## 4 Simulation and Results
 \\ \hline Link 1 & 0.20 & 0.45 & 0.75 \\ Link 2 & 0.15 & 0.40 & 0.70 \\ Link 3 & 0.18 & 0.42 & 0.68 \\ Link 4 & 0.12 & 0.38 & 0.72 \\ Link 5 & 0.22 & 0.46 & 0.78 \\ Link 6 & 0.14 & 0.41 & 0.74 \\ Link 7 & 0.16 & 0.39 & 0.71 \\ Link 8 & 0.13 & 0.37 & 0.69 Energy consumption is a pivotal concern in WSNs, as the sensors typically depend on limited power sources, such as batteries, which are difficult to replace or recharge, especially in remote or hazardous environments. Efficient energy management is essential for extending the network's lifespan, enabling consistent and reliable data collection and transmission over time. In addition, minimizing energy consumption helps maintain stable network performance and reliability, as depleted nodes can lead to data loss, reduced coverage, and compromised communication paths. Figure 5 illustrates a comparative analysis of energy consumption over time for four congestion control algorithms in WSNs: ACSRO (in purple), FSFG (in green), DRL-CaCC (in blue), and the proposed method, GAN-ACO (in red). Here, the \(y\)-axis represents the remaining energy (in Joules) of the network nodes, while the \(x\)-axis displays time in seconds, covering a range from 200 to 1000 s. This metric provides insights into each algorithm's effectiveness in preserving energy over extended network operations. The results reveal that the proposed method, GAN-ACO, consistently achieves lower energy consumption than the other algorithms as the simulation progresses. While ACSRO and FSFG maintain higher energy levels in the early stages, they show steeper declines over time, indicating accelerated energy depletion under sustained network conditions. DRL-CaCC demonstrates moderate energy usage, with a steady decline that resembles FSFG but at a slightly faster rate. The proposed method's superior energy efficiency can be attributed to optimized load distribution and adaptive routing facilitated by the GAN and ACO algorithms, which minimize redundant data transmissions and avoid energy-intensive routing decisions. Figure 6 demonstrates that the proposed method achieves the lowest latency across a wide range of network loads, maintaining consistently low delay times even as packet load reaches 10,000. This performance can be attributed to the method's GAN-ACO framework, where the GANs facilitate balanced clustering, while the ACO component ensures adaptive path selection based on real-time pheromone levels. Together, these elements enable dynamic rerouting and efficient load balancing, effectively reducing network congestion and the associated delays. The FSFG algorithm also shows relatively low latency but  
Figure 5: Energy consumption over time. begins to fall behind the proposed method as network load intensifies, likely due to its limited adaptability under high-traffic conditions. This limitation may stem from a less responsive mechanism for adjusting to real-time network demands compared to the more flexible, adaptive approach provided by GAN-ACO. In contrast, the DRL-CaCC and ACSRO algorithms exhibit higher latency levels, which increase sharply with network load. This pattern suggests that these methods may face challenges in optimizing data flow when traffic is heavy, possibly due to slower congestion response or a lack of robust load distribution techniques needed for low-latency operation. Reliability refers to the capacity of a system or component to perform its required functions consistently over a specified period. It is a key metric in evaluating the robustness and dependability of technology, especially in critical applications where consistent performance is essential for safety and user satisfaction. High reliability ensures that a system maintains its functionality and minimizes the risk of failures, which in turn enhances user confidence, reduces maintenance costs, and extends the lifespan of the system. In Figure 7, it is evident that the proposed method demonstrates superior reliability when compared to FSFG, indicating its greater effectiveness in sustaining performance over time.