Internet of Things (IoT) plays as the key infrastructure for various applications, e.g., smart city, smart grid, and smart manufacturing. Wireless communication is greatly limited for IoT devices due to the severe electromagnetic interference in industrial radio-hostile environments. Visible light communication (VLC) has been considered as a promising communication technique that could complement the wireless communication. Due to complex industrial environments, it is not practical to wire such scale of VLC-enabled IoT devices into the power line or replace batteries for them. To cope with this challenge, we propose to harvest the energy from electromagnetic interference signals to power the VLC for IoT devices. We achieve this goal by proposing advanced signal processing designs, new energy-efficient schemes, and seamless integration. Finally, we prototype the system on real devices, and our extensive experiments show that our system can achieve promising results.

With the rapid development of smart cities, white light‐emitting diodes (LEDs) are widely used in indoor lighting due to their characteristics of energy‐saving, long lifetimes, and low cost. At the same time, the high‐frequency modulated LEDs allow visible light to be used for indoor communications, positioning, and security. Visible light applications have many advantages, such as avoiding electromagnetic interference, high communication speed, great privacy, and high‐precise positioning. In this paper, we will survey the application prospects and research results of visible light from scenarios such as high‐speed communication, privacy security, and navigation localization and focus on analyzing and discussing the challenges and development trends encountered in visible light positioning.

The blooming of the Internet of Things (IoT) needs a communication infrastructure to allow connecting a wide range of devices. Visible light communication (VLC) provides an attractive solution for these IoT devices. However, the broadcast nature makes the VLC prone to be exposed in the reach of eavesdroppers, which undesirably impairs the IoT system security. The negative factors in practical use, such as light reflection, light intensity, channel correlation, and channel estimation error, remain less touched. In this paper, we propose the SecLight to support both the multiple-input single-output VLC (MISO-VLC) and the single-input single-output VLC (SISO-VLC) IoT devices. A random time reversal scheme is first proposed to automatically steer the SISO-VLC signal to the legitimate receivers by scrambling the eavesdropper's channel. Then, we mathematically analyze the impact of channel correlation and channel estimation error inherent in MISO-VLC system and propose an effective approach to alleviate the decreased secrecy capacity by introducing the Karhunen-LoEve transform and equivalent artificial noise. Through extensive evaluation, the proposed SecLight framework proves to be able to aggravate the bit error rate of eavesdroppers, even when a malicious interception is intended while holding the secrecy capacity above 3 bit/s/Hz. Overall, our results demonstrate that our framework SecLight is a promising solution for enhancing the security of the IoT devices.

The proliferation of the Internet of Things pushes the visible light positioning (VLP) system research. However, the existing positioning systems still have the following problems: 1) when the smartphone (receiver) is rotated or tilted during the positioning, existing collaborative LEDs’ positioning algorithms fail and 2) for different smartphone application scenarios, there is not an effective resource management solution between the server and the client. Therefore, in this article, we design and implement a robust and flexible indoor VLP system based on collaborative LEDs and edge computing. First, we propose the enhanced collaborative LEDs’ positioning algorithm, which uses the indoor hidden location information to obtain the rotation angle and tilt angle of the receiver, to achieve the robust system positioning. Then, we use the edge computing solution to balance between bandwidth resources and computing resources and propose a flexible functional segmentation scheme for different smartphone application scenarios. Finally, we conduct experimental tests to evaluate the positioning system performance by landmark decoding rate, positioning accuracy, and segmentation analysis. Test results show that the designed positioning system can achieve centimeter-level positioning. Meanwhile, the smartphone can exchange the least bandwidth resources for the most computing resources under Scheme-3.

Big data and cognitive computing have a wide range of applications in smart homes, smart cities, artificial intelligence, and computational social systems. Visible light positioning systems have attracted more and more attention as one of the application scenarios of computational social systems. In visible light positioning systems, the light-emitting diodes (LEDs) ceiling layout makes the smartphone usually obtain less than three LEDs in captured images. Due to the lack of necessary positioning information, most scholars combine the inertial measurement unit (IMU) with the modified filter algorithms to achieve positioning under the sparse light source. However, these systems have the following problems: 1) the azimuth angle obtained by the IMU is always not accurate, which decreases the positioning accuracy and 2) during the dynamic positioning process, the system’s initial position is difficult to automatically determine. In this article, we propose indoor high-precision visible light positioning under the sparse light source. First, we propose the geometric correction mechanism, which uses ellipse fitting to calibrate the azimuth angle, so as to increase the positioning accuracy for the static system. Then, we build a motion model for the entire positioning process through the unscented particle filter (UPF), which does not need to manually set initial state parameters, due to random generated particles. It can increase the positioning accuracy for the dynamic system. We evaluate our designed system, and the experimental results show that the average azimuth angle error is $2.04^ \circ$ and average positioning error is 8.8 cm, under the sparse light source.