| Texto completo | |
| Autor(es): |
Hassan, Mohamed Khalafalla
;
Ariffin, Sharifah Hafizah Syed
;
Ghazali, N. Effiyana
;
Hamad, Mutaz
;
Hamdan, Mosab
;
Hamdi, Monia
;
Hamam, Habib
;
Khan, Suleman
Número total de Autores: 8
|
| Tipo de documento: | Artigo Científico |
| Fonte: | SENSORS; v. 22, n. 9, p. 27-pg., 2022-05-01. |
| Resumo | |
Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps for resource allocation. This study proposes a reliable hybrid dynamic bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model. Moreover, the proposed framework can dynamically react to all the changes occurring in the data series. Backbone traffic was used to validate the proposed method. As a result, the forecasting accuracy improved significantly with the proposed framework and with minimal data loss from the smoothing process. The results showed that the hybrid moving average LSTM (MLSTM) achieved the most remarkable improvement in the training and testing forecasts, with 28% and 24% for long-term evolution (LTE) time series and with 35% and 32% for the multiprotocol label switching (MPLS) time series, respectively, while robust locally weighted scatter plot smoothing and LSTM (RLWLSTM) achieved the most significant improvement for upstream traffic with 45%; moreover, the dynamic learning framework achieved improvement percentages that can reach up to 100%. (AU) | |
| Processo FAPESP: | 21/10234-5 - Técnicas adaptativas de detecção de intrusão para cidades inteligentes baseadas em internet das coisas |
| Beneficiário: | Mosab Hamdan Adam Mohamed Alhassan |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |