Wireless human activity sensing has drawn significant interest from researchers due to its potential for transformative applications across diverse domains. However, conventional Radio Frequency (RF) signal-based and acoustic-based approaches face several challenges, including (i) interference with existing communication signals, (ii) dependence on dedicated devices and signals, and (iii) potential health concerns from prolonged exposure. Recently, researchers have explored using optical signals (e.g., LED light signals) for human gesture recognition, achieving promising performance. However, these approaches require modifications to existing lighting infrastructure to generate dedicated light signals and multiple optical receivers. To address these challenges, in this paper, we propose LightTells, an innovative single activity sensing system for one active human object that uses a single optical receiver to capture and analyze ambient light signals without modifying the existing LED lighting infrastructure. To achieve this, LightTells first analyzes the received ambient light signals and extracts activity-correlated information. Based on these features, we design a multi-modal human activity recognition model to recognize the corresponding human activity. We implement LightTells using commercial off-the-shelf components and evaluate its performance by conducting extensive evaluations under various real-world scenarios, including common indoor spaces (e.g., Lab, classroom, and lobby lounge), and RF-restricted hospital environments. The results demonstrate that LightTells is agnostic to LED sources, light receivers and its placement positions, human objects (e.g., type and clothing), and sensing distance, achieving average recognition accuracy of up to \(94.25\%\) for 12 common human activities. Furthermore, two practical use-cases: fall detection and fatigue detection, showcase LightTells’s effectiveness, achieving up to \(100\%\) accuracy in both scenarios.

Hybrid visible light communication (VLC)/radio frequency (RF) networks have emerged as a promising next-generation networking technology that can provide high data rate and wide coverage while overcoming blockage problems of VLC and the limited available radio spectrum of RF. However, there has been limited research on the joint user association and power allocation problem in indoor hybrid VLC/RF networks with consideration of networking dynamics. To bridge the gap, we first formulate a joint user association and power allocation problem with the objective of maximizing the sum throughput of the hybrid networks with consideration of user mobility, time-varying blockages and interference between VLC access points (APs) and users, and practical capacity limitations of APs. Then we propose a distributed cooperative proximal policy optimization (DC-PPO) algorithm to solve the formulated mixed integer nonlinear programming problem, where each user is modeled as a hybrid agent that distributedly determines the user-AP association (discrete action) and the desired transmission power (continuous action) variables simultaneously based on local observations and limited information exchanged between neighboring nodes. The performance of our proposed method is compared with three baselines, including deep Q-network (DQN), max-power-max-signal-to-noise ratio (SNR), and random-power-max-SNR. Simulation results show that the proposed method is mobility and blockage-ware and outperforms the baseline solutions in terms of optimality, convergence, scalability, and robustness. Specifically, our proposed method can achieve $127 \%$ throughput improvement compared with that of DQN in 30mobile-user scenario.

Optical Wireless Communication (OWC) Autonomous Aerial Vehicles (AAVs) networks have attracted ever-increasing attention of researchers in recent years. There have been significant efforts to advance the OWC AAV networking system to develop new solutions, including optimal resource allocation algorithms and networking control methods. However, current OWC-AAV research is either based on simulations or in-flexible hardware platforms that can support mostly point-to-point, low-data-rate applications. Therefore, in this paper, we are the first to propose and discuss the design of a new programmable software-defined optical wireless communication-based AAV networking platform, O-AAV, with characteristics in terms of data rate, transmission distance, and hardware/software flexibility in support of different light sources and distributed networking operations. The main architectural choices of the new platform are discussed in detail, as well as preliminary performance evaluation results. Data rates in the order of Mbps are achieved in a controlled lab environment, and 500 Kbps data rates achieved more than 10 m transmission distance in the outdoor environment, respectively. We also effectively address the PD saturation problem and interference from other ambient light sources, which most OWC testbeds face. Lastly, to the best of our knowledge, we are the first to demonstrate the networking capability of the proposed architecture and prototype by implementing ALOHA.

Optical Wireless Communication (OWC) has been emerging as a promising complementary technology to traditional Radio Frequency (RF) communication for 6G and beyond. Due to the inherent low penetration property of light, the optical signal can be easily blocked by non-transparent objects, such as walls, rendering OWC highly secure at the physical layer. However, recent studies unveil that OWC systems can be eavesdropped even outside the wall. This is because while communicating via light, OWC transmitters also emit RF signals carrying the same information as light signals, thus leading to potential security vulnerabilities. Though Commercial Off-The-Shelf (COTS) OWC products are required to follow Electromagnetic Compatibility (EMC) regulations with minimum RF signal leakage, some OWC systems still struggle to meet the requirements when deployed in practical scenarios. To bridge this gap, we are the first to propose and implement a physical layer countermeasure protocol, Dynamic Frequency and Duration (DFD), to counter RF side-channel eavesdropping in OWC systems. The key idea is to transmit the data by dynamically changing the carrier frequency and transmission duration of the modulated signals that are fed into the OWC transmitters. We first validate the RF side-channel eavesdropping phenomenon and examine the effects of the different eavesdropping antennas, Modulation Coding Schemes (MCSs), signal gains, and Light-Emitting Diode (LED) transmitters on the leaked RF side-channel signal power, based on the developed programmable software-defined OWC and eavesdropping testbed. Then, we implement and evaluate DFD with extensive experiments, where the results demonstrate that DFD is highly effective in mitigating the security risks posed by the RF side-channel leakage without significantly affecting the performance of OWC system.

As of today, there has been increasing research on designing optimization algorithms and intelligent network control methods for visible light Unmanned Aerial Vehicles (UAV) networks to provide pervasive and broadband connections. For those theoretical analysis based algorithms, there is an urgent need to have a visible light UAV network platform that can help evaluate the proposed algorithms in real-world scenarios. However, to the best of our knowledge, there is currently no dedicated high data rate and flexible visible light UAV networking prototype. To bridge this gap, in this paper, we first design a novel programmable software-defined architecture for visible light UAV networking, including control plane, network plane, signal processing chain and front-ends plane, and ground facility plane. We then implement a prototype and conduct numerous experiments to validate the feasibility of visible-light UAV networks and further evaluate the system performance pertaining to achievable data rate and transmission distance. The real-time video streaming experimental results show that up to 550 kbps data rate and a maximum distance of 7 meters can be achieved.

Hybrid Light-Fidelity (LiFi) and Wireless-Fidelity (WiFi) indoor networking has been envisioned as a promising technology to alleviate radio frequency (RF) spectrum crunch to accommodate the ever-increasing data rate demand in indoor scenarios. The hybrid LiFi/WiFi indoor networks can utilize the advantages of fast data transfer from LiFi and wider coverage of WiFi, thus complementing well with each of these technologies and increasing the network performance compared to the standalone networks. This work investigates the user-access point (AP) association problem to achieve better network-wide spectral efficiency. We first derive a mathematical formulation of the sum data rate maximization problem by determining the AP selection for each user in indoor networks with either static or mobile users. It is shown that the formulated network control problem is a mixed integer nonlinear nonconvex problem. A proximal policy optimization (PPO) based reinforcement learning method is then proposed to determine the AP assignment. The network performance is evaluated by comparing it with exhaustive search, signal strength strategy, and trust region policy optimization methods. The results indicate that the designed method achieves a network-wide data rate with up to 100% and 97.7% optimality in static and mobile user scenarios, respectively.

The mobility nature of unmanned aerial vehicles (UAVs) takes them into high consideration in military, public, and civilian applications in recent years. However, scaling out millions of UAVs in the air will inevitably lead to a more crowded radio frequency (RF) spectrum. Therefore, researchers have been focused on new technologies such as millimeter-wave, Terahertz, and visible light communications (VLCs) to alleviate the spectrum crunch problem. VLC has shown its great potential for UAV networking because of its high data rate, interference-free to legacy RF spectrum, and low-complex frontends. While the physical layer design of the VLC system has been extensively investigated, visible-light-band networking is still in its infancy because of the intermittent link availability caused by blockage and miss-alignment among transceivers. Fortunately, drones can be deployed dynamically at network runtime to establish line-of-sight (LOS) links to users in blockage-rich environments. In this article, we first formulate a sum-rate optimization problem for visible-light-band UAV networks by jointly control the real-time position and orientations of drones. We then propose a solution algorithm based on particle swarm optimization (PSO). The simulation results show that the proposed algorithm can converge in 10 to 20 iteration time and can result in up to 24% performance gain compared to that in heuristic-central-point drone deployment.

Wireless Multimedia Sensor Network (WMSN) is increasingly being deployed for surveillance, monitoring and Internet-of-Things (IoT) sensing applications where a set of cameras capture and compress local images and then transmit the data to a remote controller. Such captured local images may also be compressed in a multi-view fashion to reduce the redundancy among overlapping views. In this paper, we present a novel paradigm for compressed-sensing-enabled multi-view coding and streaming in WMSN. We first propose a new encoding and decoding architecture for multi-view video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and independent decoders. The proposed architecture leverages the properties of CS to overcome many limitations of traditional encoding techniques, specifically massive storage requirements and high computational complexity. Then, we present a modeling framework that exploits the aforementioned coding architecture. The proposed mathematical problem minimizes the power consumption by jointly determining the encoding rate and multi-path rate allocation subject to distortion and energy constraints. Extensive performance evaluation results show that the proposed framework is able to transmit multi-view streams with guaranteed video quality at lower power consumption.

This article studies distributed algorithms to control self-organizing flying drones with massive MIMO networking capabilities - a network scenario referred to as mDroneNet. We attempt to answer the following fundamental question: what is the optimal way to provide spectrally-efficient wireless access to a multitude of ground nodes with mobile hotspots mounted on drones and endowed with a large number of antennas; when we can control the position of the drone hotspots, the association between the ground users and the drone hotspots, as well as the pilot sequence assignment and transmit power for the ground users? To the best of our knowledge, this is the first time that massive MIMO capabilities are considered in self-organizing flying drone networks. We first derive a mathematical formulation of the problem of joint power, association and movement control in mDroneNet, with the objective of maximizing the aggregate spectral efficiency of the ground users. It is shown that the resulting network control problem is a mixed integer nonlinear nonconvex programming (MINLP) problem. Then, a distributed solution algorithm with polynomial time complexity is designed by solving three closely-coupled subproblems: access association, joint pilot sequence assignment and power control, and drone movement control. As a performance benchmark, a globally-optimal but centralized solution algorithm is also designed based on a combination of the branch and bound framework and convex relaxation techniques. Results indicate that the distributed solution algorithm converges fast (within tens of iterations) and achieves a network spectral efficiency very close to the global optimum obtained by the centralized solution algorithm (over 90% in average).

Indoor Visible Light Communications (VLC) are a promising technology to alleviate the looming spectrum crunch crisis in traditional RF spectrum bands. This article studies how to provide throughput-optimal WiFi-like downlink access to users in indoor visible light networks through a set of centrally-controlled and partially interfering light emitting diodes (LEDs). To reduce the effect of interference among users created by the partial overlap of each LED's field of view, we propose LiBeam, a cooperative beamforming scheme, based on forming multiple LED clusters. Each cluster then serves a subset of users by jointly determining the user-LED association strategies and the beamforming vectors of the LEDs. The paper first proposes a mathematical model of the cooperative beamforming problem, presented as maximizing the sum throughput of all VLC users. Then, we solve the resulting mixed integer nonlinear nonconvex programming (MINCoP) problem by designing a globally optimal solution algorithm based on a combination of branch and bound framework as well as convex relaxation techniques. We then design for the first time a large programmable visible light networking testbed based on USRP X310 software-defined radios, and experimentally demonstrate the effectiveness of the proposed joint beamforming and association algorithm through extensive experiments. Performance evaluation results indicate that over 95% utility gain can be achieved compared to suboptimal network control strategies.

In this paper, we design a novel multiview video encoding/decoding architecture for wirelessly multiview video streaming applications, e.g., 360 degrees video, Internet of Things (IoT) multimedia sensing, among others, based on distributed video coding and compressed sensing principles. Specifically, we focus on joint decoding of independently encoded compressively sampled multiview video streams. We first propose a novel side-information (SI) generation method based on a new interview motion compensation algorithm for multiview video joint reconstruction at the decoder end. Then, we propose a technique to fuse the received measurements with resampled measurements from the generated SI to perform the final recovery. Based on the proposed joint reconstruction method, we also derive a blind video quality estimation technique that can be used to adapt online the video encoding rate at the sensors to guarantee desired quality levels in multiview video streaming. Extensive simulation results of real multiview video traces show the effectiveness of the proposed fusion reconstruction method with the assistance of SI generated by an interview motion compensation method. Moreover, they also illustrate that the blind quality estimation algorithm can accurately estimate the reconstruction quality.

Multi-view wireless video streaming has the potential to enable a new generation of efficient and low-power pervasive surveillance systems that can capture scenes of interest from multiple perspectives, at higher resolution, and with lower energy consumption. However, state-of-the-art multi-view coding architectures require relatively complex predictive encoders, thus resulting in high processing complexity and power requirements. To address these challenges, we consider a wireless video surveillance scenario and propose a new encoding and decoding architecture for multi-view video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and independent decoders. The proposed architecture leverages the properties of CS to overcome many limitations of traditional encoding techniques, specifically massive storage requirements and high computational complexity. It also uses estimates of image sparsity to perform efficient rate adaptation and effectively exploit inter-view correlation at the encoder side.

We design a video coding and decoding framework for multi-view video systems based on compressed sensing imaging principles. Specifically, we focus on joint decoding of independently encoded compressively-sampled multi-view video streams. We first propose a novel distributed coding/decoding architecture designed to leverage inter-view correlation through joint decoding of the received compressively-sampled frames. At the encoder side, we select one view (referred to as K-view) as a reference for the other views (referred to as CS-views). The video frames of the CS-view are encoded and transmitted at a lower measurement rate than those of the selected K-view. At the decoder side, we generate side information to decode the CS-views as follows. First, each K-view frame is down-sampled and reconstructed, and then compared with the initially reconstructed CS-view frame to obtain an estimate of the inter-view motion vector. The original CS-view measurements are then fused with the generated side image to reconstruct the CS-view frame through a newly designed algorithm that operates in the measurement domain. We also propose a blind video quality estimation method that can be used within the proposed framework to design channel-adaptive rate control algorithms for quality-assured multi-view video streaming. We extensively evaluate the proposed scheme using real multi-view video traces. Results indicate that up to 1.6 dB improvement in terms of PSNR can be achieved by the proposed scheme compared with traditional independent decoding of CS frames.