# Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique
## 2 Materials and Methods  
### Ant Colony  
The ant colony optimization algorithm is a probabilistic algorithm that simulates the foraging behavior of ants in nature, with characteristics of distribution calculation, positive feedback of information, and heuristic searching [22]. In ant colony optimization-based routing algorithm simulations, the parameters are \(\alpha\), \(\beta\), and \(\rho\). The numerical value of these parameters is crucial for the performance and effectiveness of the algorithm. These parameters play different roles in ant colony algorithms and affect the decision-making process of ants when choosing paths. Parameter \(\alpha\): The factor that usually represents the importance of pheromones determines the degree to which ants are influenced by pheromones when choosing a path. A pheromone is a chemical substance released by ants during their search for food, used to transmit pathway information [23]. When the value of \(\alpha\) is high, ants are more inclined to choose paths with higher pheromone concentration, which helps the algorithm quickly converge to the optimal solution. However, an excessively large \(\alpha\) value may cause the algorithm to fall into local optima too early while ignoring other potentially better paths [24]. Therefore, adjustments need to be made based on specific issues deriving from the \(\alpha\) value to strike a balance between global search and local search. Parameter \(\beta\): The factor representing the importance of heuristic information reflects the degree to which ants are influenced by heuristic information (such as path length, congestion level, etc.) when choosing a path [25]. Heuristic information helps ants discover better paths during the exploration process. When the value of \(\beta\) is large, ants are more inclined to choose paths with better heuristic information, which helps the algorithm to avoid becoming stuck in local optima during the search process. However, an excessively large \(\beta\) value may cause the algorithm to ignore the role of pheromones, thereby reducing the convergence speed of the algorithm. Therefore, it is also necessary to make adjustments according to specific issues deriving from the \(\beta\) value to achieve an efficient search process of the algorithm. Parameter \(\rho\): This parameter indicates the volatility coefficient of pheromones, which determines the rate of decay of pheromones along the path. The volatilization of pheromones is an important step in the ant colony algorithm, which helps to prevent the algorithm from becoming stuck in local optima and promotes ants to explore other paths. When the value of \(\rho\) is large, pheromone decay is faster, which helps ants discover new and better paths more quickly [26]. However, an excessively large \(\rho\) value may cause pheromones to quickly disappear on the path, making it difficult for ants to find a stable and optimal path [27]. Therefore, it is necessary to choose the appropriate one based on the characteristics and requirements of the problem. The \(\rho\) value is used to achieve the stability and efficiency of the algorithm [28]. Parameters \(\alpha\), \(\beta\), \(and\)\(\rho\). The data size setting is not supported by a comprehensive mathematical model. It is necessary to comprehensively consider factors such as the characteristics of the problem, the complexity of the network environment, and the performance requirements of the algorithm [29]. Through reasonable parameter settings and adjustments and multiple experimental optimizations, the efficient, stable, and accurate performance of ant colony optimization-based routing algorithms can be achieved [30].