Data cleaning method in wireless sensor-based on intelligence technology

Simple item page
dc.citation.epage10
dc.citation.issue2
dc.citation.journalTitleВимірювальна техніка та метрологія
dc.citation.spage5
dc.citation.volume83
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorDiachok, Roman
dc.contributor.authorKlym, Halyna
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2023-05-09T09:19:18Z
dc.date.available2023-05-09T09:19:18Z
dc.date.created2022-02-28
dc.date.issued2022-02-28
dc.description.abstractThe method of cleaning management data in wireless sensor networks based on intelligence technology has been studied. Specific forms of application of wireless sensor networks are analyzed. The characteristics of the structure of wireless sensor networks are presented and the data cleaning technology based on the clustering model is offered. An algorithm for deleting a cluster-based replication record is proposed and the accuracy of data cleaning methods is tested. The obtained results testify to the efficiency of using the studied method.
dc.format.extent5-10
dc.format.pages6
dc.identifier.citationDiachok R. Data cleaning method in wireless sensor-based on intelligence technology / Roman Diachok, Halyna Klym // Measuring equipment and metrology. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 83. — No 2. — P. 5–10.
dc.identifier.citationenDiachok R. Data cleaning method in wireless sensor-based on intelligence technology / Roman Diachok, Halyna Klym // Measuring equipment and metrology. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 83. — No 2. — P. 5–10.
dc.identifier.doidoi.org/10.23939/istcmtm2022.02.005
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/59057
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofВимірювальна техніка та метрологія, 2 (83), 2022
dc.relation.ispartofMeasuring equipment and metrology, 2 (83), 2022
dc.relation.references[1] L. Zhu, M. Li, Z. Zhang, et al., Big data mining of users’ energy consumption patterns in the wireless smart grid [J]. IEEE Wirel. Commun. 25(1), 84–89 (2018), DOI: 10.1109/MWC.2018.1700157, https://ieeexplore.ieee.org/document/8304397
dc.relation.references[2] W. Sun, L. Zhang, Y. Zhang, et al., Enhanced works of separation for (00 01)ZnO|(111)ZrO2 interfaces via ion-doping in ZnO: Data-mining and density functional theory study [J]. Comput. Mater. Sci. 142, 410–416 (2018), DOI: 10.1016/j.commatsci.2017.10.044 https: //www.sciencedirect.com/science/article/abs/pii/S0927025617306080
dc.relation.references[3] S. Shafiee, S. Minaei, Combined data mining/NIR spectroscopy for purity assessment of lime juice [J]. Infrared Phys. Technol. 91, 193–199 (2018), DOI: 10.1016/j.infrared. 2018.04.012 https://www.sciencedirect.com/science/article/abs/pii/S135044951730662X
dc.relation.references[4] D.W. Upton, B.I. Saeed, P.J. Mather, et al., Wireless sensor network for radiometric detection and assessment of partial discharge in high-voltage equipment [J]. Radio Sci. 53(3), 357–364 (2018), DOI: 10.1002/2017RS006507 https://ieeexplore.ieee.org/abstract/document/8679774
dc.relation.references[5] P.V. Mekikis, E. Kartsakli, A. Antonopoulos, et al., Connectivity analysis in clustered wireless sensor networks powered by solar energy [J]. IEEE Trans. Wirel. Commun. 17(4), 2389–2401 (2018). DOI: 10.1109/TWC.2018.2794963 https://ieeexplore.ieee.org/abstract/document/8267240
dc.relation.references[6] D. Aygör, S.U. Rehman, F.V. Çelebİ. Impact of buffer management solutions on MAC Layer Performance in Wireless Sensor Networks. IEICE Transac. Commun. E101.B(9), 2058–2068 (2018), DOI: 10.1587/transcom. 2017EBP3389 https://www.jstage.jst.go.jp/article/transcom/advpub/0/advpub_2017EBP3389/_article/-char/ja/
dc.relation.references[7] A. Alomari, F. Comeau, W. Phillips, et al., New path planning model for mobile anchor-assisted localization in wireless sensor networks [J]. Wirel. Netw 8, 1–19 (2018), DOI:10.1088/1742-6596/1176/2/022003 https://link.springer.com/article/10.1007/s11276-017-1493-2
dc.relation.references[8] L. Kumar, V. Sharma, A. Singh, Cluster-based single-sink wireless sensor networks and passive optical network converged network incorporating sideband modulation schemes [J]. Opt. Eng. 57(2), 1 (2018). DOI: 10.1117/1.OE.57.2.026102 https://www.spiedigitallibrary.org/journals/opticalengineering/volume-57/issue-2/026102/Cluster-based-singlesink-wireless-sensor-networks-and-passiveoptical/10.1117/1.OE.57.2.026102.full
dc.relation.references[9] W.K. Lee, M.J.W. Schubert, B.Y. Ooi, et al., Multi-source energy harvesting and storage for floating wireless sensor network nodes with long range communication capability [J]. IEEE Trans. Ind. Appl. 54(3), 2606–2615 (2018) DOI: 10.1109/TIA.2018.2799158 https://ieeexplore.ieee.org/document/8272444
dc.relation.references[10] W. Zhang, J. Yang, Y. Fang, et al., Analytical fuzzy approach to biological data analysis [J]. Saudi J. Biol. Sci. 24(3), 563–573 (2017). DOI: https://doi.org/10.1016/j.sjbs.2017.01.027 https://www.sciencedirect.com/science/article/pii/S1319562X17300360?via%3Dihub
dc.relation.referencesen[1] L. Zhu, M. Li, Z. Zhang, et al., Big data mining of users’ energy consumption patterns in the wireless smart grid [J]. IEEE Wirel. Commun. 25(1), 84–89 (2018), DOI: 10.1109/MWC.2018.1700157, https://ieeexplore.ieee.org/document/8304397
dc.relation.referencesen[2] W. Sun, L. Zhang, Y. Zhang, et al., Enhanced works of separation for (00 01)ZnO|(111)ZrO2 interfaces via ion-doping in ZnO: Data-mining and density functional theory study [J]. Comput. Mater. Sci. 142, 410–416 (2018), DOI: 10.1016/j.commatsci.2017.10.044 https: //www.sciencedirect.com/science/article/abs/pii/S0927025617306080
dc.relation.referencesen[3] S. Shafiee, S. Minaei, Combined data mining/NIR spectroscopy for purity assessment of lime juice [J]. Infrared Phys. Technol. 91, 193–199 (2018), DOI: 10.1016/j.infrared. 2018.04.012 https://www.sciencedirect.com/science/article/abs/pii/S135044951730662X
dc.relation.referencesen[4] D.W. Upton, B.I. Saeed, P.J. Mather, et al., Wireless sensor network for radiometric detection and assessment of partial discharge in high-voltage equipment [J]. Radio Sci. 53(3), 357–364 (2018), DOI: 10.1002/2017RS006507 https://ieeexplore.ieee.org/abstract/document/8679774
dc.relation.referencesen[5] P.V. Mekikis, E. Kartsakli, A. Antonopoulos, et al., Connectivity analysis in clustered wireless sensor networks powered by solar energy [J]. IEEE Trans. Wirel. Commun. 17(4), 2389–2401 (2018). DOI: 10.1109/TWC.2018.2794963 https://ieeexplore.ieee.org/abstract/document/8267240
dc.relation.referencesen[6] D. Aygör, S.U. Rehman, F.V. Çelebİ. Impact of buffer management solutions on MAC Layer Performance in Wireless Sensor Networks. IEICE Transac. Commun. E101.B(9), 2058–2068 (2018), DOI: 10.1587/transcom. 2017EBP3389 https://www.jstage.jst.go.jp/article/transcom/advpub/0/advpub_2017EBP3389/_article/-char/ja/
dc.relation.referencesen[7] A. Alomari, F. Comeau, W. Phillips, et al., New path planning model for mobile anchor-assisted localization in wireless sensor networks [J]. Wirel. Netw 8, 1–19 (2018), DOI:10.1088/1742-6596/1176/2/022003 https://link.springer.com/article/10.1007/s11276-017-1493-2
dc.relation.referencesen[8] L. Kumar, V. Sharma, A. Singh, Cluster-based single-sink wireless sensor networks and passive optical network converged network incorporating sideband modulation schemes [J]. Opt. Eng. 57(2), 1 (2018). DOI: 10.1117/1.OE.57.2.026102 https://www.spiedigitallibrary.org/journals/opticalengineering/volume-57/issue-2/026102/Cluster-based-singlesink-wireless-sensor-networks-and-passiveoptical/10.1117/1.OE.57.2.026102.full
dc.relation.referencesen[9] W.K. Lee, M.J.W. Schubert, B.Y. Ooi, et al., Multi-source energy harvesting and storage for floating wireless sensor network nodes with long range communication capability [J]. IEEE Trans. Ind. Appl. 54(3), 2606–2615 (2018) DOI: 10.1109/TIA.2018.2799158 https://ieeexplore.ieee.org/document/8272444
dc.relation.referencesen[10] W. Zhang, J. Yang, Y. Fang, et al., Analytical fuzzy approach to biological data analysis [J]. Saudi J. Biol. Sci. 24(3), 563–573 (2017). DOI: https://doi.org/10.1016/j.sjbs.2017.01.027 https://www.sciencedirect.com/science/article/pii/S1319562X17300360?via%3Dihub
dc.relation.urihttps://ieeexplore.ieee.org/document/8304397
dc.relation.urihttps://www.sciencedirect.com/science/article/abs/pii/S135044951730662X
dc.relation.urihttps://ieeexplore.ieee.org/abstract/document/8679774
dc.relation.urihttps://ieeexplore.ieee.org/abstract/document/8267240
dc.relation.urihttps://www.jstage.jst.go.jp/article/transcom/advpub/0/advpub_2017EBP3389/_article/-char/ja/
dc.relation.urihttps://link.springer.com/article/10.1007/s11276-017-1493-2
dc.relation.urihttps://www.spiedigitallibrary.org/journals/opticalengineering/volume-57/issue-2/026102/Cluster-based-singlesink-wireless-sensor-networks-and-passiveoptical/10.1117/1.OE.57.2.026102.full
dc.relation.urihttps://ieeexplore.ieee.org/document/8272444
dc.relation.urihttps://doi.org/10.1016/j.sjbs.2017.01.027
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S1319562X17300360?via%3Dihub
dc.rights.holder© Національний університет “Львівська політехніка”, 2022
dc.subjectWireless sensor
dc.subjectNetwork control
dc.subjectDatabase cleaning
dc.titleData cleaning method in wireless sensor-based on intelligence technology
dc.typeArticle

Files

Original bundle

Now showing 1 - 2 of 2
Thumbnail Image
Name:
2022v83n2_Diachok_R-Data_cleaning_method_in_wireless_5-10.pdf
Size:
419.54 KB
Format:
Adobe Portable Document Format
Download
Thumbnail Image
Name:
2022v83n2_Diachok_R-Data_cleaning_method_in_wireless_5-10__COVER.png
Size:
542.5 KB
Format:
Portable Network Graphics
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.75 KB
Format:
Plain Text
Description:
Download

Collections

Вимірювальна техніка та метрологія. – 2022. – Випуск 83, №2