DOI : https://doi.org/10.18517/ijaseit.14.5.11492

Multidimensional Indicator for Data Quality Assessment in Wireless Sensor Networks: Challenges and Opportunities

Nurul Aqilah Zamri (1), M. Izham Jaya (2), Siti Salwani Yaakob (3), Hidra Amnur (4), Shahreen Kasim (5)
(1) Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia
(2) Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia
(3) Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia
(4) Department of Information Technology, Politeknik Negeri Padang, Padang, Indonesia
(5) Soft Computing and Data Mining Centre (SMC), Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
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N. A. Zamri, M. I. Jaya, S. S. Yaakob, H. Amnur, and S. Kasim, “Multidimensional Indicator for Data Quality Assessment in Wireless Sensor Networks: Challenges and Opportunities”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 14, no. 5, pp. 1663–1672, Oct. 2024.
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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.

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