## 4 Simulation and Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline Network Area & 500 m \(\times\) 500 m & Physical size of the simulated network \\ \hline Number of Sensor Nodes (N) & 100 & Total number of sensor nodes \\ \hline Number of Base Stations & 1 & Central base station for data aggregation \\ \hline Initial Node Energy & 2 Joules & Initial energy assigned to each sensor node \\ \hline Transmission Range & 100 m & Communication range for each sensor node \\ \hline Simulation Duration & 1000 s & Total time of simulation \\ \hline Packet Count & 10,000 & Total number of packets generated during simulation \\ \hline Average Packet Size & 512 bytes & Size of each data packet \\ \hline Traffic Load & Varied (low, moderate, high) & Different traffic loads to test congestion control \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset configuration parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline Pheromone Evaporation Rate (\(\rho\)) & 0.1 & Controls pheromone decay on paths \\ \hline Pheromone Deposit Factor (Q) & 0.5 & Strength of pheromone reinforcement \\ \hline Weight Parameters (\(\alpha\), \(\beta\), \(\gamma\), s, k) & 1.5, 1.2, 0.8, 1.0, 1.0 & Weights for energy, bandwidth, latency, steps, pheromone \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ACO algorithm configuration parameters. NLUDLC is another critical metric for WSNs, reflecting how long the network can operate before nodes deplete their energy. This metric is particularly important in energy-constrained environments, where extending the network lifetime is a primary goal. Analyzing network lifetime across different traffic loads helps evaluate how well the congestion control strategy conserves energy and extends network operation. A higher network lifetime at elevated traffic loads indicates that the strategy manages energy consumption efficiently, even under heavy demands. Table 6 illustrates network lifetime and the percentage of operational nodes after 1000 s for low, moderate, and high traffic loads, offering a comprehensive view of network sustainability over time. In this paper, we combined GANs with an ACO algorithm to develop an advanced congestion control strategy for WSNs. The GAN component is utilized to create realistic clusters by learning the spatial distribution and energy levels of nodes, which helps in forming stable and energy-efficient clusters that reduce overall network traffic. The ACO algorithm is then applied for adaptive routing, where pheromone-based probabilistic selection is used to dynamically adjust paths based on network conditions, such as energy levels, link quality, and \(CF\). To validate our method, we conducted a comparative analysis with three established protocols: FSFG [22], DRL-CaCC [21], and ACSRO [18]. These protocols were chosen for their focus on energy efficiency, congestion management, and longevity in WSNs, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Traffic Load** & **Network Lifetime (s)** &
\begin{tabular}{c} **Percentage of Nodes** \\ **Operational After 1000 s** \\ \end{tabular} \\ \hline Low Traffic Load & 5200 & 92\% \\ Moderate Traffic Load & 3800 & 78\% \\ High Traffic Load & 2600 & 62\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Network Lifetime Under Different Load Conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Link ID** & \begin{tabular}{c} **Low Traffic** \\ **Load CF** \\ \end{tabular}  & \ \begin{tabular}{c} **Moderate Traffic** \\ **Load CF** \\ \end{tabular}  & \begin{tabular}{c} **High Traffic** \\ **Load CF** \\ \end{tabular}