This paper addresses the handover challenge in indoor visible light communication and Wi-Fi heterogeneous networks, proposing an adaptive handover strategy based on user trajectory prediction. Extracting meaningful and important location points from massive trajectory data for clustering, an improved hidden Markov model is used to predict the user’s next location by analyzing the patterns of the user’s historical mobile trajectory data. The Q-learning algorithm is then used to determine the optimal network handover based on the current network state, while a seamless handover protocol is introduced to ensure successful network transition and uninterrupted data transmission. Compared with the traditional STD-LTE handover scheme, the proposed algorithm can reduce vertical handover rates by up to 32% during fast walking. When indoor user connections increase, the algorithm can maintain high fairness and high throughput when indoor user connections increase, verifying that it is robust in different scenarios.

Visible light positioning (VLP) can provide indoor positioning functions under LED lighting, and it is becoming a cost-effective indoor positioning solution. However, the actual application of VLP is limited by the fact that most positioning requires at least two or more LEDs. Therefore, this paper introduces a positioning system based on a single LED lamp, using an image sensor as the receiver. Additionally, due to the high computational cost of image processing affecting system real-time performance, this paper proposes a virtual grid segmentation scheme combined with the Sobel operator to quickly search for the region of interest (ROI) in a lightweight image processing method. The LED position in the image is quickly determined. Finally, the position is achieved by utilizing the geometric features of the LED image. An experimental setup was established in a space of 80 cm × 80 cm × 180 cm to test the system performance and analyze the positioning accuracy of the receiver in horizontal and tilted conditions. The results show that the positioning accuracy of the method can reach the centimeter level. Furthermore, the proposed lightweight image processing algorithm reduces the average positioning time to 53.54 ms.