ANFIS and Deep Learning based missing sensor data prediction in IoT
Yazarlar (4)
Dr. Öğr. Üyesi Metehan GÜZEL Kafkas Üniversitesi, Türkiye
İbrahim Kök Pamukkale Üniversitesi, Türkiye
Diyar Akay Gazi Üniversitesi, Türkiye
Suat Özdemir Gazi Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Concurrency and Computation: Practice and Experience (Q3) | ||
| Dergi ISSN | 1532-0626 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2020 |
| Kabul Tarihi | 18-05-2019 | Yayınlanma Tarihi | 20-06-2019 |
| Cilt / Sayı / Sayfa | 32 / 2 / – | DOI | 10.1002/cpe.5400 |
| Makale Linki | https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5400 | ||
| Özet |
| SummaryInternet of Things (IoT) consists of billions of devices that generate big data which is characterized by the large volume, velocity, and heterogeneity. In the heterogeneous IoT ecosystem, it is not so surprising that these sensor‐generated data are considered to be noisy, uncertain, erroneous, and missing due to the lack of battery power, communication errors, and malfunctioning devices. This paper presents Deep Learning (DL) and Adaptive‐Network based Fuzzy Inference System (ANFIS) based prediction models for missing sensor data problem in IoT ecosystem. First, we build ANFIS based models and optimize their parameters. Then, we construct DL based models by using Long Short Term Memory (LSTM) network structure and optimize its parameters by applying the grid search method. Finally, we evaluate all the proposed models with Intel Berkeley Lab dataset. Experimental results demonstrate that the proposed models can significantly improve the prediction accuracy and may be promising for missing sensor data prediction. |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları |