Over the past decade, visible light positioning has become increasingly important for precise localization systems, yet its widespread adoption is limited due to the necessity of modifying existing lighting systems. This paper presents HueLoc, a novel method that bypasses this issue by using inherent features of light, such as the dominant colours in white LED lights, and employs affordable, energy-efficient hue sensors for location services. We propose that by extracting the power at dominant wavelengths of LEDs, these can be uniquely identified using a specifically designed signature. The unique signatures can be used by mobile objects for spatial awareness and further localization using the proposed regression-based learning approach. Our experiments demonstrate that HueLoc attains a location-mapping accuracy of 100% and achieves decimeter-level localization precision with a moving object in uncontrolled lighting conditions. Moreover, these unique signatures can be combined with other RF-based technologies to enhance their localization accuracy. As an example, this paper details the integration of Bluetooth features with light signatures using a three-stage incremental learning approach.

A recent advance in embedded Internet of Things (IoT) exploits ambient light for wireless communication. This new paradigm enables highly efficient links via simple light modulation, but the design space has a fundamental constraint: in most State of the Art (SoA) studies, the link can only follow the propagation direction of ambient light. Consider, for example, a swarm of drones and ground robots that want to communicate with sunlight. Drone-to-robot communication could be possible because sunlight travels downwards from the air (drone) to the ground (robot), allowing drones to modulate light to send information to robots beneath them. Robot-to-robot communication, however, is not possible because sunlight does not travel sideways (parallel to the ground). To allow ‘lateral communication’ with ambient light, we propose using Luminescent Solar Concentrators (LSC). These optical components receive ambient light on their surface and re-direct part of the spectra towards their edges. Considering this optical property of LSC, our work has three main contributions. First, we benchmark various optical properties of LSC to assess their performance for ambient light communication. Second, we combine LSC with liquid crystal (LC) shutters to form lateral links with ambient light. Third, we test our links indoors and outdoors with artificial and natural ambient light, by enhancing two robots with our LSC transceivers and showing that they can exchange basic commands and coordinate tasks by communicating only with sunlight.

In recent years, the number of wireless applications has increased significantly, resulting in the radio bands becoming expensive and prone to interference. There is a new research area aiming at mitigating these issues by creating communication links using ambient light. This area, called passive-VLC, not only exploits the visible light frequencies, but does so with low-power transmitters. All the previous work in passive-VLC, however, forget about individual wavelength bands of light, and do not exploit its wide spectrum, reducing the potential channel capacity. In this paper, we propose a novel method to transmit and decode data, using liquid crystal cells that modulate and consider the full spectrum, and put it to the test by prototyping a multi-symbol communication link. The main contribution of our work is to show that passive-VLC can move from spectrum-agnostic to spectrum-aware modulation. We explore this new domain by making use of a novel type of receiver (i.e., a spectrometer) and uncovering the advantages and caveats of this spectrum-aware approach.

Cardiac patterns are being used to provide hard-to-forge biometric signatures in identification applications. However, this performance is obtained under controlled scenarios where cardiac signals maintain a relatively uniform pattern, facilitating the identification process. In this work, we analyze cardiac signals collected in more realistic (uncontrolled) scenarios and show that their high signal variability makes them harder to obtain stable and distinct features. When faced with these irregular signals, the state-of-the-art (SOTA) reduces its performance significantly. To solve these problems, we propose the CardioID framework¹ with two novel properties. First, we design an adaptive method that achieves stable and distinct features by tailoring the filtering process according to each user’s heart rate. Second, we show that users can have multiple cardiac morphologies, offering us a bigger pool of cardiac signals compared to the SOTA. Considering three uncontrolled datasets, our evaluation shows two main insights. First, while using a PPG sensor with healthy individuals, the SOTA’s balanced accuracy (BAC) reduces from 90–95% to 75–80%, while our method maintains a BAC above 90%. Second, under more challenging conditions (using smartphone cameras or monitoring unhealthy individuals), the SOTA’s BAC reduces to values between 65–75%, and our method increases the BAC to values between 75–85%.

Solar cells are mainly used as power sources, but can be used for sensing as well. We propose a novel indoor system that exploits solar cells to track people by monitoring the changes in light intensity caused by their shadows and reflections as they walk by. Our framework has three main components. First, we develop a simulator based on a ray-tracing model to determine how the solar cells should be positioned in the tracking environment to maximize the signal to noise ratio. Next, we apply changepoint detection methods to convert the (noisy) solar cell signal into a binary detection signal. Our detection method uses a Bayesian approach, which allows our system to work well in various environments, with natural and artifical light. Finally, the binary output from multiple solar cells is fused to track multiple targets. The tracking engine is based on a particle filter implementation based on the probability hypothesis density filter. This approach allows us to perform tracking without knowing the actual number of targets in the environment. To evaluate our framework, we build small tags that consist of a solar cell, a micro-controller and a wireless module, and deploy them in a real apartment. Ours results show that our system allows solar cells to track people under different lighting conditions, during day and night.

There is a growing interest in exploiting ambient light for wireless communication. This new research area has two key advantages: it utilizes a free portion of the spectrum and does not require modifications of the lighting infrastructure. Most existing designs, however, rely on a single type of optical surface at the transmitter: liquid crystal shutters (LCs). LCs have two inherent limitations, they cut the optical power in half, which affects the range; and they have slow time responses, which affects the data rate. We take a step back to provide a new perspective for ambient light communication with two novel contributions. First, we propose an optical model to understand the fundamental limits and opportunities of ambient light communication. Second, based on the insights of our analystical model, we build a novel platform, dubbed PhotoLink, that exploits a different type of optical surface: digital micro-mirror devices (DMDs). Considering the same scenario in terms of surface area and ambient light conditions, we benchmark the performance of PhotoLink using two types of receivers, one optimized for LCs and the other for DMDs. In both cases, PhotoLink outperforms the data rate of equivalent LC-transmitters by factors of 30 and 80: 30 kbps & 80 kbps vs. 1 kbps, while consuming less than 50 mW. Even when compared to a more sophisticated multi-cell LC platform, which has a surface area that is 500 times bigger than ours, PhotoLink's data rate is 10-fold: 80 kbps vs. 8 kbps. To the best of our knowledge this is the first work providing an optical model for ambient light communication and breaking the 10 kbps barrier for these types of links.

Advances in Visible Light Communication are enabling novel Internet of Things applications. Going forward, we expect that LED-to-Camera links will enable a wide range of body-centric computing applications. Up until now, most LED-to-Camera studies have been following a deploy-and-test approach instead of a principled methodology. This ad-hoc design raises up two problems. First, we cannot compare fairly the various methods proposed in the literature because they use different types of LEDs and cameras. Second, and perhaps more importantly, we cannot identify the fundamental opportunities and limits of these novel links. To overcome these challenges, we propose a simple analytical model that estimates the range and data rate of LED-to-camera links prior to deployment. The model is built from first principles and requires only a limited set of parameters. To validate the accuracy of our model, we consider the two main transmission modes used in the literature: binary transmission and communication based on the rolling shutter effect. Our experimental evaluation confirms the predictions of the analytical model.

Cardiac patterns are being used to obtain hard-to-forge biometric signatures and have led to high accuracy in state-of-the-art (SoA) identification applications. However, this performance is obtained under controlled scenarios where cardiac signals maintain a relatively uniform pattern, facilitating the identification process. In this work, we analyze cardiac signals collected in more realistic (uncontrolled) scenarios and show that their high signal variability (i.e., ir-regularity) makes it harder to obtain stable and distinct user features. Furthermore, SoA usually fails to identify specific groups of users, rendering existing identification methods futile in uncontrolled scenarios. To solve these problems, we propose a framework with three novel properties. First, we design an adaptive method that achieves stable and distinct features by tailoring the filtering spectrum to each user. Second, we show that users can have multiple cardiac morpholo-gies, offering us a much bigger pool of cardiac signals and users compared to SoA. Third, we overcome other distortion effects present in authentication applications with a multi-cluster approach and the Mahalanobis distance. Our evaluation shows that the average balanced accuracy (BAC) of SoA drops from above 90% in controlled scenarios to 75% in uncontrolled ones, while our method maintains an average BAC above 90% in uncontrolled scenarios.

A recent development in wireless communication is the use of optical shutters and smartphone cameras to create optical links solely from ambient light. At the transmitter, a liquid crystal display (LCD) modulates ambient light by changing its level of transparency. At the receiver, a smartphone camera decodes the optical pattern. This LCD-To-camera link requires low-power levels at the transmitter, and it is easy to deploy because it does not require modifying the existing lighting infrastructure. The system, however, provides a low data rate, of just a few tens of bps. This occurs because the LCDs used in the state-of-The-Art are slow single-pixel transmitters. To overcome this limitation, we introduce a novel multi-pixel display. Our display is similar to a simple screen, but instead of using embedded LEDs to radiate information, it uses only the surrounding ambient light. We build a prototype, called SunBox, and evaluate it indoors and outdoors with both, artificial and natural ambient light. Our results show that SunBox can achieve a throughput between 2 kbps and 10 kbps using a low-end smartphone camera with just 30 FPS. To the best of our knowledge, this is the first screen-To-camera system that works solely with ambient light.