# Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique
### Related Work
On the other hand, although random deployment provides a suboptimal approach that is easy to implement, it often leads to imbalanced deployment, requiring additional sensors to achieve the same level of coverage as deterministic deployment. To address these shortcomings, researchers have introduced advanced algorithms such as the whale colony algorithm and ant colony optimization algorithm to deploy nodes. For example, a recent study proposed a new whale swarm optimization algorithm that transforms the original network coverage optimization problem into a multi-objective optimization problem. By combining this algorithm with a BP neural network, the initial population distribution is more ideal and the search ability of nodes is enhanced. Meanwhile, the ant colony optimization algorithm (ACOD) developed for mobile aggregation nodes provides an optimal trajectory, with distance being the determining factor. Despite these advancements in node deployment, existing technologies still suffer from drawbacks such as low energy efficiency, shortened network lifespan, and hot issues that adversely affect network performance. Each of these challenges requires a comprehensive approach that can provide reliable, scalable, and energy-efficient alternatives for routing in wireless sensor networks, laying the foundation for our current research. Gap analysis  
Due to the potential for improving efficiency in wireless sensor networks, they hold great potential in various application fields. However, despite extensive research on this topic, there are still some challenges that have not been resolved. Firstly, existing methods for optimizing WSN performance using algorithmic techniques require sensor nodes to be in a working or dormant state, with the latter consuming less energy. However, this method does not take into account the energy conversion that occurs during the state transition process, nor does it take into account the energy waste that may occur when the node is in a dormant state for a long time. Therefore, there is a knowledge gap in how to best balance energy use between active and dormant states and optimize performance. In addition, although the use of mobile aggregation nodes in traditional optimization methods has been proven to prolong network lifespan, it cannot effectively manage \"hotspot\" issues. These strategies also do not take into account the impact of the distance between CHs and mobile aggregation nodes on the overall performance of the network. Traditional ant colony algorithms only use current information to guide ants to subsequent nodes, which leads to them deviating from the target over time. Therefore, there is a gap in developing a technique that incorporates distance factors into finding the optimal movement trajectory for mobile aggregation nodes. The key is to design a solution that optimizes the initial population distribution to improve global search efficiency and ultimately improve network coverage [15]. The whale algorithm is a swarm intelligence-based algorithm, inspired by the collective behavior of whales. Whales form a group when swimming, and they cooperate with each other to achieve common goals through mutual coordination. This behavior can be seen as a form of collective intelligence, as they can achieve complex tasks through mutual communication and collaboration [16]. In the process of information transmission in wireless sensor networks, there may be obstacles or signal interference that affect coverage. To achieve this task, several nodes in wireless sensor networks, like whale schools, need to perceive the surrounding environment in real time and be able to make quick decisions. Therefore, this algorithm process adopts the whale algorithm to achieve fast and accurate decision-making [17]. The first step in the algorithm process is to establish an environmental model. This model includes the location, speed, and information content of several nodes in a wireless sensor network, as well as the location and size of obstacles in the surrounding environment [18].