Hybrid Li-Fi/Wi-Fi networks are increasingly adopted for high-capacity indoor connectivity to mitigate RF spectrum congestion. However, Li-Fi performance is highly sensitive to user mobility, device orientation, and blockage, which are often overlooked in hybrid designs. This letter proposes OAM-LiFiNet, an orientation-aware, multi-AP hybrid Li-Fi/Wi-Fi framework with selective, sensor-assisted orientation control and mobility-aware link selection. Device orientation is triggered only when it provides measurable SINR or throughput gains, avoiding unnecessary adjustments. By integrating Random Waypoint mobility, blockage awareness, and interference-aware SINR evaluation, the proposed framework improves throughput stability and handover efficiency. Simulation results show up to 84% throughput improvement with fewer orientation adjustments compared to baseline hybrid schemes.

Li-Fi systems depend heavily on a stable Line-of-Sight (LOS) optical path for achieving high data rates. Even mi-nor obstructions caused by user movement or indoor objects can severely degrade performance or lead to link outages. Existing fallback solutions such as switching to Wi-Fi introduce additional latency and overhead. In this demo, we present a practical, low-cost solution to mitigate LOS blockage using a passive stationary mirror that enables Non-Line-of-Sight (NLOS) Li-Fi communication. We design a real-world testbed using commercial Li-Fi transceivers, a reflective mirror, and a blockage emulator. Live performance measurements demonstrate that mirror-assisted NLOS redirection can restore connectivity and maintain usable throughput under LOS failure without modifying Li-Fi hardware. The demo highlights a simple, deployable approach to improving Li-Fi robustness in indoor environments.

Li-Fi and Wi-Fi operate in non-overlapping spectrum, allowing users to receive data simultaneously from both access points (APs). Rising demand for high-speed, reliable indoor communication has spurred the development of hybrid Li-Fi/Wi-Fi networks. To assess the performance of link aggregation (LA) in such hybrid networks (LA-HLWNs), we propose an analytical model to derive rate outage probability. The model accounts for LA-overhead and benchmarks LA-HLWNs against standalone Li-Fi and Wi-Fi systems. Results show that LA-HLWNs consistently achieve lower outage probabilities across diverse scenarios, proving LA’s effectiveness in combining Li-Fi and Wi-Fi strengths to meet growing indoor communication demands.

Li-Fi networks provide high indoor throughput but face challenges due to their reliance on line-of-sight (LoS) and limited coverage. User mobility can degrade throughput as movement beyond a single access point (AP) reduces signal quality, while deploying multiple APs introduces interference and signal fluctuations. This paper uses the random waypoint mobility model to analyze how device orientation impacts signal-to-interference-plus-noise ratio (SINR) and throughput in dynamic indoor environments. Results show that orienting user devices toward a Li-Fi AP enhances SINR in high-interference areas, while a non-oriented state improves throughput in low-interference regions by preserving LoS. The proposed strategy dynamically adjusts device orientation based on real-time SINR and channel conditions, balancing interference reduction and LoS preservation. This adaptive approach optimizes network performance under varying mobility and interference scenarios, improving throughput and reliability in Li-Fi networks.

Hybrid LiFi/WiFi networks leverage the high-speed potential of optical wireless links alongside the coverage reliability of RF technologies, but LiFi's limited range and blockage sensitivity often trigger frequent handovers. Accurate prediction of Signal-To-Interference-plus-Noise Ratio (SINR) and Signal-To-Noise Ratio (SNR) from user position data can improve access point (AP) selection and reduce unnecessary handovers. This paper compares Supervised Learning (SL), Reinforcement Learning (RL), and a hybrid SL+RL approach using SINR/SNR measurements from a LiFi/WiFi environment, evaluating prediction accuracy, throughput, handover rate, and fairness.

Blockages in indoor Li-Fi networks can significantly impact the quality of service (QoS) by disrupting the line-of-sight (LOS) communication channels. This paper proposes two novel approaches for modeling blockage effects in Li-Fi networks: Fixed Obstacle Modeling (FixOM) and Shadow-Aware Blockage Modeling (SAM). FixOM focuses on stationary obstacles, and evaluates obstruction probabilities using geometric analysis of LOS paths based on blockage height, radius, and position. SAM extends this analysis by incorporating partial and complete shadow effects, providing a more realistic representation of network performance degradation. Both approaches are applied to a simulated indoor environment with predefined Li-Fi access point configurations, accounting for practical blockage scenarios. Simulation results quantify the impact of blockages on transmission success probability, highlighting the critical role of static and shadow-based obstructions in network planning. This work advances the understanding of blockage effects in Li-Fi communications, offering robust methodologies for performance modeling and aiding in the design of resilient, high-quality indoor communication networks.

LightFidelity (LiFi) is one of the most promising techniques to meet such high demand for indoor users by utilizing the visible light spectrum. A major challenge of LiFi is that its coverage is relatively limited, as the surrounding walls, objects, and other surfaces mostly absorb visible light. Thus, a number of studies have proposed aggregating the bandwidth of WiFi and LiFi to serve all users within a room. However, complementing LiFi with WiFi via bandwidth aggregation typically comes with an overhead in terms of both aggregation and computation, which reduces the data rates that can be used. Furthermore, data provided to users are often also limited by the backhaul capacity, which is typically wired Ethernet in indoor settings. In this work, we model the utilization of the functioning of WiFi and LiFi access points as a two-dimensional flow graph and show that the problem of maximizing the sum of data rate across all users is NP-Hard in practice. We then design an algorithm FLADA to solve this problem by solving its relaxed version where the variables are treated as real numbers, and then rounding to the nearest integer. We prove formally that it provides a solution that is at least 0.5× the optimal. We further compare it with a greedy baseline approach through extensive simulations and show that it outperforms it by up to 81.6%.

Light Fidelity (Li-Fi) offers high data rates in confined areas but is limited by susceptibility to signal blockages and constrained coverage. To address these limitations, Li-Fi is often integrated with Wi-Fi in a heterogeneous network (HetNet), leveraging Li-Fi's speed with Wi-Fi's broader coverage. However, hybrid networks introduce frequent handovers between Li-Fi and Wi-Fi access points (APs), especially for users near blind spots or with limited mobility. This paper proposes a link aggregation (LA) approach for a Li-Fi and Wi-Fi HetNet (LA-HLWN), where users receive data from both types of APs simultaneously, enhancing data rates and reducing handover frequency. To manage network load effectively, we develop an algorithm for dynamic data rate allocation, formulated as a multi-dimensional commodity flow problem that considers factors such as user requirements, AP signal quality, and backhaul conditions. Given the NP-hard nature of this problem, our solution models it initially as an integer linear programming (ILP) challenge and then applies a relaxed linear programming (LPP) approach with a rounding technique for real-time allocation. Additionally, to balance throughput and reduce the computational overhead, we implement a strategy to control handover frequency and load distribution across APs, achieving a 54% improvement in throughput. This approach demonstrates the potential of LA in enhancing user satisfaction and system performance in hybrid Li-Fi/Wi-Fi networks.

Light fidelity (LiFi) is an emerging wireless networking technology of visible light communication (VLC) paradigm for multiuser communication. This technology enables high data rates due to the availability of large visible light spectrum. While current studies have shown the potential for LiFi technology, they borrow the MAC-layer protocols from traditional WiFi. However, a number of prior studies have shown the challenges faced by the MAC-layer of WiFi in the presence of large number and types of devices. In this work, we show that the hybrid-coordination-function-controlled-access (HCCA) MAC protocol in LiFi provides higher throughput than the traditional CSMA/CA mechanism to user devices. We also show that HCCA has the limitation of higher message overhead in the presence of a large number of devices. We also evaluate the collision probability, busy channel probability, and delay for HCCA and CSMA/CA MAC protocol. We utilize both theoretical analysis and extensive simulations to study these performance tradeoffs and identify a threshold when a LiFi access point should switch to HCCA from CSMA/CA and vice-versa. Finally, based on our findings, we design a hybrid-MAC mechanism that switches between HCCA and CSMA/CA based on the number and type of devices present. Our evaluation shows that this hybrid mechanism can outperform both HCCA and CSMA/CA individually in the presence of different number of devices.

In recent years, LiFi has become increasingly pop-ular as an indoor communication technology that utilizes the unlicensed visible light and infra-red spectrum to transmit data. A major challenge of utilizing LiFi is that its area of coverage is limited. Thus, a large number of LiFi access points (APs) is often complemented by deploying a WiFi AP to form a hybrid WiFilLiFi network. However, such deployment does not lead to any additional improvement in the performance of WiFi or LiFi APs. Recent WiFi APs are known to have high overhead due to the requirement of channel state information (CSI), which is essential for utilizing spatial multiplexing. Thus, in this work, we propose a system called WiLiConnect (WiFi-LiFi Connectivity with CSI), which communicates the CSI requirement of the WiFi channel through the LiFi APs, thereby reducing the overhead of WiFi APs. We formulate this problem of load-balancing the overhead of CSI sharing across the LiFi APs, and show that the general problem is NP-Hard. We then propose a round-robin algorithm to solve a special case of the problem, where all the users are assumed to have a single antenna. We further utilize extensive simulation to show that WiLiConnect significantly reduces the overhead of sending CSI. Specifically, WiLiConnect incurs only 0.06% overhead on a WiFi AP having 8 antennas on the total sum rate.

Light Fidelity (Li-Fi) emerges as a cutting-edge wire-less networking paradigm within the domain of light communication (LC) technology. The technology's prowess lies in its ability to harness the expansive visible and invisible light spectrum, facilitating high-capacity data transmission. Li-Fi serves as a fully integrated indoor wireless communication solution, employing light as the conduit for data transfer, distinguishing itself from Wi-Fi, which operates on radio frequencies. In conventional indoor settings, the establishment of robust backhaul connectivity necessitates the deployment of fiber optic cables. However, this approach introduces logistical complexities. To circumvent these challenges, we propose the adoption of point-to-point (P2P) Li-Fi Transceivers as an alternative solution to traditional fiber optic cables to replace the fiber optic cable connectivity for a backhaul of more than 1 Gbps. This alternative solution ensures uninterrupted backhaul connectivity with a seamless aggregated uplink-downlink rate of 1.5 Gbps, covering distances of up to 2.5 meters. This strategic shift not only mitigates logistical intricacies but also augments the efficiency and performance of indoor wireless communication networks.

Light Fidelity (LiFi) is complementary to Wireless Fidelity (WiFi) that uses light as the communication medium with leveraging the indoor lighting infrastructure. LiFi has limited coverage but provides a higher data rate, in contrast WiFi has broader coverage with a comparatively lower data rate. The hybrid LiFi/WiFi network (HLWN) takes advantage of both LiFi and WiFi to provide higher average data rate with over a larger coverage area. In HLWN, LiFi and WiFi can coexist and complement each other, as the spectrum of LiFi and WiFi are non-overlapping and non-interfering. In this work, we demonstrate the realistic performance of a hybrid LiFi/WiFi network in a physical testbed environment for different indoor scenarios and evaluate its data rate and handover latency performance.

The increasing demand for extensive data requirements in indoor wireless communication has become a center of attention. In response, Light-Fidelity (Li-Fi) technology has emerged as a viable alternative to Wireless-Fidelity (Wi-Fi), offering a multitude of benefits. These include higher data rates, access to a wide unlicensed spectrum, and a secure and environmental friendly communication system. However, Li-Fi has limited coverage. In contrast, Wi-Fi has better coverage albeit with lower data rates. The judicious utilization of both Li-Fi and Wi-Fi can provide higher throughput with broad coverage. In this work, we develop a realistic experimental setup for a hybrid Li-Fi/Wi-Fi network (HLWN) to showcase the performance of HLWN superiority over a Wi-Fi-only network in terms of network throughput and coverage. The results validate the potential of HLWN to meet the increasing demands of indoor wireless communication, particularly for scenarios with multiple users and mobility requirements. This research contributes to the advancement of HLWN technology and paves the way for its practical implementation in real-world scenarios. Through these evaluations, we measured the average throughput performance of HLWN. This metric provides valuable insights into the network's ability to efficiently transmit data and seamlessly handle device transitions between Li-Fi and Wi-Fi access points.

The Light Fidelity (LiFi) and Wireless Fidelity (WiFi) technologies operate in non-overlapping spectrum, thus, a user is capable of receiving data concurrently from both LiFi and WiFi Access points (APs). Therefore, facilitating a link aggregation (LA) enabled heterogeneous LiFi-WiFi network (HLWN). In this paper, we have analysed the performance of LA enabled HLWN by utilizing an LA based on SINR (LA-SINR) algorithm and compared it with the conventional indoor access networks like the hybrid LiFi-WiFi, standalone WiFi and standalone LiFi. Additionally, an intuitive LA for enhancement of QoS (LA-EQoS) algorithm has been proposed to further improve the quality of service (QoS) and average data rate performance of LA enabled HLWN. The simulation results validate that for a predefined QoS requirement, proposed LA-EQoS provides 80% to 100% coverage for more number of users as compared to other networks. Moreover, for a given outage constraint, the maximum QoS offered by LA-EQoS is 1.5 times higher than other networks. 16

This chapter presents a smart healthcare monitoring system for patients by implanting wireless sensors on the body which collect different vital physiological parameters such as heart rate, blood pressure, body temperature, glucose level etc. The sensors are controlled by the Internet of Things devices and communicate the patient’s health record to the monitoring station using 5G services. This 5G service helps to enable the immediate treatment for the critical patient with no time. In addition, the rate of detection of the critical condition of the patient is enhanced with the application of machine learning models for better and efficient prediction. Therefore, the improvement of healthcare monitoring system enhances human life value by integrating advanced and cutting edge technologies as a consequence of which the frequent visit of patients to the hospitals or emergency units can be reduced.