# Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique
### Related Work  
Recently, countless technologies have been proposed to improve the performance of wireless sensor networks [9]. Among them, clustering and routing optimization have become key methods that have made significant contributions to network lifespan and efficiency [10]. Various protocols, such as the Low-Energy Adaptive Clustering Protocol (LEACH) and its variants, namely the Enhanced Heterogeneous LEACH Protocol (EHE-LEACH) for extending lifespan, the Single-Hop Energy-Saving Clustering Protocol (S-EECP), and the Multi-Hop Energy-Saving Clustering Protocol (M-EECP), have been developed to optimize clustering based on local information of SNs. These protocols use probability calculations to elect CHs and ensure the distribution of energy loads between clusters. However, their decision-making process is limited by limited local information and often excludes the selection of the optimal CH set. To improve these protocols, the literature proposes using clustering protocols based on evolutionary algorithms (EAs). These protocols utilize the global knowledge of the network and optimize CH selection using predefined objective functions. For example, the LEACH-C protocol is optimized using the simulated annealing (SA) method, while the clustering protocol based on particle swarm optimization (PSO-C) and the CH protocol based on particle swarm optimization (PSO-CH) use PSO technology, which considers EE, data transmission reliability, and scalability in the EC model [11]. At the same time, some people have focused on combining clustering and routing, resulting in two-layer protocols such as TPSO-CR and iCSHS. The TPSO-CR protocol attempts to solve inter-cluster communication problems through a two-layer approach, which determines the optimal routing tree and CH. However, the iCSHS protocol uses optimization algorithms based on cuckoo searching and routing algorithms based on improved harmonious searching. However, these protocols also bring their own challenges, including potential higher EC due to an increase in the number of active nodes and extended execution time, which requires further research [12]. In addition, significant progress has been made in the routing protocols of wireless sensor networks, such as the Mobile Convergence-Based Routing Protocol (MSRP) and the Energy-Saving Routing Protocol based on PEGASIS, which are being developed to alleviate the problem of low energy efficiency. The emergence of mobile convergence points, combined with ant colony optimization techniques, has provided significant improvements in EE and network lifespan [13]. Despite these efforts, the challenge of optimizing network performance in WSNs still exists. Specifically, most protocols assume that the SN is location aware, which is unrealistic in practical situations. In addition, using traditional first-order radio models for EC calculations often leads to overly idealized estimates, ignoring a considerable amount of EC from monitoring activities. Therefore, a realistic and comprehensive model is needed to optimize network performance in WSNs to address these issues. Overview of Previous Research  
In recent years, wireless sensor networks have undergone tremendous development and have been widely used in various social and military applications, including habitat monitoring, disaster intervention, medical care, production equipment status recognition, and emergency search and rescue. The functionality and efficiency of these applications largely depend on the coverage range of wireless sensor networks, which in turn depends on the effective deployment of sensor networks. However, traditional node deployment algorithms, including deterministic and random deployments, have inherent limitations [14]. In deterministic deployment, the position of nodes is selectively determined to achieve specific deployment goals, providing maximum coverage with a limited number of sensors. However, the node selection process is often plagued by computational complexity, especially in large-scale networks.