Lin Fengren, Lu Minrong
Department of Information Engineering, Fuzhou Polytechnic, Fuzhou, China.
Department of Accountancy, Fujian Jiangxia University, Fuzhou, China.
PLoS One. 2025 Aug 18;20(8):e0330270. doi: 10.1371/journal.pone.0330270. eCollection 2025.
With the extensive growth of the web as well as cellular networks, secure multimedia transmission through cellular networks is needed. Currently, fifth-generation (5G) cellular networks are utilized to perform secure multimedia transmission. Numerous studies have been conducted to design efficient resource allocation approaches for secure video transmission in 5G cellular networks. However, this approach does not offer complete video security related to security against dynamic eavesdroppers or patent defilements. Thus, a resource allocation algorithm named Tasmanian devil whale optimization (TDWO) is introduced for secure video transmission in 5G networks. Here, the recorded educational videos are considered and are transmitted over 5G network transmission resources initially. The resources in 5G networks are allocated via the TDWO model by considering fitness parameters such as the data rate, achievable data rate, and quality of experience (QoE). Here, the deep convolutional neural network (DCNN) model is deployed for the prediction of the QoE in resource allocation. Moreover, extensive experiments are performed to identify the resource allocation performance of the designed TDWO model. The experimental results prove that the TDWO resource allocation algorithm yields significant experimental outcomes, with throughput, bit error rate (BER), QoE and fitness values of 25.557 Mbps, 0.021, 18.332 and 0.013, respectively.
随着网络以及蜂窝网络的广泛发展，需要通过蜂窝网络进行安全的多媒体传输。目前，第五代（5G）蜂窝网络被用于执行安全的多媒体传输。已经进行了大量研究来设计用于5G蜂窝网络中安全视频传输的高效资源分配方法。然而，这种方法并不能提供与防范动态窃听者或专利侵权相关的完整视频安全。因此，引入了一种名为塔斯马尼亚恶魔鲸鱼优化（TDWO）的资源分配算法，用于5G网络中的安全视频传输。这里，考虑录制的教育视频，并首先通过5G网络传输资源进行传输。通过考虑诸如数据速率、可实现数据速率和体验质量（QoE）等适应度参数，经由TDWO模型在5G网络中分配资源。这里，部署深度卷积神经网络（DCNN）模型用于预测资源分配中的QoE。此外，进行了广泛的实验以确定所设计的TDWO模型的资源分配性能。实验结果证明，TDWO资源分配算法产生了显著的实验结果，吞吐量、误码率（BER）、QoE和适应度值分别为25.557Mbps、0.021、18.332和0.013。
