated decision-making in industrial systems.

In general, the architecture of Internet of Things (IoT) systems is organized in layers, starting with the perception layer, or physical computing, which involves the sensing process, followed by communication, processing, or middleware layer, application, and business layers. In this paper, we propose a new middleware layer positioned just above the perception layer, by integrating more powerful microcontrollers capable of running artificial intelligence algorithms, known as TinyML. We have developed an algorithm based on extreme value theory for real-time anomaly detection. The results indicate the feasibility of implementing the proposed architecture for real-time monitoring and automated decision-making in industrial systems.

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DOI: 10.5151/siintec2023-300109

Referências bibliográficas

• [1] BURHAN, Muhammad et al. IoT elements, layered architectures, and security issues: A comprehensive survey. Sensors, v. 18, n. 9, p. 2796, 2018. FAROOQ, M. Umar et al. A review on internet of things (IoT). International journal 0f computer applications, v. 113, n. 1, p. 1-7, 2015. GUMBEL, Emil Julius. Statistical theory of extreme values and some practical applications: a series of lectures. US Government Printing Office, 1954. JIANG, R.; MURTHY, D. N. P. A study of Weibull shape parameter: Properties and significance. Reliability Engineering & System Safety, v. 96, n. 12, p. 1619-1626, 2011. MRABET, Hichem et al. A survey of IoT security based on a layered architecture of sensing and data analysis. Sensors, v. 20, n. 13, p. 3625, 2020. SCHEIRER, Walter J. et al. Meta-recognition: The theory and practice of recognition score analysis. IEEE transactions on pattern analysis and machine intelligence, v. 33, n. 8, p. 1689-1695, 2011. ZHONG, Chang-Le; ZHU, Zhen; HUANG, Ren-Gen. Study on the IOT architecture and gateway technology. In: 2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, 2015. p. 196-199.

Como citar:

Pereira, Eduardo dos Santos ; Marcondes, Leonardo S. ; Silva, Josemar M. ; "SMART SENSORS FOR ANOMALY DETECTION DRIVEN BY TINYML IN IOT ARCHITECTURE", p-25-32. In: . São Paulo: Blucher, 2023.
ISSN 23577592, DOI 10.5151/siintec2023-300109