Coverage Optimization of Heterogeneous Wireless Sensor Network Based on Improved Wild Horse Optimizer

One of the most important challenges for heterogeneous wireless sensor networks (HWSNs) is adequate network coverage and connectivity. Aiming at this problem, this paper proposes an improved wild horse optimizer algorithm (IWHO). Firstly, the population's variety is increased by using the SPM chaotic mapping at initialization; secondly, the WHO and Golden Sine Algorithm (Golden-SA) are hybridized to improve the WHO's accuracy and arrive at faster convergence; Thirdly, the IWHO can escape from a local optimum and broaden the search space by using opposition-based learning and the Cauchy variation strategy. The results indicate that the IWHO has the best capacity for optimization by contrasting the simulation tests with seven algorithms on 23 test functions. Finally, three sets of coverage optimization experiments in different simulated environments are designed to test the effectiveness of this algorithm. The validation results demonstrate that the IWHO can achieve better and more effective sensor connectivity and coverage ratio compared to that of several algorithms. After optimization, the HWSN's coverage and connectivity ratio attained 98.51% and 20.04%, and after adding obstacles, 97.79% and 17.44%, respectively.

Automated summary

This paper proposes an improved wild horse optimizer algorithm (IWHO) to tackle the problem of network coverage and connectivity in heterogeneous wireless sensor networks (HWSNs). The IWHO combines the WHO algorithm and Golden Sine Algorithm (Golden-SA) to improve accuracy and convergence speed. It also incorporates opposition-based learning and the Cauchy variation strategy to avoid local optima and expand the search space. Experimental results demonstrate that the IWHO outperforms seven other algorithms in optimization capability. Validation experiments in different simulated environments show that the IWHO achieves better sensor connectivity and coverage ratio compared to other algorithms. After optimization, the HWSN achieves coverage and connectivity ratios of 98.51% and 20.04%, respectively, which decrease to 97.79% and 17.44% when obstacles are added.