Title: Adaptive optimisation of a resource allocation algorithm for secure video transmission in 5G networks

Authors: Augustin Minalkar; Srinath Doss; Ruchi Doshi

Addresses: Department of Computer Science and IT, Azteca University, Mexico ' Faculty of Engineering and Technology, Botho University, Botswana ' Department of Computer Science and IT, Azteca University, Mexico

Abstract: This paper presents a novel optimisation model Tasmanian devil whale optimisation (TDWO) for secure transmission of educational video using fifth-generation (5G) cellular networks. At first, the input educational videos taken from the database is transmitted via 5G networks. Then, the allocation of resource is performed using the designed TDWO model by considering different fitness parameters, like data rate, achievable data rate, and quality of experience (QoE). Here, the deep learning model, deep convolutional neural networks (DCNN) is utilised for the prediction of QoE for resource allocation. Moreover, the resource allocation performance of the TDWO is validated by comparing with other resource allocation schemes. Here, the TDWO algorithmic approach achieved superior performance with throughput of 25.557Mbps accuracy of 91.43%, bit error rate (BER) of 0.021, QoE of 18.332, and fitness of 0.013.

Keywords: deep convolutional neural networks; DCNN; Tasmanian devil whale optimisation; TDWO; whale optimisation algorithm; WOA; Tasmanian devil optimisation; TDO; network resource allocation.

DOI: 10.1504/IJAHUC.2025.147569

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.49 No.3, pp.188 - 201

Received: 15 Feb 2024
Accepted: 13 Nov 2024
Published online: 21 Jul 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article