Research on Multi-Hop Path Optimization of UAV Based on Quality Factor and Neo4j

With the wide application of UAVs in military, how to ensure stable communication link in harsh environment becomes an important problem. Especially under the influence of electronic interference and complex terrain, a single UAV cannot provide reliable communication. To solve this problem, a multi-hop communication path optimization method based on quality factor and Neo4j knowledge graph is proposed in this paper. The method considers the signal strength, spectrum utilization, interference, modulation mode and other factors, sets the quality factor to evaluate the path, and uses the Neo4j graph database to calculate the optimal communication path efficiently. By setting the threshold reasonably and optimizing the path selection, the throughput, connectivity and anti-interference ability of the multi-hop communication network are significantly improved. The experimental results show that this method can effectively improve the communication throughput in most cases, especially in complex environments, showing good performance and reliability.

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Publication Details

Published
2024-12-27
DOI
https://doi.org/10.1109/icse63502.2024.10899081
Primary Topic
Robotic Path Planning Algorithms
Type
article
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article

Research on Multi-Hop Path Optimization of UAV Based on Quality Factor and Neo4j

J. Y. Liu, Beiming Li, Qingling Liu
Robotic Path Planning Algorithms
article

Research on Multi-Hop Path Optimization of UAV Based on Quality Factor and Neo4j

J. Y. Liu, Beiming Li, Qingling Liu
article en

Abstract

With the wide application of UAVs in military, how to ensure stable communication link in harsh environment becomes an important problem. Especially under the influence of electronic interference and complex terrain, a single UAV cannot provide reliable communication. To solve this problem, a multi-hop communication path optimization method based on quality factor and Neo4j knowledge graph is proposed in this paper. The method considers the signal strength, spectrum utilization, interference, modulation mode and other factors, sets the quality factor to evaluate the path, and uses the Neo4j graph database to calculate the optimal communication path efficiently. By setting the threshold reasonably and optimizing the path selection, the throughput, connectivity and anti-interference ability of the multi-hop communication network are significantly improved. The experimental results show that this method can effectively improve the communication throughput in most cases, especially in complex environments, showing good performance and reliability.

Harbin Engineering University (CN)
Openalex Percentile: Top 72%
Robotic Path Planning Algorithms
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