An efficient coverage method for SEMWSNs based on adaptive chaotic Gaussian variant snake optimization algorithm

Xiang Liu; Min Tian; Jie Zhou; Jinyan Liang; Xiang Liu; Min Tian; Jie Zhou; Jinyan Liang

doi:10.3934/mbe.2023150

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 2: 3191-3215. doi: 10.3934/mbe.2023150

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An efficient coverage method for SEMWSNs based on adaptive chaotic Gaussian variant snake optimization algorithm

1.
College of mechanical and electrical engineering, Shihezi University, Shihezi 832000, China
2.
College of information science and technology, Shihezi University, Shihezi 832000, China

Academic Editor: Wenqiang Zhang

Received: 08 October 2022 Revised: 21 November 2022 Accepted: 25 November 2022 Published: 02 December 2022

Soil element monitoring wireless sensor networks (SEMWSNs) are widely used in soil element monitoring agricultural activities. SEMWSNs monitor changes in soil elemental content during agriculture products growing through nodes. Based on the feedback from the nodes, farmers adjust irrigation and fertilization strategies on time, thus promoting the economic growth of crops. The critical issue in SEMWSNs coverage studies is to achieve maximum coverage of the entire monitoring field by adopting a smaller number of sensor nodes. In this study, a unique adaptive chaotic Gaussian variant snake optimization algorithm (ACGSOA) is proposed for solving the above problem, which also has the advantages of solid robustness, low algorithmic complexity, and fast convergence. A new chaotic operator is proposed in this paper to optimize the position parameters of individuals, enhancing the convergence speed of the algorithm. Moreover, an adaptive Gaussian variant operator is also designed in this paper to effectively avoid SEMWSNs from falling into local optima during the deployment process. Simulation experiments are designed to compare ACGSOA with other widely used metaheuristics, namely snake optimizer (SO), whale optimization algorithm (WOA), artificial bee colony algorithm (ABC), and fruit fly optimization algorithm (FOA). The simulation results show that the performance of ACGSOA has been dramatically improved. On the one hand, ACGSOA outperforms other methods in terms of convergence speed, and on the other hand, the coverage rate is improved by 7.20%, 7.32%, 7.96%, and 11.03% compared with SO, WOA, ABC, and FOA, respectively.
- coverage optimization,
- snake optimizer,
- chaotic operator,
- Gaussian variant,
- power consumption
Citation: Xiang Liu, Min Tian, Jie Zhou, Jinyan Liang. An efficient coverage method for SEMWSNs based on adaptive chaotic Gaussian variant snake optimization algorithm[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 3191-3215. doi: 10.3934/mbe.2023150

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Abstract

Soil element monitoring wireless sensor networks (SEMWSNs) are widely used in soil element monitoring agricultural activities. SEMWSNs monitor changes in soil elemental content during agriculture products growing through nodes. Based on the feedback from the nodes, farmers adjust irrigation and fertilization strategies on time, thus promoting the economic growth of crops. The critical issue in SEMWSNs coverage studies is to achieve maximum coverage of the entire monitoring field by adopting a smaller number of sensor nodes. In this study, a unique adaptive chaotic Gaussian variant snake optimization algorithm (ACGSOA) is proposed for solving the above problem, which also has the advantages of solid robustness, low algorithmic complexity, and fast convergence. A new chaotic operator is proposed in this paper to optimize the position parameters of individuals, enhancing the convergence speed of the algorithm. Moreover, an adaptive Gaussian variant operator is also designed in this paper to effectively avoid SEMWSNs from falling into local optima during the deployment process. Simulation experiments are designed to compare ACGSOA with other widely used metaheuristics, namely snake optimizer (SO), whale optimization algorithm (WOA), artificial bee colony algorithm (ABC), and fruit fly optimization algorithm (FOA). The simulation results show that the performance of ACGSOA has been dramatically improved. On the one hand, ACGSOA outperforms other methods in terms of convergence speed, and on the other hand, the coverage rate is improved by 7.20%, 7.32%, 7.96%, and 11.03% compared with SO, WOA, ABC, and FOA, respectively.

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