An Enhanced Food Digestion Algorithm for Mobile Sensor Localization.

Shu-Chuan Chu, Zhi-Yuan Shao, Ning Zhong, Geng-Geng Liu, Jeng-Shyang Pan

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In one paragraph

Article in Sensors (Basel, Switzerland), 2023. 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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5 · Who and what money

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5 authors.

Shu-Chuan ChuCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.ORCID 0000-0003-2117-0618

Zhi-Yuan ShaoCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

Ning ZhongDepartment of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0816, Japan.ORCID 0000-0001-7882-8340

Geng-Geng LiuCollege of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China.ORCID 0000-0002-3099-4371

Jeng-Shyang PanCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.ORCID 0000-0002-3128-9025

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mobile sensors can extend the range of monitoring and overcome static sensors' limitations and are increasingly used in real-life applications. Since there can be significant errors in mobile sensor localization using the Monte Carlo Localization (MCL), this paper improves the food digestion algorithm (FDA). This paper applies the improved algorithm to the mobile sensor localization problem to reduce localization errors and improve localization accuracy. Firstly, this paper proposes three inter-group communication strategies to speed up the convergence of the algorithm based on the topology that exists between groups. Finally, the improved algorithm is applied to the mobile sensor localization problem, reducing the localization error and achieving good localization results.

Indexed as

compact strategyfood digestion algorithmmobile sensorsMonte Carlo Localizationparallel strategy

Identifiers

PMID37687962

DOI10.3390/s23177508

PMCPMC10490790

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