Integrating machine learning in embedded sensor systems for Internet-of-Things applications

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...

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Bibliographic Details
Published in2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) pp. 290 - 294
Main Authors Lee, Jongmin, Stanley, Michael, Spanias, Andreas, Tepedelenlioglu, Cihan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
Classification algorithms
Clustering algorithms
condition monitoring
Data analysis
embedded machine learning
Embedded systems
Internet of Things
Machine learning
Machine learning algorithms
Real-time systems
sensor data analytics
Sensor systems
Signal processing algorithms
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DOI10.1109/ISSPIT.2016.7886051

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Summary: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.
DOI:10.1109/ISSPIT.2016.7886051