Reliable task allocation for soil moisture wireless sensor networks using differential evolution adaptive elite butterfly optimization algorithm

Haitao Huang; Min Tian; Jie Zhou; Xiang Liu; Haitao Huang; Min Tian; Jie Zhou; Xiang Liu

doi:10.3934/mbe.2023656

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 14675-14698. doi: 10.3934/mbe.2023656

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Reliable task allocation for soil moisture wireless sensor networks using differential evolution adaptive elite butterfly 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: 15 March 2023 Revised: 06 June 2023 Accepted: 07 June 2023 Published: 06 July 2023

Wireless sensor technology advancements have made soil moisture wireless sensor networks (SMWSNs) a vital component of precision agriculture. However, the humidity nodes in SMWSNs have a weak ability in information collection, storage, calculation, etc. Hence, it is essential to reasonably pursue task allocation for SMWSNs to improve the network benefits of SMWSNs. However, the task allocation of SMWSNs is an NP (Non-deterministic Polynomial)-hard issue, and its complexity becomes even higher when constraints such as limited computing capabilities and power are taken into consideration. In this paper, a novel differential evolution adaptive elite butterfly optimization algorithm (DEAEBOA) is proposed. DEAEBOA has significantly improved the task allocation efficiency of SMWSNs, effectively avoided plan stagnation, and greatly accelerated the convergence speed. In the meantime, a new adaptive operator was designed, which signally ameliorates the accuracy and performance of the algorithm. In addition, a new elite operator and differential evolution strategy are put forward to markedly enhance the global search ability, which can availably avoid local optimization. Simulation experiments were carried out by comparing DEAEBOA with the butterfly optimization algorithm (BOA), particle swarm optimization (PSO), genetic algorithm (GA), and beluga whale optimization (BWO). The simulation results show that DEAEBOA significantly improved the task allocation efficiency, and compared with BOA, PSO, GA, and BWO the network benefit rate increased by 11.86%, 5.46%, 8.98%, and 12.18% respectively.
- task allocation,
- precision agriculture,
- soil moisture wireless sensor networks,
- algorithm,
- network benefit
Citation: Haitao Huang, Min Tian, Jie Zhou, Xiang Liu. Reliable task allocation for soil moisture wireless sensor networks using differential evolution adaptive elite butterfly optimization algorithm[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14675-14698. doi: 10.3934/mbe.2023656

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Abstract

Wireless sensor technology advancements have made soil moisture wireless sensor networks (SMWSNs) a vital component of precision agriculture. However, the humidity nodes in SMWSNs have a weak ability in information collection, storage, calculation, etc. Hence, it is essential to reasonably pursue task allocation for SMWSNs to improve the network benefits of SMWSNs. However, the task allocation of SMWSNs is an NP (Non-deterministic Polynomial)-hard issue, and its complexity becomes even higher when constraints such as limited computing capabilities and power are taken into consideration. In this paper, a novel differential evolution adaptive elite butterfly optimization algorithm (DEAEBOA) is proposed. DEAEBOA has significantly improved the task allocation efficiency of SMWSNs, effectively avoided plan stagnation, and greatly accelerated the convergence speed. In the meantime, a new adaptive operator was designed, which signally ameliorates the accuracy and performance of the algorithm. In addition, a new elite operator and differential evolution strategy are put forward to markedly enhance the global search ability, which can availably avoid local optimization. Simulation experiments were carried out by comparing DEAEBOA with the butterfly optimization algorithm (BOA), particle swarm optimization (PSO), genetic algorithm (GA), and beluga whale optimization (BWO). The simulation results show that DEAEBOA significantly improved the task allocation efficiency, and compared with BOA, PSO, GA, and BWO the network benefit rate increased by 11.86%, 5.46%, 8.98%, and 12.18% respectively.

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