Advanced Temporal Attention Mechanism based 5G Traffic Prediction Model for IoT Ecosystems

Traffic prediction in 5G is important for effective deployment and operation of Internet of Things (IoT) ecosystems. It enables resource management and optimization, guaranteeing that the network can handle unpredictable traffic volumes without experiencing traffic jams. This helps to ensure high quality of service and low latency for applications such as autonomous automobiles and virtual reality. Predictive traffic management further enhances user experience by keeping services consistent and reliable, particularly during busy hours. There are various approaches to traffic prediction in 5G networks, and each has advantages and disadvantages of its own. The choice of model will depend on how precise, adaptable, and computationally demanding the network must be. The model proposed in this paper integrates lightweight convolution with temporal attention to deliver accurate and efficient traffic prediction for 5G networks that may further be useful for developing IoT ecosystem.