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 language | English (US) |
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Title of host publication | 2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 290-294 |
Number of pages | 5 |
ISBN (Electronic) | 9781509058440 |
DOIs | |
State | Published - Mar 23 2017 |
Event | 2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016 - Limassol, Cyprus Duration: Dec 12 2016 → Dec 14 2016 |
Other
Other | 2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016 |
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Country | Cyprus |
City | Limassol |
Period | 12/12/16 → 12/14/16 |
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