Missing Data Recovery for Downstream Learning in Low-Cost Environmental Sensor Networks

Benjamin Agbo, Hussain Al-Aqrabi, Tariq Alsboui, Muhammad Hussain, Richard Hill

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The development of low-cost sensor networks (LCSNs) has facilitated the generation of vast amounts of environmental monitoring data in air quality monitoring stations. State-of-the-art research has focused on developing robust models to detect and mitigate events which may impact societies. Making adequate predictions is important for meeting the sustainability needs of future generations. Therefore, quality data is required to produce accurate results for downstream analytics and visualisations. When dealing with large amounts of data, it is however common to observe numerous amounts of missing data. This is a challenge for data miners because various methods for data analysis only work well on complete datasets. A traditional approach to handling missing data is to discard instances of missing values and only use complete cases for analysis. However, research has shown that this approach is not practical especially when large amounts of data are missing. This led to an increased need to develop strategies for replacing missing values with plausible values through imputation. This study presents an imputation strategy called k-BFMVI for recovering missing values before training downstream regression models for low cost sensor calibration. Experiments simulated missingness from 10% to 40% using MCAR and MAR mechanisms and the performance of the proposed technique was measured against state-of-the-art techniques. Overall, the proposed algorithm recorded the best imputation accuracy as opposed to existing techniques and showed significant improvements on downstream learning.

Original languageEnglish
Title of host publication2023 9th International Conference on Information Technology Trends
Subtitle of host publicationITT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350327502
ISBN (Print)9798350327519
Publication statusPublished - 24 Jul 2023
Event9th International IEEE Conference in Information Technology Trends 2023: The Application of AI in Sustainable Computing - HCT-Dubai Men’s campus, Dubai, United Arab Emirates
Duration: 24 May 202325 May 2023
Conference number: 9


Conference9th International IEEE Conference in Information Technology Trends 2023
Abbreviated titleITT 2023
Country/TerritoryUnited Arab Emirates
Internet address

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