# Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique
## 3 Results
### Simulation settings  
For analysis, we simulated a WSN (WSN) consisting of N SNs (designated as CHs) and a base station. Considering the actual usage scenario, we assume that all nodes are fixed after deployment and their positions remain unknown. Each node is assigned a unique ID, with ID 0 assigned to the BS. In order to obtain the most realistic simulation results, we followed the EC model based on specific features of the Chipcon CC2420 radio transceiver data table. The total energy consumption E (Sn) of any sensor was calculated by adding up the energy consumed by the sensor during different operating states (i.e., sleep, reception, or transmission) and the energy consumed during transitions between these states. In these calculations, the corresponding time spent in these states and the number of transitions for each sensor were considered. Figure 31: BP regression plot. Figure 32: WOA-BP regression plot. The entire simulation experiment was divided into two main parts. Firstly, the effectiveness of the standard whale optimization algorithm in solving the WSN coverage problem was evaluated. Subsequently, in an identical simulation environment, the effectiveness of the enhanced whale optimization algorithm was measured based on the previously tested basic version. This two-stage simulation process is crucial for ensuring a comprehensive understanding of the comparative advantages of each algorithm. The parameters for the experiment have been carefully selected to accurately reflect common challenges and constraints in real-world scenarios. Specifically, we carefully considered factors such as the average EC of each node, the throughput of the base station, and execution time when setting up the simulation. By incorporating these considerations into our performance evaluation strategy, our goal is to create a highly credible simulation setup that is highly relevant to the actual deployment of WSNs. The ultimate goal is to effectively represent the impact and potential of the proposed optimization strategy on WSN performance using this setting. Compare network performance indicators  
To quantify the performance of the proposed network optimization strategy, it is necessary to evaluate several important network metrics [32]. One of the most important indicators is network lifespan. It is defined as the point at which the first sensor node in the network stops running due to energy depletion [33]. Comparing our method with the network lifetime of standard ant colony optimization and mobile aggregation ant colony optimization M proves the advantages of our model. Specifically, our network did not begin node depletion until after approximately 2600 rounds, while ant colony optimization M began node depletion after 2330 rounds. This indicates that our proposed algorithm reduces the energy consumption of CHs by using mobile aggregation nodes to directly communicate with CHs within a short communication range, thereby greatly extending the lifespan of the network [34]. In addition, the proposed method exhibits enhanced performance on different numbers of nodes, as demonstrated by the extensive simulation results. These indicate that our network optimization strategy always performs better than ant colony optimization M by improving the network lifespan and reducing the total energy consumption [35]. The final key performance indicator involves determining the optimal trajectory for the mobile convergence point through the ACOD. Traditional ACOA allows ants to deviate from the target over time. Our model enhances the global search ability, improves the convergence speed, and avoids becoming trapped in the local optimal solution by considering the distance between CHs. By doing so, our model reduces latency, enhances data collection, and thus improves the overall network performance of wireless sensor networks.