When BLE Meets Light: Multi-modal Fusion for Enhanced Indoor Localization Jagdeep Singh jagdeep.singh@toshiba-bril.com Toshiba Europe Ltd., BRIL, UK Tim Farnham tim.farnham@toshiba-bril.com Toshiba Europe Ltd., BRIL, UK Qing Wang qing.wang@tudelft.nl Delft University of Technology, NL ABSTRACT Designing a reliable and highly accurate indoor localization system is challenging due to the non-uniformity of indoor spaces, multipath fading, and satellite signal blockage. To ad- dress these issues, we propose a Deep Neural Network-based localization system that combines passive Visible Light Posi- tioning (p-VLP) and Bluetooth Low Energy (BLE) technolo- gies to achieve stable, energy-efficient, and accurate indoor localization. Our solution leverages incremental learning to fuse data from visible light and BLE, overcoming their individual limitations and achieving centimeter-level local- ization accuracy. We build a prototype using low-cost S9706 hue sensors for p-VLP and low-power nrf52830 BLE boards to collect data simultaneously from both technologies in a 25m2 testbed. Our approach demonstrates a significant lo- calization accuracy improvement of approximately 47% and 64% compared to individual p-VLP and BLE technologies, respectively, achieving a mean localization error of 20 cm. CCS CONCEPTS • Networks → Location based services; • Computing method- ologies → Neural networks. KEYWORDS Multi-modal fusion, visible light positioning, Bluetooth 5.1 1 INTRODUCTION Building smart indoor environments has intensified the need to design more accurate indoor localization systems. Difficult Commercialization & Limited Usable Range. One promising technology for highly-accurate indoor lo- calization is Visible Light Positioning (VLP), which utilizes light’s directive properties for precise indoor localization. However, existing VLP systems perform well only in con- trolled environments. In real-world scenarios, factors like ex- ternal ambient light sources, obstacles, and shadows, decline localization performance, ultimately restricting the e ective localization areas. Additionally, active VLP systems have lim- ited market adoption due to luminaire design changes [5]. Limited Accuracy. In contrast, RF technologies like Blue- tooth Low Energy (BLE) have gained popularity for indoor localization due to their ubiquity and low-cost hardware. However, BLE’s narrowband nature makes it susceptible to multipath fading, limiting ranging accuracy. The angle of arrival/departure techniques can utilize signal phase infor- mation to overcome this. However, they require multiple antennas, typically sized 16 × 16 cm, which are bulky, expen- sive, and challenging to acquire commercially [3]. Hybrid Localization? To address these challenges, this paper investigates combining BLE and VLP to design a re- liable, accurate hybrid localization system. We aim to over- come BLE’s low accuracy and VLP’s limited usage range by leveraging both technologies’ strengths. Our system utilizes unmodulated light sources’ inherent characteristics, specifi- cally the power at dominant wavelengths, via single-pixel hue sensors, without modifying existing lighting infrastruc- ture. Additionally, we leverage BLE 5.1 standard and employ Constant Tone Extension (CTE), a waveform with constant frequency and amplitude [1, 3], to collect Received Signal Strength (RSS) using a single antenna. Furthermore, we lever- age Deep Neural Networks (DNNs) to e ectively fuse signal features from both technologies. DNNs show promise in modeling the mapping between signal features and target lo- cations, addressing multipath complexity in indoor areas [3]. Our contributions are summarized as follows: i) We propose BLELight, a hybrid accurate localization sys- tem for large indoor spaces. BLELight fuses the intrinsic features of unmodulated light (the ratio of power at dom- inant wavelengths) with the BLE signal strength. ii) We propose an incremental learning-based approach to train the DNN model