Enhancement of Network Performance Using Sigmoid-Based Metrics on a Routing Protocol

Efficient routing in network traffic protocols is critical for optimizing performance and ensuring seamless data transmission. This research introduces sigmoid-based metrics as a novel approach to enhancing routing performance in network traffic protocols. By leveraging the mathematical properties of the sigmoid function, the proposed metrics dynamically evaluate link quality and network congestion, enabling more adaptive and reliable routing decisions. The study explores the integration of sigmoid functions into traditional routing algorithms, emphasizing their potential to smooth performance variations, prioritize paths with minimal delays, and jitter. Experimental simulations demonstrate significant improvements in throughput, latency, and packet delivery ratio compared to conventional metrics. Additionally, the adaptive nature of sigmoid-based metrics ensures robust performance across varying network conditions. This work contributes to the advancement of intelligent routing mechanisms, offering a scalable and efficient solution for modern network challenges. Future directions include real-world deployment and exploration of sigmoid-based approaches in emerging network architectures such as 5G and IoT.