Li-Pos: A Light Positioning Framework Leveraging OFDM for Visible Light Communication

Abstract

The design of solid-state lighting is vital, as numerous metrics are involved in their exact positioning, and as it is utilized in various processes, ranging from intelligent buildings to the internet of things (IoT). This work aims to determine the power and delay spread from the light source to the receiver plane. The positions of the light source and receiver were used for power estimation. We focus on analog orthogonal frequency-division multiplexing (OFDM) in visible light communication (VLC) and assess the area under the curve (AUC). The proposed system was designed using modulation techniques (i.e., quadrature amplitude modulation; QAM) for visible light communication (VLC) and pulse-width modulation (PWM) for dimming sources. For the positioning and spreading of brightness, the proof-of-concept was weighted equally over the entire area. Therefore, the receiver plane was analyzed, in order to measure the power of each light-emitting diode (LED) in a given area, using the delayed mean square error (MSE). A framework was applied for the placement of LEDs, using full-width at half-maximum (FWHM) parameters with varying distances. Then, the received power was confirmed. The results show that the AUC using DRMS values for LEDs significantly increased (by 30%) when the number of source LEDs was changed from four to three. These results confirm that our system, associated with the simple linear lateration estimator, can achieve better energy consumption.

Visible Light Positioning as a Next-Generation Indoor Positioning Technology: A Tutorial

“…One-shot VLP Localisation: One-shot localization refers to the positioning approaches that do not keep state, nor use sensor fusion. The following algorithms have featured for VLP [366]: vision analysis (based on projected images), proximity (also centroid), hyperbolic (TDOA) or circular (TOA/RSS) (non)linear least squares multilateration or multiangulation [13], [250], [421], [422], probabilistic or deterministic scene analysis or fingerprinting [423], explicit (Bayesian) statistics [330], [424] and iterative minimisation or maximum likelihood estimation [200], implicit (supervised) machine learning including deep learning-based classification (e.g. with K-nearest neighbour [425]) or regression [426]- [431].…”

A Review on the Evaluation of Feature Selection Using Machine Learning for Cyber-Attack Detection in Smart Grid

“…By employing deep network design, neural networks can extract high-dimensional characteristics, resulting in improved robustness and generalization performance [22][23][24][25][26][27][28] . Additionally, the smart grid's unique ability to communicate with itself provides advantages in terms of effective energy utilization and distribution for a variety of smart devices and machines [29][30][31][32] . However, because the smart grid may store sensitive information, cybersecurity is crucial, and a variety of security solutions must be evaluated and analyzed [33][34][35] .…”

Smartphone Sensors for Indoor Positioning

“…For visible light communication (VLC), the design of solid-state lighting is investigated in [10]. Keeping in view its importance and usage in intelligent buildings, VLC communication, and IoT, the study determines the power and delay spread from a light source to a receiver plane.…”