Enhanced sparrow search algorithm with DV-Hop for high-precision fire sensor localization in underground spaces

Abstract

Fire monitoring in underground spaces is critical for emergency response, yet traditional localization methods like DV-Hop suffer from significant localization errors due to hop count ambiguity and premature convergence in optimization. To address these issues, we propose an Enhanced Sparrow Search Algorithm with Improved DV-Hop (ESSADV-Hop) method. The method incorporates a golden ratio-based communication radius division strategy to refine hop count granularity and an enhanced sparrow search algorithm with Gaussian perturbations to escape local optima. Experimental results show that ESSADV-Hop reduces the average localization error by 55.7% compared to DV-Hop (from 0.2910 to 0.1288) and outperforms other variants by 11.74%∼23.05% in accuracy, demonstrating its effectiveness for fire sensor localization in complex underground environments.