Multidimensional indicator for data quality assessment in wireless sensor networks: challenges and opportunities
Wireless Sensor Networks (WSN) are equipped with numerous sensors that generate vast quantities of data, essential for operational efficiency and informed decision-making. However, the value of this data is contingent upon its suitability for the specific applications it serves. A significant challe...
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| Format: | Article |
| Language: | English |
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Indonesian Society for Knowledge and Human Development
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42888/ http://umpir.ump.edu.my/id/eprint/42888/1/Multidimensional%20Indicator%20for%20Data%20Quality%20Assessment%20in%20Wireless%20Sensor%20Networks%20Challenges%20and%20Opportunities.pdf |
| _version_ | 1848826727555399680 |
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| author | Nurul Aqilah, Zamri Mohd Izham, Mohd Jaya Siti Salwani, Yaakob Amnur, Hidra Shahreen, Kasim |
| author_facet | Nurul Aqilah, Zamri Mohd Izham, Mohd Jaya Siti Salwani, Yaakob Amnur, Hidra Shahreen, Kasim |
| author_sort | Nurul Aqilah, Zamri |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Wireless Sensor Networks (WSN) are equipped with numerous sensors that generate vast quantities of data, essential for operational efficiency and informed decision-making. However, the value of this data is contingent upon its suitability for the specific applications it serves. A significant challenge in WSNs is the selection of appropriate data quality dimensions and metrics necessary to construct robust Data Quality Indicators (DQI) and comprehensively assess data quality in various contexts. This systematic literature review seeks to identify the key data quality dimensions and the corresponding measurement metrics within WSNs, while exploring the use of multi-dimensional data quality criteria in developing DQI. A thorough search of SCOPUS and Web of Science databases yielded 475 potential research articles, from which 64 primary studies were selected for in-depth analysis. The findings highlight four key data quality dimensions in WSN: accuracy, timeliness, completeness, and consistency. However, choosing measurement metrics for each dimension requires an in-depth understanding of the data's context. Various approaches for obtaining DQI in WSN research were identified, including weighted linear average models and application-specific contextual information. Effective DQI incorporates weights to each dimension, reflecting the priorities of specific data users, and leverages contextual information pertinent to the sensors’ data. It is crucial to evaluate whether the data collected by WSNs meets established quality standards, a key aspect of WSN operation. These insights will aid in developing more robust and reliable WSNs, ensuring the provision of high-quality data essential for effective operation and decision-making. |
| first_indexed | 2025-11-15T03:49:25Z |
| format | Article |
| id | ump-42888 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:49:25Z |
| publishDate | 2024 |
| publisher | Indonesian Society for Knowledge and Human Development |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-428882024-11-07T00:04:10Z http://umpir.ump.edu.my/id/eprint/42888/ Multidimensional indicator for data quality assessment in wireless sensor networks: challenges and opportunities Nurul Aqilah, Zamri Mohd Izham, Mohd Jaya Siti Salwani, Yaakob Amnur, Hidra Shahreen, Kasim QA75 Electronic computers. Computer science Wireless Sensor Networks (WSN) are equipped with numerous sensors that generate vast quantities of data, essential for operational efficiency and informed decision-making. However, the value of this data is contingent upon its suitability for the specific applications it serves. A significant challenge in WSNs is the selection of appropriate data quality dimensions and metrics necessary to construct robust Data Quality Indicators (DQI) and comprehensively assess data quality in various contexts. This systematic literature review seeks to identify the key data quality dimensions and the corresponding measurement metrics within WSNs, while exploring the use of multi-dimensional data quality criteria in developing DQI. A thorough search of SCOPUS and Web of Science databases yielded 475 potential research articles, from which 64 primary studies were selected for in-depth analysis. The findings highlight four key data quality dimensions in WSN: accuracy, timeliness, completeness, and consistency. However, choosing measurement metrics for each dimension requires an in-depth understanding of the data's context. Various approaches for obtaining DQI in WSN research were identified, including weighted linear average models and application-specific contextual information. Effective DQI incorporates weights to each dimension, reflecting the priorities of specific data users, and leverages contextual information pertinent to the sensors’ data. It is crucial to evaluate whether the data collected by WSNs meets established quality standards, a key aspect of WSN operation. These insights will aid in developing more robust and reliable WSNs, ensuring the provision of high-quality data essential for effective operation and decision-making. Indonesian Society for Knowledge and Human Development 2024-10 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/42888/1/Multidimensional%20Indicator%20for%20Data%20Quality%20Assessment%20in%20Wireless%20Sensor%20Networks%20Challenges%20and%20Opportunities.pdf Nurul Aqilah, Zamri and Mohd Izham, Mohd Jaya and Siti Salwani, Yaakob and Amnur, Hidra and Shahreen, Kasim (2024) Multidimensional indicator for data quality assessment in wireless sensor networks: challenges and opportunities. International Journal on Advanced Science, Engineering and Information Technology, 14 (5). pp. 1663-1672. ISSN 2088-5334. (Published) https://doi.org/10.18517/ijaseit.14.5.11492 10.18517/ijaseit.14.5.11492 |
| spellingShingle | QA75 Electronic computers. Computer science Nurul Aqilah, Zamri Mohd Izham, Mohd Jaya Siti Salwani, Yaakob Amnur, Hidra Shahreen, Kasim Multidimensional indicator for data quality assessment in wireless sensor networks: challenges and opportunities |
| title | Multidimensional indicator for data quality assessment in wireless sensor networks: challenges and opportunities |
| title_full | Multidimensional indicator for data quality assessment in wireless sensor networks: challenges and opportunities |
| title_fullStr | Multidimensional indicator for data quality assessment in wireless sensor networks: challenges and opportunities |
| title_full_unstemmed | Multidimensional indicator for data quality assessment in wireless sensor networks: challenges and opportunities |
| title_short | Multidimensional indicator for data quality assessment in wireless sensor networks: challenges and opportunities |
| title_sort | multidimensional indicator for data quality assessment in wireless sensor networks: challenges and opportunities |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/42888/ http://umpir.ump.edu.my/id/eprint/42888/ http://umpir.ump.edu.my/id/eprint/42888/ http://umpir.ump.edu.my/id/eprint/42888/1/Multidimensional%20Indicator%20for%20Data%20Quality%20Assessment%20in%20Wireless%20Sensor%20Networks%20Challenges%20and%20Opportunities.pdf |