## 5 Open Issue  
The GAN-ACO congestion control strategy offers significant advantages in maintaining energy efficiency, reducing latency, and enhancing network reliability in WSNs. However, several open issues remain, and further investigation is required to fully realize the potential of this method across diverse WSN applications. **Analysis of Results and Observed Patterns**  
The experimental results show that the proposed GAN-ACO strategy achieves higher energy efficiency and lower latency than traditional algorithms like FSFG, DRL-CaCC, and ACSRO. These improvements stem from GANs' ability to create balanced clusters that minimize redundant data transmissions and ACO's adaptive path selection based on real-time pheromone levels. The criteria for selecting this GAN-ACO configuration were largely based on the need to reduce communication overhead and enhance adaptive load balancing. For instance, the use of a lower pheromone evaporation rate (\(\rho\) = 0.1) ensures that the pheromone buildup focuses on optimal paths without overcrowding suboptimal routes. This balance prevents excessive retransmissions and rerouting, conserving energy and reducing congestion. Energy efficiency, a critical criterion, is improved as GAN-generated clusters reduce the number of intra-cluster communications by strategically grouping nodes, which minimizes unnecessary transmissions and energy-intensive long-range communication. As shown in Figure 5, GAN-ACO consistently maintains lower energy consumption levels compared to other methods, due to reduced data redundancy and efficient clustering. In high-traffic situations, DRL-CaCC and ACSRO exhibit faster energy depletion, which can be attributed to less optimized routing, especially under high loads, where DRL-CaCC faces computational delays in updating routes, and ACSRO's predefined routing strategies are less adaptive. Similarly, the lower latency of the GAN-ACO strategy, demonstrated in Figure 6, is a direct result of ACO's dynamic pheromone-based routing, which enables rapid path selection and adaptation to changes in traffic flow. This criterion is crucial, as WSNs often require minimal delay, especially for real-time monitoring applications. The results indicate that under high traffic, algorithms like DRL-CaCC and ACSRO suffer from increased latency due to their limited rerouting flexibility. The GAN component's role in load balancing across clusters further reduces latency, as it prevents bottleneck formation by distributing data more evenly across network links. The proposed method, as shown in Figure 7, demonstrates exceptionally high reliability, primarily due to its use of the ACO algorithm. The method draws inspiration from how ants leave pheromone trails to signal the best routes, directing others along the quickest paths. Modeling this behavior, the algorithm efficiently discovers optimal network routes, reducing congestion and delays. This ensures data flows smoothly and reliably, achieving high throughput to the BS, even as network demands vary. **Key Open Issues:**  
**Real-Time Adaptability and Response to Dynamic Network Conditions:** Although GAN-ACO dynamically adjusts routes based on pheromone levels, it lacks mechanisms for instantaneous response to sudden environmental changes, such as node failures. Currently, ACO's pheromone trail system allows for gradual adaptation, which may not be sufficient for critical applications requiring rapid reconfiguration. Future work could incorporate RL techniques to enhance real-time adaptability, allowing the algorithm to adjust routes or cluster formations in real time in response to abrupt changes. **Optimizing for Resource Constraints in Ultra-Low Power WSNs:** The current GAN-ACO configuration is optimized for networks with moderate computational resources, which may not be feasible in ultra-low-power sensor networks. The GAN's clustering mechanism, while effective, is computationally intensive and may drain node energy rapidly.