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

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.

Resource Allocation in a Quantum Key Distribution Network with LEO and GEO trusted-repeaters

Quantum Key Distribution~(QKD) is a technology that enables the exchange of private encryption keys between two legitimate parties, using protocols that involve quantum mechanics principles. The rate at which secret keys can be exchanged depends on the attenuation that is experienced. Therefore, it is more convenient to replace many terrestrial fiber segments (and repeaters) by just few optical satellite links that would enable flexible global coverage. Then, the satellite nodes can take the role of trusted-relays, forwarding the secret keys from source to destination. However, since the rate at which secret keys can be generated in each quantum link is limited, it is very important to select the intermediate satellite nodes to inter-connect ground stations efficiently. This paper studies the most convenient allocation of resources in a QKD network that combines complementary connectivity services of GEO and LEO satellites. The aim of the centralized routing algorithm is to select the most convenient trusted-relays to forward the secret keys between pairs of ground stations, verifying the constraints that satellite-to-ground and inter-satellite quantum channels have.

Visible light communication-based monitoring for indoor environments using unsupervised learning

Visible Light Communication~(VLC) systems provide not only illumination and data communication, but also indoor monitoring services if the effect that different events create on the received optical signal is properly tracked. For this purpose, the Channel State Information that a VLC receiver computes to equalize the subcarriers of the OFDM signal can be also reused to train an Unsupervised Learning classifier. This way, different clusters can be created on the collected CSI data, which could be then mapped into relevant events to-be-monitored in the indoor environments, such as the presence of a new object in a given position or the change of the position of a given object. When compared to supervised learning algorithms, the proposed approach does not need to add tags in the training data, simplifying notably the implementation of the machine learning classifier. The practical validation the monitoring approach was done with the aid of a software-defined VLC link based on OFDM, in which a copy of the intensity modulated signal coming from a Phosphor-converted LED was captured by a pair of Photodetectors~(PDs). The performance evaluation of the experimental VLC-based monitoring demo achieved a positioning accuracy in the few-centimeter-range, without the necessity of deploying a large number of sensors and/or adding a VLC-enabled sensor on the object to-be-tracked.