The Use of Open-Source Boards for Data Collection and Machine Learning in Remote Deployments

Gabriel Kiarie, Jason Kabi, Lorna Mugambi, Ciira wa Maina

Keywords: Open-source hardware, single board computer, microcontroller, on-board processing, edge computing, field programmable gate array

Abstract: Machine learning is being adopted in many walks of life to solve various problems. This is being driven by development of robust machine learning algorithms, availability of large datasets and low cost computation resources. Some machine learning applications require deployment of devices off-the-grid for data collection and real time monitoring. Such applications require development of systems that can operate autonomously during their deployment. Advancement in technology has seen development of low-cost and low-power open-source microcontrollers and single board computers. These boards can be interfaced with a wide array of sensors and can perform computation processes. The boards are finding wide applications in data collection and machine learning initiatives. This paper will describe how the boards are leveraged for off-grid deployments.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: Infrastructure (eg, datasets, competitions, implementations, libraries)

TL;DR: This paper describes how open source hardware are used for data collection and machine learning tasks in off-grid setups.