BeSound: Bluetooth-Based Position Estimation Enhancing with Cross-Modality Distillation
Hymalai Bello, Sungho Suh, Bo Zhou, Paul Lukowicz

TL;DR
This paper introduces BeSound, a Bluetooth-based worker localization system in smart factories that uses cross-modality distillation from ultrasound signals to improve BLE RSSI accuracy, ensuring privacy and scalability.
Contribution
The paper presents a novel knowledge distillation approach that enhances BLE-based localization accuracy by leveraging ultrasound signals without requiring additional hardware during inference.
Findings
11.79% increase in F1-score over baseline
Effective knowledge transfer from ultrasound to BLE models
Scalable, privacy-preserving worker localization solution
Abstract
Smart factories leverage advanced technologies to optimize manufacturing processes and enhance efficiency. Implementing worker tracking systems, primarily through camera-based methods, ensures accurate monitoring. However, concerns about worker privacy and technology protection make it necessary to explore alternative approaches. We propose a non-visual, scalable solution using Bluetooth Low Energy (BLE) and ultrasound coordinates. BLE position estimation offers a very low-power and cost-effective solution, as the technology is available on smartphones and is scalable due to the large number of smartphone users, facilitating worker localization and safety protocol transmission. Ultrasound signals provide faster response times and higher accuracy but require custom hardware, increasing costs. To combine the benefits of both modalities, we employ knowledge distillation (KD) from…
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Taxonomy
TopicsBluetooth and Wireless Communication Technologies · Indoor and Outdoor Localization Technologies · Inertial Sensor and Navigation
MethodsKnowledge Distillation
