An enhanced inference algorithm for data sampling efficiency and accuracy using periodic beacons and optimization

Kang, James Jin, Fahd, Kiran and Venkatraman, Sitalakshmi 2019, An enhanced inference algorithm for data sampling efficiency and accuracy using periodic beacons and optimization, Big data and cognitive computing, vol. 3, no. 1, pp. 1-13, doi: 10.3390/bdcc3010007.

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Title An enhanced inference algorithm for data sampling efficiency and accuracy using periodic beacons and optimization
Author(s) Kang, James JinORCID iD for Kang, James Jin orcid.org/0000-0002-0242-4187
Fahd, Kiran
Venkatraman, Sitalakshmi
Journal name Big data and cognitive computing
Volume number 3
Issue number 1
Article ID 7
Start page 1
End page 13
Total pages 13
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2019
ISSN 2504-2289
2504-2289
Keyword(s) data accuracy
data optimization
data inferencing
inference algorithm
beacon
data sampling
Summary Transferring data from a sensor or monitoring device in electronic health, vehicular informatics, or Internet of Things (IoT) networks has had the enduring challenge of improving data accuracy with relative efficiency. Previous works have proposed the use of an inference system at the sensor device to minimize the data transfer frequency as well as the size of data to save network usage and battery resources. This has been implemented using various algorithms in sampling and inference, with a tradeoff between accuracy and efficiency. This paper proposes to enhance the accuracy without compromising efficiency by introducing new algorithms in sampling through a hybrid inference method. The experimental results show that accuracy can be significantly improved, whilst the efficiency is not diminished. These algorithms will contribute to saving operation and maintenance costs in data sampling, where resources of computational and battery are constrained and limited, such as in wireless personal area networks emerged with IoT networks.
Language eng
DOI 10.3390/bdcc3010007
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, the authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30118059

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