Develop A Novel Deep Learning Routing with IoT for Improving the Performance of Efficient Data Transmission and Attack Detection in 5G WSN

Abstract

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have accelerated due to the
rapid development of 5G technology, making safe and effective communication techniques
essential. Researchers compare the effectiveness of various deep learning models, such as
Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), for network
path optimization and harmful activity detection. Experimental findings demonstrate significant
improvements in network efficiency parameters, including packet delivery ratio, productivity,
and delay. Emphasize how these protocols' enhanced security measures help detect and
counteract routing attacks, ensuring the accuracy and reliability of information transmission in
dynamic and heterogeneous network conditions. The results highlight how deep learning
approaches can be integrated into routing protocols to meet the stringent demands of next-
generation WSN and IoT systems.