Intelligent Congestion Control in Wireless Sensor Networks (WSN) Based on Generative Adversarial Networks (GANs) and Optimization Algorithms

Internet of Things (IoT) technology has facilitated the deployment of autonomous sensors in remote and challenging environments, enabling substantial advancements in environmental monitoring and data collection. IoT sensors continuously gather data, transmitting it to a central Base Station (BS) via designated Cluster Heads (CHs). However, data flow encounters frequent congestion at CH nodes, negatively impacting network performance and Quality of Service (QoS). This paper introduces a novel congestion control strategy tailored for Wireless Sensor Networks (WSNs) to balance energy efficiency and data reliability. The proposed approach follows an eight-step process, integrating Generative Adversarial Networks (GANs) for enhanced clustering and Ant Colony Optimization (ACO) for optimal CH selection and routing. GANs simulate realistic node clustering, achieving better load distribution and energy conservation across the network. ACO then selects CHs based on energy levels, distance, and network centrality, using pheromone-based routing to adaptively manage data flows. A congestion factor (CF) threshold is also incorporated to dynamically reroute traffic when congestion risks arise, preserving QoS. Simulation results show that this approach significantly improves QoS metrics, including latency, throughput, and reliability. Comparative evaluations reveal that our method outperforms existing frameworks, such as Fuzzy Structure and Genetic-Fuzzy (FSFG), Deep Reinforcement Learning Cache-Aware Congestion Control (DRL-CaCC), and Adaptive Cuckoo Search Rate Optimization (ACSRO).