Data Quality Assessment and Enhancement for Sustainable Water Management in IOT-Enabled Peacekeeping Missions
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| Publicat a: | PQDT - Global (2025) |
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| 100 | 1 | |a Jolaiya, Emmanuel Ayodele | |
| 245 | 1 | |a Data Quality Assessment and Enhancement for Sustainable Water Management in IOT-Enabled Peacekeeping Missions | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a Water is a critical resource for sustaining life, and its efficient management is essential for global sustainability. In United Nations peacekeeping missions, where infrastructure monitoring is crucial for operational efficiency, IoT-based water consumption monitoring has become an indispensable tool. However, challenges such as data gaps, sensor failures, and anomalies compromise the reliability of IIoTgenerated data, ultimately affecting decision-making. This study assesses the data quality (DQ) of IoT-surveyed water consumption data in the Abyei camp and enhances data reliability through expert-driven reconstruction and a systematic evaluation of imputation techniques.To achieve this, a Data Quality Score (DQS) framework was developed, incorporating four key dimensions: validity, accuracy, completeness, and timeliness. A comprehensive assessment of IoT-based water consumption data revealed that while most devices reported high-quality data, several exhibited systematic challenges, including device resets, outliers, and irregular reporting patterns. Based on these findings, business-rule-driven trend reconstruction was applied alongside a suite of imputation techniques, categorized into univariate (Forward Fill, Backward Fill, Mean, Median, Linear, and Cubic Interpolation) and multivariate methods (KNearest Neighbors (KNN) and Multiple Linear Regression (MLR)) to reconstruct missing values and correct anomalies.The results demonstrated that trend reconstruction significantly reduced anomalies, improving overall DQ. Additionally, all imputation methods contributed to positive improvements, with Linear Interpolation (LI) consistently yielding the highest improvement in DQS, followed closely by KNN imputation. These findings emphasize the importance of structured DQ assessment and device-specific imputation strategies for ensuring reliable water consumption monitoring in IoT-enabled peacekeeping missions. | |
| 653 | |a Industrial Internet of Things | ||
| 653 | |a Sustainable development | ||
| 653 | |a Decision making | ||
| 653 | |a Computer science | ||
| 653 | |a Information technology | ||
| 653 | |a Sustainability | ||
| 653 | |a Water resources management | ||
| 773 | 0 | |t PQDT - Global |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3224571167/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3224571167/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |