# Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique
### Goals and Contributions  
The main purpose of this study is to propose and implement a comprehensive model for optimizing network performance based on wireless sensor networks. This study aims to address key issues that plague modern wireless sensor networks, such as low energy efficiency, hotspot issues, and a shortened network lifespan, which can significantly reduce overall network performance. To alleviate these issues, the model integrates ant colony optimization-based routing, K-means clustering, and an improved whale algorithm.  
Specifically, our proposed model divides the network into different clusters, each led by a CH, and uses a combination of the K-means algorithm and whale algorithm (WOA) to determine the optimal node layout with the optimal number of CHs, cluster nodes, and link quality. In order to balance the load of wireless sensor nodes, the ACOD is applied to determine the optimal path for mobile aggregation nodes to communicate with CHs, while considering the distance between them. This method greatly improves the energy-saving effect of the network in the communication process. Finally, in order to expand coverage, we adopted an upgraded whale algorithm [8].  
The prominent feature of this article is that it effectively solves the clustering, routing, and network coverage problems in wireless sensor networks simultaneously. The implementation of a new individual coding mechanism has optimized the interaction between the two. In addition, considering real-world network coverage and communication models, the accuracy of performance evaluation has been improved. This article also conducts critical research on different types of networks based on sensor energy, homogeneous networks and heterogeneous networks, and conducts important simulation experiments on several famous clustering protocols.  
The main contributions of this study can be summarized as follows:  
1. Solving the coverage optimization problem of WSNs (wireless sensor networks):  
We explore how to effectively deploy sensor nodes while ensuring sensor network connectivity to maximize the coverage of monitoring areas and avoid blind spots and node redundancy. This article studies the WOA-BP coverage algorithm, which achieves a more uniform node distribution and higher coverage.
2. Solving energy consumption and optimization  
Sensor nodes in WSNs are usually powered by batteries and have limited energy. A large amount of data perception, processing, and transmission leads to rapid energy consumption, reducing the lifecycle of sensors.  
This article adopts the K-means algorithm for routing. By improving the clustering method between sensor nodes, the optimal path for data transmission is achieved, and the energy consumption of nodes is reduced.
3. Solving data processing and performance optimization  
Due to the large scale of nodes in WSNs, a huge amount of data is generated, and the types of data are diverse, including numbers, text, images, and videos. This leads to network congestion, high storage costs, and insufficient upload bandwidth. This article adopts the ant colony algorithm (ACOD) to calculate the optimal routing algorithm for WSNs, optimize network protocol design, and improve network transmission efficiency.