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A wireless sensor node deployment scheme based on embedded virtual force resampling particle swarm optimization algorithm
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DOI：10.1007/s10489-021-02745-0.png)
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Abstract
En 中文
In recent years, wireless sensor network (WSN) has been widely used in many fields. Network coverage is the basis of providing perception services and collecting location information and has become one of the hot topics. For node deployment, this paper proposes two algorithms. One is an improved virtual force (VF) algorithm. The virtual forces of nodes include repulsive force between nodes and repulsive force at the boundary. The improved VF algorithm sets the virtual force threshold. The other is the resampling particle swarm optimization algorithm embedded with virtual force (RPSO-DV). The algorithm combines the advantages of three algorithms, including resampling particle swarm optimization (RPSO) algorithm, particle swarm optimization algorithm based on coefficient adjustment (PSO-D) and improved VF algorithm. In this paper, the two proposed algorithms and reference algorithms in the pieces of literature and are simulated and compared. Firstly, this paper compares the impact of different node numbers and deployment modes on coverage performance in the improved VF algorithm. The simulation shows that the improved VF algorithm can make the network reach a stable state quickly and achieve a high coverage rate. Secondly, this paper lists the confidence intervals for the coverage rate of multiple algorithms at the significance level of 0.05. At the same time, we analyze the specific coverage rate curves and deployment diagrams. The simulation results show that our proposed RPSO-DV algorithm improves the diversity of the population and speeds up the convergence speed. Compared with other reference algorithms, the RPSO-DV algorithm has the highest coverage rate. Finally, this paper analyzes the sensitivity of the parameters of the proposed RPSO-DV algorithm. According to the orthogonal experiment design method, we design 64 sets of experiments. The simulation results show that the algorithm has a certain tolerance and robustness to parameter values.
Keywords:
Resampling particle swarm optimization
Virtual force
Node deployment
Coverage optimization
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