OTA-TinyML: Over the Air Deployment of TinyML Models and Execution on IoT Devices

• Published in: IEEE internet computing Vol. 26; no. 3; pp. 69 - 78
• Main Authors: Sudharsan, Bharath, Breslin, John G., Tahir, Mehreen, Intizar Ali, Muhammad, Rana, Omer, Dustdar, Schahram, Ranjan, Rajiv
• Format: Journal Article
• Language: English
• Published: IEEE 01.05.2022
• Subjects: Atmospheric modeling; Internet of Things; Machine learning; Performance evaluation; Task analysis; Visualization; Web servers
• DOI: 10.1109/MIC.2021.3133552
• ISSN: 1089-7801

This article presents a novel over-the-air (OTA) technique to remotely deploy tiny ML models over Internet of Things (IoT) devices and perform tasks, such as machine learning (ML) model updates, firmware reflashing, reconfiguration, or repurposing. We discuss relevant challenges for OTA ML deployment over IoT both at the scientific and engineering level. We propose OTA-TinyML to enable resource-constrained IoT devices to perform end-to-end fetching, storage, and execution of many TinyML models. OTA-TinyML loads the C source file of ML models from a web server into the embedded IoT devices via HTTPS. OTA-TinyML is tested by performing remote fetching of six types of ML models, storing them on four types of memory units, then loading and executing on seven popular MCU boards.