The Big Data for WSN Nodes: Leveraging Scalable Architecture
Certain applications requires a scalable cost effective storage and execution system with facility to store data and have feature to analyze data to its finest granularity level in future. This increase the quality and accuracy of result analysis. Wireless sensor Network (WSN) nodes deployed for cer...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2023/07/itmconf_icaect2023_02006.pdf |
| _version_ | 1797343932514828288 |
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| author | Shinghal Deepti Shinghal Kshitij Saxena Amit Saxena Shuchita Misra Rajul |
| author_facet | Shinghal Deepti Shinghal Kshitij Saxena Amit Saxena Shuchita Misra Rajul |
| author_sort | Shinghal Deepti |
| collection | DOAJ |
| description | Certain applications requires a scalable cost effective storage and execution system with facility to store data and have feature to analyze data to its finest granularity level in future. This increase the quality and accuracy of result analysis. Wireless sensor Network (WSN) nodes deployed for certain data intensive applications such as surveillance, war zone monitoring etc. generates a massive amount of raw data. There is an essential requirement of storing this data in its native format for analytics purpose in anticipation of future requirements. In present work, a data lake implemented on Amazon AWS is presented for storage of data in original version for future reference. Data Lake implementation service is utilized for storing the data generated in big volumes, high speed and in variety. The data in Data Lake is stored in three zones i.e. raw, reformed and curated. This paper proposes an efficient method of storing structured, unstructured and semi-structured, data in to Data Lake for future retrieval and analytics purpose. The results are comprehensively presented highlighting the advantages of using Data Lake in place of data warehouses. |
| first_indexed | 2024-03-08T10:54:58Z |
| format | Article |
| id | doaj.art-182503d89e494e1298e8e4661b32f904 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-08T10:54:58Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-182503d89e494e1298e8e4661b32f9042024-01-26T16:34:27ZengEDP SciencesITM Web of Conferences2271-20972023-01-01570200610.1051/itmconf/20235702006itmconf_icaect2023_02006The Big Data for WSN Nodes: Leveraging Scalable ArchitectureShinghal Deepti0Shinghal Kshitij1Saxena Amit2Saxena Shuchita3Misra Rajul4Regular Member, International Association for Engineering & TechnologyDept. of E&C Engg., Moradabad Institute of TechnologyDept. of E&C Engg., Moradabad Institute of TechnologyDept. of E&C Engg., Moradabad Institute of TechnologyDept. of Electrical Engg., Moradabad Institute of TechnologyCertain applications requires a scalable cost effective storage and execution system with facility to store data and have feature to analyze data to its finest granularity level in future. This increase the quality and accuracy of result analysis. Wireless sensor Network (WSN) nodes deployed for certain data intensive applications such as surveillance, war zone monitoring etc. generates a massive amount of raw data. There is an essential requirement of storing this data in its native format for analytics purpose in anticipation of future requirements. In present work, a data lake implemented on Amazon AWS is presented for storage of data in original version for future reference. Data Lake implementation service is utilized for storing the data generated in big volumes, high speed and in variety. The data in Data Lake is stored in three zones i.e. raw, reformed and curated. This paper proposes an efficient method of storing structured, unstructured and semi-structured, data in to Data Lake for future retrieval and analytics purpose. The results are comprehensively presented highlighting the advantages of using Data Lake in place of data warehouses.https://www.itm-conferences.org/articles/itmconf/pdf/2023/07/itmconf_icaect2023_02006.pdfbig datadata lakescalable architecturewsn nodes |
| spellingShingle | Shinghal Deepti Shinghal Kshitij Saxena Amit Saxena Shuchita Misra Rajul The Big Data for WSN Nodes: Leveraging Scalable Architecture ITM Web of Conferences big data data lake scalable architecture wsn nodes |
| title | The Big Data for WSN Nodes: Leveraging Scalable Architecture |
| title_full | The Big Data for WSN Nodes: Leveraging Scalable Architecture |
| title_fullStr | The Big Data for WSN Nodes: Leveraging Scalable Architecture |
| title_full_unstemmed | The Big Data for WSN Nodes: Leveraging Scalable Architecture |
| title_short | The Big Data for WSN Nodes: Leveraging Scalable Architecture |
| title_sort | big data for wsn nodes leveraging scalable architecture |
| topic | big data data lake scalable architecture wsn nodes |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2023/07/itmconf_icaect2023_02006.pdf |
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