An Improved DV‐Hop Localization Algorithm in Anisotropic‐Based Wireless Sensor Network Using Domination Method and Q‐Learning‐Based Crayfish Optimization

ABSTRACT Sensor localization is a crucial factor in ensuring reliable operation in wireless sensor network (WSN). The existing research on localization focuses more under isotropic conditions, assuming homogeneous environments with uniformly distributed nodes. In contrast, real‐world deployments frequently exhibit Anisotropic WSN (AWSN) characteristics, including irregular topologies, obstacles, and non‐uniform connectivity, which significantly challenge the localization process. Among many localization methods, the DV‐Hop algorithm is widely used approach for its cost‐effectiveness and easy implementations. However, the DV‐Hop algorithm performs poorly when there is an obstacle encountered; the average hop distance might deteriorate, and it is hard to locate the coordinates of the location of unknown nodes. In order to achieve better localization accuracy with cost‐effective, this study proposes Domination‐based Anchor Placement for AWSNs (DAPA) to obtain minimum anchor requirement and optimal anchor placement. Further, a Q‐learning‐based Crayfish Optimization Algorithm (QCOA) is proposed to enhance the DV‐Hop algorithm localization accuracy. The proposed QCOA was compared using 10 benchmark functions with some similar existing optimization algorithms. The proposed DAPA approach was compared with random anchor deployment method using three variants of DV‐Hop algorithms. DAPA outperform the random anchor deployment in each three comparisons. Further, the proposed DAPA‐based QCOA algorithm simulated in C‐, S‐, and O‐shaped topologies based on localization error from varying the AWSN metrics. The proposed algorithm achieves high localization accuracy among the existing algorithms.