Visible light positioning (VLP) based on the received signal strength (RSS) can leverage a dense deployment of LEDs in future lighting infrastructure to provide accurate and energy-efficient indoor positioning. However, its positioning accuracy heavily depends on the density of collected fingerprints, which is labor-intensive. In this work, we propose a data pre-processing method, including data cleaning and data augmentation, to construct reliable and dense fingerprint samples, thereby alleviating the impact of noisy samples as well as reducing labor intensity. Extensive experiments demonstrate that our proposed method achieves an average positioning error of 1.7 cm, utilizing a sparse dataset that reduces the fingerprint collection effort by 98 percent. Running a tinyML-based model for VLP on the Arduino Nano microcontroller, we also show the possibilities for deploying RSS fingerprint-based VLP systems on resource-constrained embedded devices for real-world applications.

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In visible light communication (VLC), the quality of communication is primarily dominated by line-of-sight links. To ensure an appropriate link quality anywhere, beamsteering has been proposed where transmitters (TXs) dynamically steer their beams to create beamspots on the users. However, these highly dynamic TXs face the beam tracking problem and result in highly variable illumination. In this work, we propose BlendVLC, a cell-free network architecture to improve the mobility robustness of users by blending the beamspots from both steerable and fixed TXs. We solve the beam tracking by designing a centimeter-level visible light positioning algorithm empowered by a neural network. Relying on this location information, we formulate and solve an optimization problem on the beamspot blending, and design a fast and scalable heuristic for large networks. We build a proof-of-concept testbed as well as a simulator to evaluate BlendVLC. We show that it achieves superior performance compared to denser networks with fully fixed TXs. For example, in a large-scale VLC network of 8 m x 4 m, BlendVLC improves the average system throughput by 30%, while only requiring half the number of TXs.

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We propose a concept of coverage-bounding and 'visual' wireless communication-HODOR 1-to secure the Internet of Things (IoT). Coverage-bounding means the communication coverage is controlled accurately in 3-dimensions. 'Visual' implies that the communication coverage and process are visible to user, representing an important and user-friendly side-channel for se-curing IoT. HODOR can provide secure wireless communication both psychologically (visible to users) and technically (nodes only communicate with each other within their delimited coverage). It can benefit IoT applications for secure wireless communications, especially those that demand secure interactions in proximity.

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Visible Light Communication (VLC) has attracted significant attention over the past decade. Although numerous research studies have been performed to improve the data rate of VLC links, an important fact has been largely neglected: human bodies that host VLC receivers could block their Line-of-Sight (LOS) downlinks, and thus, degrade the system performance greatly. In this paper, we propose a system that can significantly improve the robustness for VLC networks by avoiding performance degradation due to blockage. A novel user-in-the-loop mechanism is designed in which users (including human bodies and VLC receivers) are guided by the network to rotate themselves to improve the system performance and the individual user experience. Both our simulation and experimental results demonstrate that the proposed user-in-the-loop mechanism can improve the system throughput and user fairness on average by 48% and 14%, respectively. For individual users, the average gain in throughput can reach up to 135%. Furthermore, to make the system more practical, two lightweight heuristics are designed and implemented which can achieve similar gains while reducing the computational complexity by 99%.@en

LED luminaries are now deployed densely in indoor areas to provide uniform illumination. Visible Light Communication (VLC) can also benefit from this dense LED infrastructure. In this paper, we propose DenseVLC, a cell-free massive MIMO networking system enabled by densely distributed LEDs, that forms different beamspots to simultaneously serve multiple receivers. This is a cell-free system, as there is no notion of autonomous cells and transmitters cooperate to jointly serve the users. Given a power budget for communication, DenseVLC assigns the power budget among the distributed LEDs to optimize the system throughput and user fairness. We formulate an optimization problem to derive the optimal policy for the power allocation. Our insights from the optimal policies allow us to simplify DenseVLC's system design and propose a heuristic algorithm that can reduce the complexity by 99.96%. Besides, we propose a novel synchronization method using non-line-of-sight VLC to synchronize all the transmitters that will form a beamspot to serve the same receiver. We implement DenseVLC with off-the-shelf devices, solve practical challenges in the system design, and evaluate it with extensive and realistic experiments in a system of 36 transmitters and 4 receivers in an area of\:\text{m}\,\,\times 3\:\text{m}$. Our results show that DenseVLC can improve the average system throughput by 45%, or improve the average power efficiency by 2.3 times, while maintaining the requirement for uniform illumination. Finally, we demonstrate that DenseVLC is robust against blockage.

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