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

2025-06-01

AI Summary

To address the challenge of achieving high-accuracy, low-overhead localization for multifunctional nodes in hardware-constrained UAV-enabled wireless sensor networks (UAV-WSNs), this paper proposes a purely software-based, hardware-free localization method that synergistically integrates RSSI-derived angular inference with geometric bounding-box optimization. We introduce a novel RSSI-based angular estimation algorithm grounded in signal attenuation modeling, eliminating reliance on dedicated ranging or angle-measurement hardware. Furthermore, we design a lightweight, angle-constrained geometric bounding-box optimization mechanism to enable fast, robust infrastructure-free cooperative localization. Evaluated across three representative scenarios, our approach reduces average localization error by 72.4% compared to Min-Max and DV-Hop, significantly improving both accuracy and real-time performance. The method is specifically tailored for resource-limited environments, ensuring practicality and deployability without additional hardware overhead.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Accurate localization of UAV-WSN nodes without extra hardware
• Angle estimation using RSSI for improved node positioning
• Enhanced accuracy in constrained environments via angular relationships

Innovation

• Angle-inferred localization using RSSI signals
• No additional hardware required for implementation
• Bounding box strategy enhances accuracy