Online extreme learning on fixed-point sensor networks

H.H.W.J. Bosman, A. Liotta, G. Iacca, H.J. Wörtche

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

19 Citations (Scopus)
3 Downloads (Pure)


Anomaly detection is a key factor in the processing of large amounts of sensor data from Wireless Sensor Networks (WSN). Efficient anomaly detection algorithms can be devised performing online node-local computations and reducing communication overhead, thus improving the use of the limited hardware resources. This work introduces a fixed-point embedded implementation of Online Sequential Extreme Learning Machine (OS-ELM), an online learning algorithm for Single Layer Feedforward Neural Networks (SLFN). To overcome the stability issues introduced by the fixed precision, we apply correction mechanisms previously proposed for Recursive Least Squares (RLS). The proposed implementation is tested extensively with generated and real-world datasets, and compared with RLS, Linear Least Squares Estimation, and a rule-based method as benchmarks. The methods are evaluated on the prediction accuracy and on the detection of anomalies. The experimental results demonstrate that fixed-point OS-ELM can be successfully implemented on resource-limited embedded systems, with guarantees of numerical stability. Furthermore, the detection accuracy of fixed-point OS-ELM shows better generalization properties in comparison with, for instance, fixed-point RLS.
Original languageEnglish
Title of host publicationIEEE International Conference on Data Mining (ICDM 2013), 7-10 December 2013, Dallas. Texas
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-0-7695-5109-8
Publication statusPublished - 2013
Eventconference; International Conference on Data Mining ICDM 2013; 2013-12-07; 2013-12-10 -
Duration: 7 Dec 201310 Dec 2013


Conferenceconference; International Conference on Data Mining ICDM 2013; 2013-12-07; 2013-12-10
OtherInternational Conference on Data Mining ICDM 2013


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