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

Haitao Huang1, Min Tian1, Jie Zhou2

  • 1College of mechanical and electrical engineering, Shihezi University, Shihezi 832000, China.

Mathematical Biosciences and Engineering : MBE
|September 7, 2023
PubMed
Summary
This summary is machine-generated.

A new algorithm, DEAEBOA, enhances task allocation for soil moisture wireless sensor networks (SMWSNs) in precision agriculture. This optimization significantly boosts network benefits by overcoming computational and power limitations.

Keywords:
algorithmnetwork benefitprecision agriculturesoil moisture wireless sensor networkstask allocation

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Area of Science:

  • Agricultural Engineering
  • Computer Science
  • Optimization Algorithms

Background:

  • Advancements in wireless sensor technology have led to the development of soil moisture wireless sensor networks (SMWSNs) crucial for precision agriculture.
  • Existing SMWSN humidity nodes exhibit limitations in data collection, storage, and processing capabilities.
  • Efficient task allocation is essential to maximize the network benefits of SMWSNs, especially given their inherent constraints.

Purpose of the Study:

  • To address the NP-hard nature of task allocation in SMWSNs, particularly under constraints of limited computing power and energy.
  • To introduce a novel optimization algorithm, the differential evolution adaptive elite butterfly optimization algorithm (DEAEBOA), for improved task allocation.
  • To enhance the efficiency, accuracy, and convergence speed of task allocation in SMWSNs.

Main Methods:

  • Development of the differential evolution adaptive elite butterfly optimization algorithm (DEAEBOA).
  • Incorporation of a new adaptive operator to improve algorithm accuracy and performance.
  • Integration of a new elite operator and differential evolution strategy to enhance global search capabilities and avoid local optima.
  • Comparative simulation experiments against Butterfly Optimization Algorithm (BOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Beluga Whale Optimization (BWO).

Main Results:

  • DEAEBOA demonstrated significant improvements in task allocation efficiency for SMWSNs.
  • The algorithm effectively mitigated plan stagnation and accelerated convergence.
  • DEAEBOA achieved notable increases in the network benefit rate compared to existing algorithms: 11.86% over BOA, 5.46% over PSO, 8.98% over GA, and 12.18% over BWO.

Conclusions:

  • The proposed DEAEBOA algorithm offers a superior solution for task allocation in SMWSNs.
  • DEAEBOA effectively overcomes the computational and power constraints of SMWSN nodes.
  • The algorithm presents a promising approach to enhance the overall performance and efficiency of precision agriculture systems.