# Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique
###### Abstract  
A wireless sensor network (WSN) is a wireless network composed of sensor nodes, widely used in military, industrial, and agricultural fields. In these practical application scenarios, due to the limited routing search ability of wireless sensor nodes and the small coverage range of a WSN, it faces the problem of a relatively short network lifecycle. Therefore, increasing a WSN's coverage range and reducing node energy consumption are of high value. This article proposes a method of using the ant colony algorithm (ACOD) to calculate the optimal routing of a WSN, the K-means algorithm for clustering and the whale algorithm (WOA) and a backpropagation (BP) neural network to increase the coverage range of a WSN. After multiple experiments, the total energy consumption of WSN nodes has been effectively reduced, the coverage range of the WSN has been increased, and the lifecycle of the WSN has been extended. wireless sensor network; ant colony; K-means; whale optimization algorithm (WOA) 2024 14 5273 1-11 1  
sensors [5]. However, current methods find it difficult to balance the number of sensors with the number of cluster nodes and the quality of the link between sensor nodes. A method of combining the K-means algorithm with the whale algorithm (WOA) has been proposed to optimize these challenges and provide a more realistic computational model [6]. As optimizing WSN performance is crucial for fully unleashing the potential of the Internet of Things, this article provides a comprehensive method for optimizing network coverage, routing, and clustering. We propose a holistic model that integrates the K-means algorithm for routing, the whale algorithm (WOA), and a BP neural network for the improved whale algorithm with maximum coverage [7]. The contribution of this study lies in developing an energy-saving, reliable, and scalable routing method, optimizing the performance of WSNs in terms of basic indicators, and developing a scalable network optimization model. The rest of this article provides a detailed introduction to the developed model, its implementation in a simulation environment, and an evaluation and comparison of performance indicators between our proposed model and traditional methods. In the past few decades, wireless sensor networks have become a critical component in many applications, including military operations, public safety systems, environmental monitoring, healthcare, and more. This increasing correlation is due to the numerous advantages of wireless sensor networks, including their self-organizing ability, distributed network characteristics, and widespread application efficiency. However, the inherent characteristics of a single sensor node, such as limited power supply and transmission range, still pose a daunting challenge in designing effective and efficient routing protocols for wireless sensor networks. The basic premise of a typical cluster-based architecture is to divide the network runtime into several rounds, each consisting of three stages--cluster head (CH) selection, cluster formation, and data transmission. Although there have been some successful implementations, some current clustering protocols face limitations due to their inability to accurately simulate clustering in sensor networks, which may lead to unrealistic estimates of power consumption. In addition, reliability issues often arise from protocols relying solely on the distance between two nodes as an indicator of their link quality, thereby ignoring the inherent asymmetric characteristics of WSNs. In addition, algorithms that maximize network coverage play an important role in extending network lifespan, enhancing network load balancing, and improving network robustness, which has aroused great interest. Various strategies have been proposed to increase coverage, but achieving maximum coverage on a limited number of sensors remains a research hotspot in the field of wireless sensor networks.