RAIL: An Accurate and Fast Angle-inferred Localization Algorithm for UAV-WSN Systems
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918042686980096 |
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| author | Zhang, Ze Dong, Qian |
| author_facet | Zhang, Ze Dong, Qian |
| contents | Location information is a fundamental requirement for unmanned aerial vehicles (UAVs) and other wireless sensor networks (WSNs). However, accurately and efficiently localizing sensor nodes with diverse functionalities remains a significant challenge, particularly in a hardware-constrained environment. To address this issue and enhance the applicability of artificial intelligence (AI), this paper proposes a localization algorithm that does not require additional hardware. Specifically, the angle between a node and the anchor nodes is estimated based on the received signal strength indication (RSSI). A subsequent localization strategy leverages the inferred angular relationships in conjunction with a bounding box. Experimental evaluations in three scenarios with varying number of nodes demonstrate that the proposed method achieves substantial improvements in localization accuracy, reducing the average error by 72.4% compared to the Min-Max and RSSI-based DV-Hop algorithms, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00766 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RAIL: An Accurate and Fast Angle-inferred Localization Algorithm for UAV-WSN Systems Zhang, Ze Dong, Qian Networking and Internet Architecture Location information is a fundamental requirement for unmanned aerial vehicles (UAVs) and other wireless sensor networks (WSNs). However, accurately and efficiently localizing sensor nodes with diverse functionalities remains a significant challenge, particularly in a hardware-constrained environment. To address this issue and enhance the applicability of artificial intelligence (AI), this paper proposes a localization algorithm that does not require additional hardware. Specifically, the angle between a node and the anchor nodes is estimated based on the received signal strength indication (RSSI). A subsequent localization strategy leverages the inferred angular relationships in conjunction with a bounding box. Experimental evaluations in three scenarios with varying number of nodes demonstrate that the proposed method achieves substantial improvements in localization accuracy, reducing the average error by 72.4% compared to the Min-Max and RSSI-based DV-Hop algorithms, respectively. |
| title | RAIL: An Accurate and Fast Angle-inferred Localization Algorithm for UAV-WSN Systems |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2506.00766 |