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) |
|---|---|
| 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 |
|---|---|
| Country | Cyprus |
| City | Limassol |
| Period | 12/12/16 → 12/14/16 |
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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
Integrating machine learning in embedded sensor systems for Internet-of-Things applications. / Lee, Jongmin; Stanley, Michael; Spanias, Andreas; Tepedelenlioglu, Cihan.
2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 290-294 7886051.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Integrating machine learning in embedded sensor systems for Internet-of-Things applications
AU - Lee, Jongmin
AU - Stanley, Michael
AU - Spanias, Andreas
AU - Tepedelenlioglu, Cihan
PY - 2017/3/23
Y1 - 2017/3/23
N2 - 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.
AB - 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.
KW - condition monitoring
KW - embedded machine learning
KW - Internet-of-Things
KW - sensor data analytics
UR - http://www.scopus.com/inward/record.url?scp=85006702130&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006702130&partnerID=8YFLogxK
U2 - 10.1109/ISSPIT.2016.7886051
DO - 10.1109/ISSPIT.2016.7886051
M3 - Conference contribution
SP - 290
EP - 294
BT - 2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -