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

Jiang Li, Liliang Dong, Le Xu, Fangqiong Luo, Zhenkun Lu, Yajian Huang, Shenghan Wei

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Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Jiang LiSchool of Electrical Engineering, Hunan Polytechnic of Water Resources and Electric Power, Changsha, Hunan, China.

Liliang DongSchool of Mathematics and Computer Engineering, Guangxi Science & Technology Normal University, Laibin, Guangxi, China.ORCID https://orcid.org/0009-0001-9541-4411

Le XuSchool of Computer Science and Technology, Guizhou University, Guiyang, Guizhou, China.

Fangqiong LuoSchool of Mathematics and Computer Engineering, Guangxi Science & Technology Normal University, Laibin, Guangxi, China.

Zhenkun LuSchool of Electronic Information, Guangxi Minzu University, Nanning, Guangxi, China.

Yajian HuangSchool of Electrical Engineering, Hunan Polytechnic of Water Resources and Electric Power, Changsha, Hunan, China.

Shenghan WeiSchool of Mathematics and Computer Engineering, Guangxi Science & Technology Normal University, Laibin, Guangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AlgorithmsFires

Identifiers

PMID41406150

DOI10.1371/journal.pone.0338706

PMCPMC12711067

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