| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Fed Rio de Janeiro UFRJ, Grp Teleinformat & Automacao GTA, PEE COPPE DEL Poli, BR-21941972 Rio De Janeiro, RJ - Brazil
[2] Sorbonne Univ, Lab Informat Paris 6 LIP6, F-75005 Paris - France
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | INFORMATION; v. 12, n. 10 OCT 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
In an Internet of Things (IoT) environment, sensors collect and send data to application servers through IoT gateways. However, these data may be missing values due to networking problems or sensor malfunction, which reduces applications' reliability. This work proposes a mechanism to predict and impute missing data in IoT gateways to achieve greater autonomy at the network edge. These gateways typically have limited computing resources. Therefore, the missing data imputation methods must be simple and provide good results. Thus, this work presents two regression models based on neural networks to impute missing data in IoT gateways. In addition to the prediction quality, we analyzed both the execution time and the amount of memory used. We validated our models using six years of weather data from Rio de Janeiro, varying the missing data percentages. The results show that the neural network regression models perform better than the other imputation methods analyzed, based on the averages and repetition of previous values, for all missing data percentages. In addition, the neural network models present a short execution time and need less than 140 KiB of memory, which allows them to run on IoT gateways.</p> (AU) | |
| FAPESP's process: | 15/24494-8 - Communications and processing of big data in cloud and fog computing |
| Grantee: | Nelson Luis Saldanha da Fonseca |
| Support Opportunities: | Research Projects - Thematic Grants |