# Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique
## 4 Discussion  
In this section, the results obtained from the proposed model will be evaluated and compared with other relevant models, and the impact of the proposed model on WSNs will be discussed. Information transmission between wireless sensor nodes is affected by power supply, quality, and terrain, which are based on probability, dispersion, and liquidity; the proposed model has strong global search ability, is not continuous, and is not based on location. The following is shown in Table 6:  
Compared with traditional models, the test results of the proposed model show a significant improvement in network performance. This improvement stems from the integration of ant colony optimization-based routing, K-means clustering, and the improved whale algorithm to achieve maximum coverage. Observations indicate that using ant colony optimization-based routing can significantly reduce the travel distance and energy consumption of mobile aggregation nodes. In addition, the application of the ACOD helps to determine the most effective network layout, thereby improving the robustness and lifespan of the network. By comparing the proposed model with traditional methods, it is evident that our model outperforms the latter in all aspects. A traditional WSN is often affected by issues related to low energy efficiency, a shortened network lifespan, and hotspots. The proposed model solves these problems through energy-saving routing, optimal node layout, an efficient data transmission mode, and the improved whale algorithm to achieve better network coverage. In terms of impact, the proposed model provides a promising approach for efficient management of WSNs. This model can address the common challenges in this field and can be widely implemented to improve overall network performance. If applied properly, this WSN-based performance optimization integrated model can make progress in military operations, environmental monitoring, public safety, healthcare, and other \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **Particle Swarm Optimization** & **Ant Colony** \\ \hline Inspiration source & A school of birds or fish & Ants foraging for food \\ \hline Information transmission & Location and speed information & Pheromone \\ \hline Search method & Based on location & Based on probability \\ \hline Mathematical model & Simple & Complex \\ \hline Applicable problem types & Continuous optimization & Discrete optimization \\ \hline Convergence & Easy to fall into local optima & Has a strong global search ability \\ \hline Computational complexity & Low & High \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimization algorithm feature comparison. fields. The potential of this model lies in its ability to meet the growing demand for more efficient and reliable wide-area sensor networks. In addition, the scalability and EE of the proposed model can extend the lifespan of WSNs, making it suitable for long-term on-site deployment. Therefore, it has broad application prospects in improving the efficiency and expected lifespan of wireless sensor networks. Future work can focus on further improving algorithms and application technologies to further enhance network performance and adaptability to different network conditions. A. Interpretation of evaluation results  
The evaluation results obtained from the simulation of our proposed model confirm the hypothesis that integrating ant colony optimization and advanced whale algorithms into WSNs can significantly optimize network performance. In our simulation environment, we set parameters related to the proposed algorithm and ant colony optimization M algorithm for comparative analysis. We uniformly deployed all SNs and aggregation nodes in a 2 \(\times\) 200 \(\times\) 200 m\({}^{3}\) area and conducted multiple simulations to determine the appropriate values for parameter changes.