Data Quality Improvements for Internet of Things Using Artificial Neural Networks

The Internet of Things has gained considerable attention due to its potential applications in multiple domains. However, some deployment environments may be hostile and this may affect the quality of data (QoD) and alter its accuracy. In order to ensure a high level of reliability, an IoT system should be able to clean its own sensed data by discarding those instances that are erroneous or incoherent. To achieve the data quality improvements, this paper suggests a new approach based on Artificial Neural Network (ANN). The proposed scheme can prematurely and efficiently detect outliers before forwarding them to a central processing unit. The performance of this proposed solution is validated through simulations, using a real dataset, and compared with other well-known models. Our findings demonstrate that the proposed approach outperforms the compared models in terms of accuracy, f-score, recall and precision metrics.