| Texto completo | |
| Autor(es): |
Horstmannm, Leonardo Passig
;
Conradi Hoffmann, Jose Luis
;
Scheffel, Roberto Milton
;
Frohlich, Antonio Augusto
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2023 IEEE 9TH WORLD FORUM ON INTERNET OF THINGS, WF-IOT; v. N/A, p. 6-pg., 2023-01-01. |
| Resumo | |
Due to the nature of Wireless Sensor Networks (WSN), several factors can interfere with sampling and communication. These faults may compromise data quality and disrupt timing and power requirements due to re-sampling and re-transmission. Recent works in the literature propose data imputation and confidence attribution mechanisms as alternatives to overcome missing, or bad-quality, data. Nevertheless, these solutions often lack mechanisms to evaluate the quality of data imputation on-the-fly. This work combines a confidence attribution mechanism previously proposed by the authors with a Deep Autoencoder (DAE) to promote an effective data imputation mechanism able to handle transient faults in WSNs. We rely on the ability of Deep Autoencoders to learn data correlation in order to attribute confidence to data based on the loss of information in the encoding-decoding process. The fine-tuning of the confidence attribution parameters considers the discrepancy between the original confidence attribution method and the loss of information calculated for original and predicted data. Finally, through a case-study, we demonstrate that the DAE-based confidence attribution is capable of matching the confidence attribution that relies on the comparison between original and predicted data in more than 86% of the cases, without requiring the original data. (AU) | |
| Processo FAPESP: | 21/02385-3 - Uso de Machine Learning e monitoramento de performance para verificação de integridade em Gateways IIoT |
| Beneficiário: | Leonardo Passig Horstmann |
| Modalidade de apoio: | Bolsas no Brasil - Programa Capacitação - Treinamento Técnico |
| Processo FAPESP: | 21/02384-7 - Desenvolvimento de um protocolo de verificação de integridade e implementação Gateway IIoT baseado em Ambiente de Execução Segura (TEE) |
| Beneficiário: | José Luis Conradi Hoffmann |
| Modalidade de apoio: | Bolsas no Brasil - Programa Capacitação - Treinamento Técnico |
| Processo FAPESP: | 20/05142-1 - Gateway seguro para a internet das coisas industriais |
| Beneficiário: | Antônio Augusto Medeiros Fröhlich |
| Modalidade de apoio: | Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE |