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About

Fakhrul Alam is a researcher whose work sits at the intersection of indoor positioning systems, visible light communication, and autonomous robotics. He is best known for pioneering practical applications of Visible Light Positioning (VLP), a technology that harnesses LED lighting infrastructure to enable precise indoor localization — a domain where GPS-based systems fall short. His 2016 paper on using visible light communication for mobile robot localization, garnering 48 citations, marked a significant shift from simulation-based VLC research toward real-world experimental validation, demonstrating that accurate indoor positioning was achievable with consumer-grade hardware. Alam's subsequent work expanded this foundation considerably. His 2021 study introduced an autonomous fingerprinting methodology using Virtual Reality equipment to efficiently collect large-scale Received Signal Strength datasets, advancing the scalability of VLP systems. His 2017 hybrid VLP approach further refined robot localization by augmenting existing lighting infrastructure with minimal modification. More recently, his 2024 publication connects VLP directly to robot navigation, closing the loop between positioning theory and autonomous mobility. Earlier in his career, Alam also explored neuronal oscillator models for adaptive traffic signal control, reflecting a broader interest in intelligent systems. Collectively, his research has meaningfully advanced the feasibility of deploying light-based indoor navigation in real-world environments.