Porting and execution of anomalies detection models on embedded systems in IoT: Demo abstract

Sudharsan, Bharath, Patel, Pankesh, Wahid, Abdul, Yahya, Muhammad, Breslin, John G., & Ali, Muhammad Intizar. (2021). Porting and Execution of Anomalies Detection Models on Embedded Systems in IoT: Demo abstract. Paper presented at the Proceedings of the International Conference on Internet-of-Things Design and Implementation, Charlottesvle, VA, USA, 18-21 May, https://doi.org/10.1145/3450268.3453513

Abstract

In the Industry 4.0 era, Microcontrollers (MCUs) based tiny embedded sensor systems have become the sensing paradigm to interact with the physical world. In 2020, 25.6 billion MCUs were shipped, and over 250 billion MCUs are already operating in the wild. Such low-power, low-cost MCUs are being used as the brain to control diverse applications and soon will become the global digital nervous system. In an Industrial IoT setup, such tiny MCU-based embedded systems are equipped with anomaly detection models and mounted on production plant machines for monitoring the machine’s health/condition. These models process the machine’s health data (from temperature, RPM, vibration sensors) and raise timely alerts when it predicts/detects data patterns that show deviations from the normal operation state. In this demo, we train One Class Support Vector Machines (OCSVM) based anomaly detection models and port the trained models to their MCU executable versions. We then deploy and execute the ported models on 4 popular MCUs and report their on-board inference performance along with their memory (Flash and SRAM) consumption. The steps/procedure that we show in the demo is generic, and the viewers can use it to efficiently port a wide variety of datasets-trained classifiers and execute them on different resource-constrained MCU and small CPU-based devices.

Funder

Science Foundation Ireland
European Regional Development Fund

Publisher

Association for Computing Machinery (ACM)

Rights

Attribution 4.0 International (CC BY 4.0)

cb

Collections

Data Science Institute (Demonstration Papers)