RAIL: An Accurate and Fast Angle-inferred Localization Algorithm for UAV-WSN Systems

TL;DR

This paper introduces RAIL, a fast and accurate UAV-WSN localization algorithm that infers angles from RSSI without extra hardware, significantly improving accuracy over existing methods.

Contribution

The paper presents a novel angle-inference localization algorithm that enhances accuracy and efficiency without additional hardware in UAV-WSN systems.

Findings

Reduces average localization error by 72.4%

Outperforms Min-Max and RSSI-based DV-Hop algorithms

Effective in diverse scenarios with varying node counts

Abstract

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.