# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 1 Introduction  
Wireless sensor networks (WSNs) is a multi-hop self-organizing network composed of a large number of sensor nodes used for monitoring physical events [1]. Due to their outstanding performance advantages, they play a crucial role in various fields such as environmental monitoring, healthcare, agriculture, and urban traffic [2; 3; 4; 5], contributing significantly to the development and application of Internet of Things (IoT) technology. Coverage optimization is an important research direction in the field of WSNs. The distribution of nodes within the monitoring area, among other factors, determines the sensing coverage capability of WSNs. Additionally, the sensing and monitoring capabilities within the monitoring area determine whether the WSNs can provide effective monitoring services. In practical applications, when deploying wireless sensor nodes in complex environments such as forests or oceans that are difficult for humans to access, the typical approach is to deploy them in a random manner, which can lead to issues such as decreased network performance and resource wastage. Deploying mobile nodes is a commonly used coverage optimization method to improve network coverage, but this approach significantly increases deployment costs. Therefore, designing appropriate node deployment strategies is crucial for effectively enhancing the monitoring performance and data transmission qualityof WSNs while keeping energy consumption at an acceptable level. This plays a pivotal role in the future development of the entire WSNs application landscape. The application of swarm intelligence algorithms and their improved algorithms in the field of WSNs coverage optimization is quite common. Scholars continuously analyze and research swarm intelligence algorithms and have proposed various bio-inspired algorithms. These include the Particle Swarm Optimization (PSO) [6] algorithm, which simulates the foraging behavior of bird flocks; the Flower Pollination Algorithm (FPA) [7], which mimics the pollination process of flowering plants in nature; the Fruit Fly Optimization Algorithm (FOA) [8], which is based on the foraging behavior of fruit flies; the Firefly Optimization Algorithm (FA) [9], which imitates fireflies' aggregation through bioluminescence; the Grey Wolf Algorithm (GWO) [10], inspired by the hierarchical hunting behavior of wolf packs; the Whale Optimization Algorithm (WOA) [11], derived from the behavior of whales capturing food; The salp swarm algorithm, inspired by the aggregative behavior of salp swarm [12], and various related improved or hybrid algorithms. However, existing research on coverage optimization for network coverage can only ensure a more reasonable distribution of nodes, with relatively limited improvements in network performance. Comprehensive coverage optimization studies for WSNs, considering objectives such as network coverage, connectivity, and node energy consumption, are more practically valuable. Typically, multiple-objective optimization algorithms are used to comprehensively optimize these multiple objectives. Most existing research on WSNs coverage optimization has overlooked the variability in coverage requirements within the monitoring target area [13; 14; 15; 16; 17]. It generally assumes that the monitoring area has uniform coverage requirements. However, in real-world scenarios where different levels of monitoring coverage are needed, this assumption may not hold. This can lead to partial coverage issues when users specify their required coverage thresholds based on their actual business needs. Nonetheless, in many cases, partial coverage is sufficient to meet business requirements and can be achieved with fewer active nodes, thus extending the network's lifespan. To address the challenge of balancing network coverage, node utilization, and node energy consumption in WSNs coverage optimization, this paper introduces an enhanced multi-objective salp swarm algorithm (EMSSA).