Abstract

Soil temperature wireless sensor networks provide essential data for continuous soil temperature monitoring in cotton fields and support agricultural environmental regulation and crop growth management. However, conventional sensor node deployment methods often lead to coverage blind spots, redundant coverage, and insufficient utilization of sensing resources, which restrict network monitoring performance. To address these issues, this study proposes an Adaptive Chaotic Lévy Flight Prairie Dog Optimization algorithm, named ACLFPDO, for optimizing node deployment in soil temperature wireless sensor networks. The incremental novelty of ACLFPDO does not lie in the individual use of chaotic initialization, adaptive parameter adjustment, or Lévy-flight perturbation, which have been widely used in metaheuristic optimization, but in coupling these strategies with a stage-based Prairie Dog Optimization (PDO) position-updating framework and a coverage-oriented fitness design tailored to the STWSN area-coverage problem. An idealized two-dimensional simulation model was established, in which the cotton-field monitoring region was simplified as a regular square area and each sensor node was modeled using a fixed-radius circular binary sensing model. Coverage rate and node utilization efficiency were used as the main evaluation metrics. Comparative simulations were conducted against Prairie Dog Optimization, Snake Optimization (SO), and Whale Optimization algorithms (WO). Under a monitoring area side length of 50, sensing radius of 5, and 42 sensor nodes, ACLFPDO achieved a coverage rate of 98.49% and a node utilization efficiency of 74.65%. Compared with PDO, SO, and WO, the coverage rate increased by 16.88, 9.52, and 10.86 percentage points, respectively. The results indicate that ACLFPDO can improve coverage performance and sensing resource utilization under idealized simulation conditions. However, practical cotton-field deployment still requires further consideration of irregular boundaries, ridges, furrows, obstacles, burial-depth differences, communication connectivity, energy consumption, and soil spatial heterogeneity.

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