# Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique
## 5 Conclusions  
In the extensive research presented in this article, we propose a comprehensive approach to improve various aspects of network optimization in WSNs. This optimization model revolves around ant colony optimization of routing and the integration of the improved whale algorithm to maximize coverage range. Through the ant colony optimization method (ACOD), we have adopted a centralized multi-objective solution that is highly effective for clustering and routing problems in wireless sensor networks. For most test cases, this method has better scalability than previous models, mainly due to the use of a larger number of CHs to cover the entire network. We use the ACOD to optimize routing. This algorithm aims to significantly reduce the average EC of each node and improve the throughput of the base station. Our research findings indicate that reducing the active nodes and limiting communication between clusters within CHs can significantly improve the EE in WSNs. Mobile aggregation nodes collect routing data along the optimal trajectory determined by the ACOD, providing an effective routing method. When using the K-means algorithm for WSN (wireless sensor network) clustering, the following conclusions can be drawn based on the experiments:  
Clustering effect: The K-means algorithm can effectively divide the nodes in a WSN into several clusters, with the center of each cluster determined by the average value of the nodes within that cluster. This clustering method helps to organize the network structure and improve data transmission efficiency and energy utilization efficiency. Energy optimization: Through reasonable clustering, the communication distance between nodes can be reduced, thereby reducing energy consumption. In WSNs, communication energy consumption is directly proportional to the square of distance, so optimizing clustering through the K-means algorithm can significantly reduce unnecessary long-distance communication and prolong the network lifespan. Local optimal problem: The K-means algorithm may fall into a local optimal solution, which means the algorithm may not be able to find the globally optimal clustering method. Figure 34: The above figure shows the relationship between node death and time. This is usually related to the selection of initial cluster centers, as different initial cluster centers may lead to completely different clustering results. Sensitivity to K value: The performance of the K-means algorithm highly depends on the preset number of clusters K. In WSNs, selecting the appropriate K value is crucial for balancing the granularity of clustering and communication efficiency. A K value that is too small may lead to too many nodes in the cluster, increasing communication burden; an excessively large K value may lead to frequent inter-cluster communication, which also reduces efficiency. Handling outliers: The K-means algorithm is more sensitive to outliers. In WSNs, if the position or data of certain nodes are abnormal, this may have a significant impact on the clustering results. Therefore, it is important to preprocess and clean the data before applying the K-means algorithm for clustering. Scalability and parallelization: The K-means algorithm has good scalability and can be applied to large-scale WSNs. In addition, due to its iterative nature, the K-means algorithm is also suitable for parallelization to further improve computational efficiency. In summary, the K-means algorithm has a potential application value in WSN clustering, but it also needs to be adjusted and optimized appropriately for specific application scenarios and needs. For example, the ability to handle large datasets can be improved by improving the selection method of initial cluster centers, introducing kernel methods to handle complex-shaped clusters, or using accelerated K-means algorithms. We also adopted a combination of the whale algorithm (WOA) and a BP neural network to increase the coverage range of the wireless sensor network.