Optimized Clustering of LEDs and IoT Devices in VLC Networks With Uniformity Constraints

Abstract

Given the growing congestion in the legacy radio frequency (RF) spectrum, visible light communication (VLC) is gaining traction as a complementary wireless communication paradigm, particularly suited for dense, short-range applications. This paper presents a VLC network architecture using a multi-element hemispherical light-emitting diode (LED) bulb to support scalable communication for Internet-of-Things (IoT) devices. We formulate a time allocation strategy to maximize the minimum signal-to-interference-plus-noise ratio (SINR) across the IoT devices while supporting prioritized access for selected IoT devices. The optimization framework configures clusters of IoT devices and LEDs on the bulb, and determines their associations. By tuning transmit powers of LEDs, it also satisfies illumination uniformity requirements. Due to the non-convexity of the problem, we propose a complete solution containing several steps: 1) LED power optimization, 2) clustering of IoT devices using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN), 3) clustering of LEDs using the $k$ -means, and 4) mapping of LED and IoT device cluster. The system is designed to adapt to varying IoT device distributions by re-optimizing LED-IoT device associations and time allocations for each scenario, demonstrating robustness to dynamic environments. The simulation results show that our approach maintains uniform illumination throughout the room, enhances SINR performance, and ensures fairness among the IoT devices.