Due to the dual functionality of lighting and localization, received signal strength (RSS) based visible light positioning (VLP) has become a promising high-precision indoor positioning method in industrial environments by leveraging pervasively available lighting infrastructure. However, its widespread deployment is hindered by two key challenges: 1) the need for densely sampled fingerprint datasets, and 2) performance degradation due to LED aging in a long-lasting deployment. To address these limitations, we develop a data-efficient VLP method with an online calibration strategy that automatically compensates for gradual light intensity decay caused by LED aging, maintaining robustness without manual re-fingerprinting. Extensive simulations demonstrate that the proposed approach maintains centimeter-level precision across varying LED usage durations. A lightweight on-device model deployed on the Raspberry Pi Pico microcontroller achieves 1.62 cm mean positioning error with 8.39 ms inference latency, demonstrating the practicality of our approach for long-lasting indoor positioning in industrial and IoT scenarios. The source code is publicly available at https://github.com/joeywli/VLP. ... Read more