# Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique
## 4 Discussion
The results indicate that our improved ACOD always performs better than its corresponding algorithm, ant colony optimization M.  
An important indicator used in network performance evaluation is network lifespan, defined as the duration of time before the first node runs out of energy and stops running. Our model indicates that there is a significant improvement in extending the network lifespan due to the direct communication between mobile convergence points and CHs over relatively short distances. In terms of energy utilization, our algorithm selects the node with the highest remaining energy as the CH. The CH will only rotate when its remaining energy is below the preset energy threshold. The EC rate in our scheme has always been lower than that of ant colony optimization M, indicating that the energy performance in WSNs has been enhanced. In addition, we have deployed the ACOD to determine the optimal movement trajectory of the moving convergence point. Our improved ant colony optimization algorithm is different from the traditional algorithm. The traditional algorithm usually makes the ants move to the next node according to the current information, but it will deviate from the target over time. By considering the distance between CHs to improve the distance heuristic factor, we have improved the global search ability, avoided becoming stuck in local optima, and improved the convergence speed. Finally, our method significantly enhances the data collection performance of WSNs. Compared to ant colony optimization M, the amount of data collected within a given time range is higher. In summary, the simulation results provide convincing evidence that our proposed model can significantly improve the performance of WSNs in multiple aspects. Finally, the comparison with the whale swarm optimization algorithm shows that the proposed model has the superior ability to improve network coverage with fewer iterations, which helps to optimize the overall network performance. In summary, the proposed model combines ant colony optimization algorithm-based routing coverage maximization with an enhanced whale algorithm and exhibits robust comparative performance compared to the well-known WSN protocol. It provides an energy-saving, reliable, and scalable solution for WSN performance optimization, outperforming existing methods in terms of average EC, base station throughput, and execution time per node. B. The meaning of the proposed model  
The impact of the proposed model is multifaceted, including both theoretical and practical aspects of wireless sensor networks. Firstly, the model indicates that the EC in WSNs can be significantly optimized. The proposed ant colony optimization routing model proposes an energy-saving mechanism. Itmaintains the number of active nodes at the lowest level by carefully selecting the CH, and follows an optimized trajectory for moving aggregation nodes. In addition, the model also demonstrates the possibility of improving network data reliability by establishing dedicated routing trees. It optimizes the total throughput of the base station, thereby enhancing the consistency of data transmission. This is particularly important in applications such as infrastructure monitoring, industrial sensing, and telemetry, where correct and timely data transmission can have a significant impact. The model also demonstrates the efficiency of the integrated whale algorithm in maximizing coverage. By optimizing the initial population distribution and accelerating the global search, the whale algorithm ensures a wide-area coverage of nodes, which is an ideal feature in wireless sensor networks. Finally, the proposed model emphasizes the crucial role of effective clustering mechanisms in wireless sensor networks. It can be inferred from the ant colony algorithm that determining the number of CHs, the clustering nodes, and the link quality between the CM and CHs can significantly improve network performance.