The growth rate of Internet-of-Things (IoT) devices sold globally is constantly lower than the forecast. This deceleration is caused in part by the need for batteries and the scalability cost for their replacement. Backscatter has attracted significant interest over the past couple of years to enable sustainable sensing devices by eliminating batteries. IoT devices have been designed for transmitting sensed data with backscatter, but the question of efficient reception of data with battery-free devices is still open. As shown in this paper, classical low-power Radio Frequency (RF) envelope detectors are affected by low sensitivity, false detection alarms, and low energy efficiency. We argue that Light Fidelity (LiFi) can provide downlink and harvesting medium as LED lights are becoming pervasively deployed for illumination. We show, for the first time, that the advantages of LiFi and RF backscatter can be combined for battery-free communication. We design a low-power platform that leverages the complementary nature of these two mediums. We demonstrate that our platform removes energy-inefficiency in the downlink reception typical of RF backscatter, and significantly expands the deployment scenarios for battery-free tags when compared to conventional single-technology designs.

Light-based positioning systems (LPS) are gaining significant attention as a means to provide localization with cm accuracy. Many of these systems estimate the object position based on the received light intensity, and work properly in `ideal' environments such as large open spaces without obstructions around the light-emitting diode (LED) and the receiver, where reflections are negligible. In more dynamic environments, such as indoor spaces with moving people and city roads with moving vehicles, materials cause a wide variety of reflections. This causes variations in the received light intensity and, as a consequence, gross localization errors in LPS. We propose a new multipath detection technique for improving LPS that does not require the knowledge of the channel impulse response and then, it is suited to be implemented in low-cost positioning receivers that use a single-pixel photodetector. To develop our technique, we (i) analyze the statistical properties of non-line-of-sight (NLOS) components, (ii) develop an automated testbed to study the reflections of different types of surfaces and materials, and (iii) design an algorithm to remove the NLOS components affecting the positioning estimate. Our experimental evaluation shows that, in complex environments, our methodology can reduce the localization error using LEDs up to 93%.

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 3 m × 3 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.

In this paper we document the demo of the latest version of OpenVLC, a low-cost platform for communicating using Visible Light, and we prove we can transmit video with it. This paper shows the setup used, the behavior of each of its components, and the results expected. This is the first time video transmission has been possible with OpenVLC and to the best of our knowledge, the first time it has been done with low-cost components.

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 system enabled by densely distributed LEDs, that forms different beamspots to serve multiple receivers simultaneously. Given a power budget for communication, DenseVLC can optimize the system throughput by properly assigning the power budget among the distributed LEDs. 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 3 m x 3 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.

Visible light is gaining significant attention as a medium to achieve accurate relative localization. Most of the studies in the area focus on indoor positioning and rely on two important assumptions: (i) lights are static, and (ii) the receiver has line-of-sight with multiple lights. These requirements limit the application of localization methods in scenarios where nodes have a single light and are mobile, such as motorbikes or swarms of robots. In general, this particular type of scenarios (single lights moving on a plane) leads to under-determined localization systems where no unique solution can be found. We follow a holistic approach that includes theory, simulations, and experiments to overcome some of the limitations present in such type of scenarios. Our theoretical and simulation results show that if nodes are enhanced with sensors providing relative directions (such as compasses), we can derive dependencies in the system to obtain unique solutions. Our proof-of-concept implementation validates our model by showing that single lights can provide relative localization with high accuracy: an average error below 5 cm.