Moh Khalid Hasan ¹ , Md. Osman Ali ² , Sijia Yang ³ , Wei Chen ⁴ and Ke Wang ², ¹ James Madison University, USA, ² RMIT University, Australia, ³ USTB, China, ⁴ Centre for Opto-Electronic Engineering and Technology, China

Optical camera communication (OCC) is considered as a key enabler of optical wireless com munication technology. In OCC, light-emitting diodes (LEDs) serve as the transmitter and rolling shutter (RS) cameras as the receiver for high-speed communication. However, the received luminance from the LED is critically important for reliable data retrieval in OCC, which faces challenges due to the inherent nature of data collection. Smartphones, typically equipped with RS cameras, represent one of the most promising platforms for the commercial deployment of OCC. To ensure system reliability, various methods have been proposed to address the diversity in pixel illumination values captured by smartphone cameras. Furthermore, AI-based approaches that make RS cameras compatible with low-speed mobile scenarios and enhance overall system performance also introduce additional system complexity. In this paper, we provide a systematic review of the state-of-the-art methods for data retrieval in smartphone camera-based OCC. In particular, we provide specific challenges due to important factors, such as communication distance variation and the blooming effect. Furthermore, we discuss recent advancements, especially promising AI applications in OCC. Finally, we outline open research directions on smartphone camera-based OCC.

Optical camera communication (OCC), rolling shutter camera, smartphone-based OCC, bloom-ing effect.