Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture

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dc.contributor.authorRIBEIRO JUNIOR, F. M.
dc.contributor.authorReinaldo Bianchi
dc.contributor.authorPRATI, R. C.
dc.contributor.authorKOLEHMAINEN, K.
dc.contributor.authorSOININEN, J-P.
dc.contributor.authorKAMIENSKI, C. A.
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-9097-827X
dc.date.accessioned2022-03-01T06:05:01Z
dc.date.available2022-03-01T06:05:01Z
dc.date.issued2022-01-25
dc.description.abstractSmart agriculture applications that analyse and manage agricultural yield using IoT systems may suffer from intermittent operation due to cloud disconnections commonly occurring in rural areas. A fog computing solution enables the IoT system to process data faster and deal with intermittent connectivity. However, the fog needs to send a high volume of data to the cloud and this can cause link congestion with unusable data traffic. Here we propose an approach to collect and store data in a fog-based smart agriculture environment and different data reduction methods. Sixteen techniques for data reduction are investigated; eight machine learning (ML) methods combined with run-length encoding, and eight combined with Huffman encoding. Our experiment uses two real data sets, where the first contains air temperature and humidity values, and the second has soil moisture and temperature conditions. The fog filters cluster the unlabelled data using unsupervised machine learning algorithms that group data into categories according to their value ranges in all experiments. Supervised learning classification methods are also used to predict the class of data samples from these categories. After that, the fog filter compresses the identified categories using two data compression techniques, run-length encoding (RLE) and the Huffman encoding, preserving the data time series nature. Our results reveal that a k-means combined with RLE method achieved the highest reduction, where the fog needed to store and transmit only 3%–6% of the original data generated by sensors.
dc.identifier.citationRIBEIRO JUNIOR, F. M.; BIANCHI, R.; PRATI, R. C.; KOLEHMAINEN, K.; SOININEN, J-P.; KAMIENSKI, C. A. Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture. Biosystems Engineering, 2022.
dc.identifier.doi10.1016/j.biosystemseng.2021.12.021
dc.identifier.issn1537-5110
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/4424
dc.relation.ispartofBiosystems Engineering
dc.rightsAcesso Restrito
dc.subject.otherlanguageData reduction
dc.subject.otherlanguageInternet of Things (IoT)
dc.subject.otherlanguageMachine learning (ML)
dc.subject.otherlanguageSmart agriculture
dc.titleData reduction based on machine learning algorithms for fog computing in IoT smart agriculture
dc.typeArtigo
fei.scopus.citations27
fei.scopus.eid2-s2.0-85123707420
fei.scopus.subjectAgricultural yields
fei.scopus.subjectAgriculture applications
fei.scopus.subjectComputing solutions
fei.scopus.subjectHuffman encoding
fei.scopus.subjectIntermittent operation
fei.scopus.subjectInternet of thing
fei.scopus.subjectMachine learning
fei.scopus.subjectMachine learning algorithms
fei.scopus.subjectRun-length encoding
fei.scopus.subjectSmart agricultures
fei.scopus.updated2024-07-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85123707420&origin=inward
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