Abstract

Interpreting sensor data in Internet-of-Things applications is a challenging problem particularly in embedded systems. We consider sensor data analytics where machine learning algorithms can be fully implemented on an embedded processor/sensor board. We develop an efficient real-time realization of a Gaussian mixture model (GMM) for execution on the NXP FRDM-K64F embedded sensor board. We demonstrate the design of a customized program and data structure that generates real-time sensor features, and we show details and training/classification results for select IoT applications. The integrated hardware/software system enables real-time data analytics and continuous training and re-training of the machine learning (ML) algorithm. The real-time ML platform can accommodate several applications with lower sensor data traffic.

Original languageEnglish (US)
Title of host publication2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages290-294
Number of pages5
ISBN (Electronic)9781509058440
DOIs
StatePublished - Mar 23 2017
Event2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016 - Limassol, Cyprus
Duration: Dec 12 2016Dec 14 2016

Other

Other2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016
CountryCyprus
CityLimassol
Period12/12/1612/14/16

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Keywords

  • condition monitoring
  • embedded machine learning
  • Internet-of-Things
  • sensor data analytics

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing

Cite this

Lee, J., Stanley, M., Spanias, A., & Tepedelenlioglu, C. (2017). Integrating machine learning in embedded sensor systems for Internet-of-Things applications. In 2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016 (pp. 290-294). [7886051] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISSPIT.2016.7886051