Reem Alkanhel1, El-Sayed M. El-kenawy2,3, D. L. Elsheweikh4, Abdelaziz A. Abdelhamid5,6, Abdelhameed Ibrahim7, Doaa Sami Khafaga8,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 427-442, 2023, DOI:10.32604/cmc.2023.032885
Abstract Traffic prediction of wireless networks attracted many researchers and practitioners during the past decades. However, wireless traffic frequently exhibits strong nonlinearities and complicated patterns, which makes it challenging to be predicted accurately. Many of the existing approaches for predicting wireless network traffic are unable to produce accurate predictions because they lack the ability to describe the dynamic spatial-temporal correlations of wireless network traffic data. In this paper, we proposed a novel meta-heuristic optimization approach based on fitness grey wolf and dipper throated optimization algorithms for boosting the prediction accuracy of traffic volume. The proposed algorithm is employed to optimize the… More >