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Missing Data Imputation with Bayesian Maximum Entropy for Internet of Things Applications

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dc.contributor.authorGonzález Vidal, Aurora
dc.contributor.authorRathore, Punit
dc.contributor.authorRao, Aravinda S.
dc.contributor.authorMarimuthu Palaniswami
dc.contributor.authorSkarmeta Gómez, Antonio
dc.contributor.authorMendoza Bernal, José
dc.contributor.departmentIngeniería de la Información y las Comunicaciones
dc.contributor.otherFacultades de la UMU::Facultad de Informática
dc.date.accessioned2026-02-04T08:16:11Z
dc.date.available2026-02-04T08:16:11Z
dc.date.copyright© 2021, IEEE
dc.date.issued2021-11-01
dc.description.abstractInternet of Things (IoT) enables the seamless integration of sensors, actuators and communication devices for real-time applications. IoT systems require good quality sensor data in order to make real-time decisions. However, values are often missing from the sensor data collected owing to faulty sensors, a loss of data during communication, interference and measurement errors. Considering the spatiotemporal nature of IoT data and the uncertainty of the data collected by sensors, we propose a new framework with which to impute missing values utilizing Bayesian Maximum Entropy (BME) as a convenient means to estimate the missing data from IoT applications. Missing sensor measurements adversely affect the quality of data, and consequently the performance and outcomes of IoT systems. Our proposed framework incorporates BME in order to impute missing values in diverse IoT scenarios by making use of the combination of low- and high-precision sensors. Our approach can incorporate the measurement errors of low- precision sensors as interval quantities along with the high-precision sensor measurements, making it highly suitable for real-time IoT systems. Our framework is robust to variations in data, requires less execution time, and requires only a single input parameter, thus outperforming existing IoT data imputation methods. The experimental results obtained for three IoT datasets demonstrate the superiority of the BME framework as regards accuracy, running time and robustness. The framework can additionally be extended to distributed IoT nodes for the online imputation of missing values.
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dc.identifier.citationGonzález-Vidal, A., Rathore, P., Rao, A. S., Mendoza-Bernal, J., Palaniswami, M., & Skarmeta-Gómez, A. F. (2020). Missing data imputation with bayesian maximum entropy for internet of things applications. IEEE Internet of Things Journal, 8(21), 16108-16120.
dc.identifier.doihttp://dx.doi.org/10.1109/JIOT.2020.2987979
dc.identifier.eissn2327-4662
dc.identifier.urihttp://hdl.handle.net/10201/199029
dc.languageeng
dc.publisherIEEE Internet of Things Journal
dc.relationThis work has been sponsored by MINECO through the PERSEIDES project (ref. TIN2017-86885-R) and by the European Comission through the H2020 IoTCrawler (contract 779852), and DEMETER (grant agreement 857202) EU Projects and also co-financed by the European Social Fund (ESF) and the Youth European Initiative (YEI) under the Spanish Seneca Foundation (CARM). This work is also supported by Australian Research Council (ARC) Discovery Project (DP190102828).
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9066992
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMissing data
dc.subjectImputation
dc.subjectBayesian Maximum Entropy (BME),
dc.subjectSpatio-temporal analysis
dc.subjectInternet of Things (IoT)
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titleMissing Data Imputation with Bayesian Maximum Entropy for Internet of Things Applications
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublicationcf8009bf-6088-449d-9f79-a516af312945
relation.isAuthorOfPublication7dacd587-416b-43e7-bec1-30dbb093d0a4
relation.isAuthorOfPublication89099ce3-297a-40dc-9833-322bee8d2fbd
relation.isAuthorOfPublication.latestForDiscoverycf8009bf-6088-449d-9f79-a516af312945
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