Revolutionizing Congestion Control Protocols for Robust WSN Routing Dynamics Through Optimized Dual Aggregated Attention Capsule Network

Abstract

Wireless sensor networks (WSNs) have a vital part in real-time data distribution in areas like intelligent cities and tracking the environment. However, challenges like network congestion often result in packet loss, delays, and energy inefficiencies, severely impacting performance. Existing congestion control methods struggle with unpredictable traffic patterns and fluctuating node energy levels, leading to incorrect routing decisions and shortened network lifespans. This highlights the need for an intelligent congestion control protocol that dynamically manages traffic flow and adapts to network conditions. This study introduces a novel framework, “Revolutionizing Congestion Control Protocols for Robust WSN Routing Dynamics through Optimized Dual Aggregated Attention Capsule Network (DAT-G2ACN-GTAO),” which leverages an Advanced Atomic Orbital Search Paradigm to organize nodes and select cluster heads, thus enhancing complexity management and extending network lifespan. The framework employs a Dual Aggregation Transformer-based Gated Graph Attention Capsule Network to accurately detect congestion, steering data toward less congested routes to optimize transmission. Additionally, the Giant Trevally Adaptive Optimization (GTAO) method fine-tunes network parameters in real time, enhancing throughput and minimizing energy consumption. Experimental results demonstrate that the DAT-G2ACN-GTAO protocol significantly outperforms traditional methods, achieving a packet delivery ratio exceeding 99.2%, maintaining network throughput stability above 99.5%, and ensuring 99.3% accuracy in congestion detection and prioritized data transmission. This robust congestion control framework marks a substantial improvement over conventional approaches, significantly boosting WSN efficiency and network longevity, making it a critical enabler for deploying reliable, energy-efficient WSNs across diverse applications.