Akademik Veri Yönetim Sistemi
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, cilt.20, sa.3, 2025 (SCI-Expanded)
In the context of Internet of Things (IoT) structures, sensor nodes have been observed to generate erroneous data due to their constrained operational capacity and position. The presence of faulty nodes can lead to significant challenges in communication, data traffic, and data evaluation. Consequently, it is imperative to segregate data obtained from faulty nodes from standard data. Concurrently, the identification of the specific fault type is paramount. The present study utilised machine learning and deep learning techniques to classify fault types, with the data collected from 54 sensors in a closed building over a period of 3 months. Initially, the performance analysis of of data. Subsequently, as certain classes were characterised by limited data, data augmentation was implemented using synthetic data, and the SMOTET-LSTM model was developed through HPO to the other algorithms.