Joint Visible Light and Backscatter Communications for Proximity-Based Indoor Asset Tracking Enabled by Energy-Neutral Devices

TL;DR

This work tackles energy-aware indoor localization by merging VLC-based proximity signaling with RF backscatter reporting under a SLIPT framework. A battery-free BD harvests light energy from LED APs, receives VLC IDs via BFSK across multi-cell VLC links, and backscatters ambient RF to an edge reader for position inference. A multi-cell VLC deployment with an FDM scheme and a PF-based fusion algorithm at the reader enables robust real-time tracking by combining proximity IDs and backscatter RSS. Simulations and experiments demonstrate submeter-level accuracy (median around 0.32 m) with very low BD power consumption, highlighting a scalable, cost-effective solution for energy-neutral asset tracking in pervasive IoT deployments.

Abstract

In next-generation wireless systems, providing location-based mobile computing services for energy-neutral devices has become a crucial objective for the provision of sustainable Internet of Things (IoT). Visible light positioning (VLP) has gained great research attention as a complementary method to radio frequency (RF) solutions since it can leverage ubiquitous lighting infrastructure. However, conventional VLP receivers often rely on photodetectors or cameras that are power-hungry, complex, and expensive. To address this challenge, we propose a hybrid indoor asset tracking system that integrates visible light communication (VLC) and backscatter communication (BC) within a simultaneous lightwave information and power transfer (SLIPT) framework. We design a low-complexity and energy-neutral IoT node, namely backscatter device (BD) which harvests energy from light-emitting diode (LED) access points, and then modulates and reflects ambient RF carriers to indicate its location within particular VLC cells. We present a multi-cell VLC deployment with frequency division multiplexing (FDM) method that mitigates interference among LED access points by assigning them distinct frequency pairs based on a four-color map scheduling principle. We develop a lightweight particle filter (PF) tracking algorithm at an edge RF reader, where the fusion of proximity reports and the received backscatter signal strength are employed to track the BD. Experimental results show that this approach achieves the positioning error of 0.318 m at 50th percentile and 0.634 m at 90th percentile, while avoiding the use of complex photodetectors and active RF synthesizing components at the energy-neutral IoT node. By demonstrating robust performance in multiple indoor trajectories, the proposed solution enables scalable, cost-effective, and energy-neutral indoor tracking for pervasive and edge-assisted IoT applications.

Figures (14)

• Figure 1: Overview of the joint VLC-BC system proposed for indoor asset tracking. The BD attached to the asset receives LED IDs over light (yellow cones) and convert it to electrical signal to modulate the ambient RF carrier waves (green arrow) emitted by the RFS. The modulated signal is backscattered (blue arrow) and detected by an edge reader for location inference.
• Figure 2: A schematic diagram of the joint VLC-BC system. LED APs illuminate the indoor space and transmit unique IDs through VLC links. The BD receives VLC signals and converts them into electrical signals, whose DC components are used to power the BD while AC components are used to modulate the captured RF carriers emitted by the RFS. The modulated signal is backscattered toward the RF reader, which is decoded and used for positioning purposes.
• Figure 3: An example of VLC deployment based on FDM. A VLC cluster consists of four VLC cells denoted by four different colors. Within a cluster, each cell adopts a distinct frequency pair to implement the BFSK modulation to transmit the VLC cell ID, avoiding interference between adjacent cells.
• Figure 4: Implementation of the proof of concept. Six LED luminaires emit VLC signals embedded with their IDs. A signal generator emits 2.4 GHz carrier waves. The BD receives the VLC signals, converts them into electrical signals and modulates with the carrier signal. The modulated signals are backscattered and captured by a USRP, which is connected to a host computer for signal processing and location inference of the BD. The setup is within an indoor space.
• Figure 5: Prototype of the BD with its schematic. A 2-euro coin is placed in the picture to show the scale of the BD prototype.