# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 2 Related Works  
In recent years, researchers have conducted extensive work on coverage optimization in WSNs, achieving significant advancements. Swarm intelligence optimization algorithms possess several advantages, such as strong search capabilities and inherent randomness. Utilizing intelligent optimization algorithms in coverage optimization, based on these algorithms, has become increasingly common across various domains, including the optimization deployment of wireless sensor nodes. In order to enhance the Quality of Service (QoS) in WSNs and extend network lifespan, [PERSON] et al. [13] improved the classical multi-objective Ant Lion Optimization (MOALO) algorithm and proposed an enhanced MOALO algorithm based on fast non-dominated sorting (NSIMOALO). [PERSON] et al. [14], addressing the issue of nodes deviating from the optimal deployment positions in complex monitoring areas like battlefields and disaster zones, resulting in coverage holes, introduced a WSNs coverage enhancement strategy based on Virtual Force-directed Ant Lion Optimization (VF-IALO). Experimental results demonstrated the effectiveness of the proposed improved algorithms. [PERSON] et al. [15], addressing the optimization problem of maximizing coverage. Inspired by the predation behavior of Army Ants, this paper introduces a novel swarm intelligence (SI) technique called the Army Ant Search Optimizer (AASO) to tackle the problem of maximizing coverage. Simulation results demonstrate that WMSNs (Wireless Multimedia Sensor Networks) enhanced with AASO exhibit superior coverage performance. To balance coverage and average node movement distance, [PERSON] et al. [16] combined Virtual Force Algorithm (VFA) with Grey Wolf Optimizer (GWO) and proposed the VFA-based Levy Flight GWO for coverage optimization in WSNs. Simulation experiments demonstrated the algorithm's strong applicability as the number of nodes and monitoring area size varied. Addressing the issue of heterogeneous node deployment optimization in WSNs with obstacles in the monitoring area, [PERSON] et al. [17] introduced two new Flower Pollination Algorithms (FPA) for network deployment. Simulation experiments indicated that both improved algorithms could provide better solutions for WSNs deployment. [PERSON] et al. [18] proposed a novel approach for optimizing node placement, referred to as MOONGA (Multi-Objective Optimization for WSNs using Genetic Algorithms). It can generate optimal deployments based on topology, environment, specifications of different applications, as well as the preferences of network designers and users. [PERSON] et al. [19] introduced a Bee algorithm to maximize network coverage area, significantly expanding the area covered while reducing coverage gaps. However, the algorithm exhibits poor convergence. [PERSON] et al. [20], in response to the issues of network coverage gaps and high deployment costs, proposed an improved Genetic Algorithm-based dynamic deployment technique that achieves maximum target area coverage with the fewest nodes. Addressing the problems of high coverage costs and node failures leading to inadequate monitoring, [PERSON] et al. [21] presented an efficient deployment model based on a probabilistic sensing model and the Harmony Search algorithm. This model balances coverage performance and deployment costs. [PERSON] et al. [22] introduced a novel model combining Genetic Algorithms to meet WSNs coverage requirements. This model aims to monitor targets for as long as possible with limited energy, but it has a relatively high algorithmic complexity. [PERSON] et al. [23] proposed a strategy based on weighted distance location updates, known as the Weighted Salp Swarm Algorithm (WSSA). Simulation results have confirmed the improved performance and convergence speed of WSSA. Furthermore, the WSSA method has been applied to a probabilistic sensor model to maximize coverage range and to a wireless energy model to minimize energy consumption.