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 article presents HueLoc, a novel method that bypasses this issue by using inherent features of light, such as the dominant colors in white light-emitting diode (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 article details the integration of Bluetooth features with light signatures using a three-stage incremental learning approach. The experimental results show that this fusion method significantly improves Bluetooth localization by over 75%, overcoming challenges associated with severe indoor multipath and achieving highly precise location accuracy within decimeters.

Although desirable, achieving a high-fidelity digital twin of buildings often requires a substantial influx of real-time data, demanding a dense network of environmental sensors. Regrettably, factors such as high hardware expenses, deployment constraints, and sensor malfunctions can impede the realization of such digital twins. In this demonstration, we introduce a pioneering approach that harnesses data-driven virtual sensing within a real-time, high-fidelity 3D digital twin of a building environment. Our innovative method provides accurate machine learning-based inference of real-time sensor variables across both two-dimensional (varying rooms) and three-dimensional (diverse elevations) domains, with limited reliance on physical sensor inputs. Thus, by extending the sensing coverage through the use of ML-driven virtual sensing, we are able to create a more accurate digital twin. Our initial results indicate a mean absolute percentage error of2, when compared against the ground truth physical sensors.

Designing a reliable and highly accurate indoor localization system is challenging due to the non-uniformity of indoor spaces, multipath fading, and satellite signal blockage. To address these issues, we propose a Deep Neural Network-based localization system that combines passive Visible Light Positioning (p-VLP) and Bluetooth Low Energy (BLE) technologies to achieve stable, energy-efficient, and accurate indoor localization. Our solution leverages incremental learning to fuse data from visible light and BLE, overcoming their individual limitations and achieving centimeter-level localization accuracy. We build a prototype using low-cost S9706 hue sensors for p-VLP and low-power nrf52830 BLE boards to collect data simultaneously from both technologies in a 25m2 testbed. Our approach demonstrates a significant localization accuracy improvement of approximately 47% and 64% compared to individual p-VLP and BLE technologies, respectively, achieving a mean localization error of 20 cm.

In LED-to-Smartphone Camera communication, the camera detects the status of the light source (ON or OFF) in each frame to receive information. If the light source is modulated faster than a single frame duration, there are multiple white & dark stripes corresponding to the transmitted information bits. Further, the receiver’s region of interest in LED-to-Smartphone Camera communication is limited by the size of the light source in the frame in terms of the number of pixels. As the distance between the LED and the camera increases, the light source does not cover the whole frame, resulting in a significant amount of data loss. Therefore, LED-to-Smartphone Camera communication is limited by distance, making it difficult to transmit data from several meters away. On the other hand, there is a loss of data even at relatively short ranges due to small region of interest, especially for tiny light sources. In this work, we enhance the smartphone camera with a telephoto lens that provides optical zoom on the light source and increases the region of interest. The optical zoom from a telephoto lens helps the user focus on the light source and reduces data loss. We compare the data transfer rate with and without a telephoto lens at short distances (15-80 cm). The initial results show that a telephoto lens improves the data transfer rate at short distances and also expands the possible range for LED-to-Smartphone Camera links.