## 4 Simulation and Results
Reliability here refers to the probability that data packets are successfully delivered without loss, which is essential in applications requiring consistent and accurate data transmission. The proposed method demonstrates the highest reliability throughout the duration, consistently close to 100%. This high reliability can be attributed to its use of GANs for optimized cluster formation and ACO for dynamic path selection. Together, these components enable the method to adapt effectively to network changes, reroute traffic as needed, and prevent packet loss even under varying network conditions. FSFG and ACSRO also exhibit relatively high reliability but fall slightly below the proposed method, indicating that while they are effective in maintaining stable performance, they may not respond adaptively to congestion or changing network loads. DRL-CaCC shows the lowest reliability among the  
Figure 6: Latency metrics in different packets. four algorithms, with noticeable declines over time. This trend suggests that DRL-CaCC may struggle with maintaining high packet delivery rates under prolonged operation, potentially due to its reliance on computationally intensive learning processes that may not adjust to real-time changes as efficiently. Overall, the proposed method's superior reliability across all time intervals highlights its robustness and suitability for critical applications in WSNs where consistent data integrity is essential. Throughput is a measure of the rate at which a system or network processes data over a given period. It reflects the system's ability to handle data traffic efficiently, indicating how much information can be successfully transmitted from source to destination without loss or delay. High throughput is essential in applications that require fast data exchange, as it directly impacts performance, user experience, and operational efficiency. The proposed method in Figure 8 consistently achieves the highest throughput across all time intervals, showing a clear advantage over FSFG, DRL-CaCC, and ACSRO. This superior performance can be attributed to the use of GANs and ACO, which enable effective load distribution and adaptive path selection. The GAN-based clustering allows for balanced data handling within each cluster, while ACO optimizes routing to reduce bottlenecks, resulting in efficient data flow and higher throughput. FSFG also performs well but trails slightly behind the proposed method, maintaining relatively stable throughput that increases moderately over time. DRL-CaCC and ACSRO exhibit lower throughput, with more gradual increases, indicating less efficiency in managing data under similar conditions. This difference suggests that DRL-CaCC and ACSRO may face limitations in adapting to real-time congestion or optimizing network paths as dynamically as the proposed method. Overall, the proposed method's consistent high throughput highlights its suitability for data-intensive WSN applications, where maintaining high data transfer rates is essential for network performance and reliability. Figure 7: Reliability metrics in different time. Figure 9 illustrates the execution time of the proposed GAN-ACO congestion control method compared to FSFG, DRL-CaCC, and ACSRO under varying network loads. The \(X\)-axis represents the network load in packets, reflecting the input size, while the \(Y\)-axis denotes execution time in milliseconds, indicating the computational time. As network load increases from 1000 to 4000 packets, all methods exhibit a rising execution time, but the proposed method consistently demonstrates the lowest execution time, highlighting its computational efficiency. This aligns with the reviewer's concern regarding run-time complexity, showing that GAN-ACO scales more efficiently than other approaches. Figure 8: Throughput metrics in different time. Figure 9: Execution time metrics in network load packets.