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

González-Vidal, Aurora; Rathore, Punit; Rao, Aravinda S.; Mendoza-Bernal, Jose; Palaniswami, Marimuthu; Skarmeta-Gómez, Antonio F.

doi:10.1109/jiot.2020.2987979

Cited by 30 publications

(17 citation statements)

References 43 publications

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“…In the case of multiple sensors, we employ a knowledge-based Bayesian Maximum Estimation (BME) for imputing an identified faulty value [56]. BME is a mapping method for spatiotemporal estimation that allows various knowledge bases to be incorporated in a logical manner-definite rules for prior information, hard (high precision) and soft (low precision) data into modelling [57].…”

Section: Fault Detection and Fault Recoverymentioning

confidence: 99%

See 1 more Smart Citation

IoTCrawler: Challenges and Solutions for Searching the Internet of Things

Iggena¹,

Ilyas²,

Fischer³

et al. 2021

Sensors

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Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios.

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Section: Fault Detection and Fault Recoverymentioning

confidence: 99%

“…BME is a mapping method for spatiotemporal estimation that allows various knowledge bases to be incorporated in a logical manner-definite rules for prior information, hard (high precision) and soft (low precision) data into modelling [57]. More details about this algorithm can be checked in [56].…”

Section: Fault Detection and Fault Recoverymentioning

confidence: 99%

IoTCrawler: Challenges and Solutions for Searching the Internet of Things

Iggena¹,

Ilyas²,

Fischer³

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [ 31 ], the missing values imputation within sensor-based measurements is performed through the Bayesian maximum entropy (BME) technique. The performance of the BME technique seems to outperform the PMF in terms of accuracy, time efficiency, and robustness.…”

Section: Related Workmentioning

confidence: 99%

Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study

Erhan

Mauro

Anjum

et al. 2021

Sensors

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Recent developments in cloud computing and the Internet of Things have enabled smart environments, in terms of both monitoring and actuation. Unfortunately, this often results in unsustainable cloud-based solutions, whereby, in the interest of simplicity, a wealth of raw (unprocessed) data are pushed from sensor nodes to the cloud. Herein, we advocate the use of machine learning at sensor nodes to perform essential data-cleaning operations, to avoid the transmission of corrupted (often unusable) data to the cloud. Starting from a public pollution dataset, we investigate how two machine learning techniques (kNN and missForest) may be embedded on Raspberry Pi to perform data imputation, without impacting the data collection process. Our experimental results demonstrate the accuracy and computational efficiency of edge-learning methods for filling in missing data values in corrupted data series. We find that kNN and missForest correctly impute up to 40% of randomly distributed missing values, with a density distribution of values that is indistinguishable from the benchmark. We also show a trade-off analysis for the case of bursty missing values, with recoverable blocks of up to 100 samples. Computation times are shorter than sampling periods, allowing for data imputation at the edge in a timely manner.

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“…What complicates things with regard to the imputation of missing data in IoT, is that the data to be collected in such systems is diverse, and the techniques developed must therefore provide a high level of confidence for different types of applications, besides the need to be robust to the increase in the scale of IoT (and IIoT) deployments. Furthermore, techniques must be lightweight to be able to fulfil real-time IoT application requirements [11].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Classification in Industrial IoT in Case of Missing Sensor Data

Dzaferagic

Marchetti

Macaluso

2022

IEEE Internet Things J.

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This paper addresses the issue of reliability in Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems. We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model. The latter is responsible of imputing missing sensor measurements so that the monitoring system performance is robust to missing data. In particular, we adopt Generative Adversarial Networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules. We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process dataset. Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.

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

Cited by 30 publications

References 43 publications

IoTCrawler: Challenges and Solutions for Searching the Internet of Things

IoTCrawler: Challenges and Solutions for Searching the Internet of Things

Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study

Fault Detection and Classification in Industrial IoT in Case of Missing Sensor Data

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