This paper proposes a novel indoor optical wireless positioning (IOWP) framework that aims to enhance localization precision and robustness through an advanced machine learning (ML)-driven fusion technique. Unlike traditional single-model approaches, the proposed framework uses received signal strength (RSS) data to intelligently combine multiple lightweight ML algorithms, including K-Nearest Neighbors (KNN), Random Forest (RF), and Gaussian Process Regression (GPR). In the training phase, our system utilizes a performance-optimized weight allocation strategy to identify the optimal weights, harnessing the complementary strengths of individual models while mitigating their limitations to achieve exceptional generalization in complex indoor environments. A comprehensive evaluation is conducted under a realistic ray-traced channel model that incorporates typical light distributions, high-order multipath reflections from walls and objects, and mixed diffuse-reflective surface interactions. Performance is assessed in terms of mean positioning error (MPE), 90th percentile (P90) error, and computational complexity. Results demonstrate that the proposed method achieves an MPE of 0.5 cm and a P90 error below 1 cm, offering a practical and scalable solution for next-generation IOWP applications in smart environments.

As 6G and beyond aim for sustainable, high-capacity wireless connectivity, optical wireless communication (OWC) has emerged as a compelling solution. Recent advances in vertical-cavity surface-emitting laser (VCSEL) arrays have significantly enhanced OWC performance, enabling high-speed, low-power data transmission. However, dense VCSEL deployments introduce challenges related to interference and energy efficiency (EE). This paper proposes a scalable precoding framework for EE maximization in fully cooperative VCSEL-based OWC broadcast systems. We formulate a non-convex optimization problem to design the precoding matrix under practical optical constraints while guaranteeing minimum user rates. To solve this, we apply Dinkelbach’s method to handle the fractional objective and the inner approximation technique to iteratively convexify and solve the problem. Simulation results show that our approach consistently outperforms regularized zero-forcing in terms of EE, particularly in large-scale deployments, demonstrating its potential for next-generation sustainable dense OWC networks.

Vehicle-to-Everything (V2X) networks require secure, low-latency data exchanges under dynamic mobility and evolving threats. Conventional blockchain consensus mechanisms, though effective for decentralized trust, lack real-time adaptive authentication capabilities essential for heterogeneous V2X environments. To address this, we introduce Proof of Authentication 2.0 (PoAh 2.0), an adaptive blockchain consensus integrated with an Edge Large Language Model (Edge LLM) and a Random Forest (RF) classifier. Edge LLM semantically analyzes transaction contexts, while RF processes numerical metadata, collectively classifying vehicular transactions into normal, sensitive, or critical, dynamically adjusting cryptographic authentication intensity accordingly. Our approach ensures real-time contextual adaptability, robust resistance against Sybil, replay, and 51% attacks, minimal communication overhead, and data privacy by localized processing. Comprehensive theoretical security analyses with formal proofs underscore the resilience of PoAh 2.0. Empirical validations through realistic V2X scenarios are earmarked as critical future work.

Internet of Things (IoT) networks have been deployed widely making device authentication a crucial requirement that poses challenges related to security vulnerabilities, power consumption, and maintenance overheads. While current cryptographic techniques secure device communication; storing keys in Non-Volatile Memory (NVM) poses challenges for edge devices. Physically Unclonable Functions (PUFs) offer robust hardware-based authentication but introduce complexities such as hardware production and conservation expenses and susceptibility to aging effects. This paper’s main contribution is a novel scheme based on split learning, utilizing an encoder–decoder architecture at the device and server nodes, to first create a Virtual PUF (VPUF) that addresses the shortcomings of the hardware PUF and secondly perform device authentication. The proposed VPUF reduces maintenance and power demands compared to the hardware PUF while enhancing security by transmitting latent space representations of responses between the node and the server. Also, since the encoder is placed on the node, while the decoder is on the server, this approach further reduces the computational load and processing time on the resource-constrained node. The obtained results demonstrate the effectiveness of the proposed VPUF scheme in modeling the behavior of the hardware-based PUF. Additionally, we investigate the impact of Gaussian noise in the communication channel between the server and the node on the system performance. The obtained results further reveal that the achieved authentication accuracy of the proposed scheme is 100%, as measured by the validation rate of the legitimate nodes. This highlights the superior performance of the proposed scheme in emulating the capabilities of a hardware-based PUF while providing secure and efficient authentication in IoT networks.

This paper introduces a novel hybrid physical layer authentication (PLA) method designed to enhance security in multi-node networks by leveraging inherent hardware impairments. The approach specifically exploits carrier frequency offset (CFO), direct current offset (DCO), and phase offset (PO) as multi-attribute features, improving the verification process for authorized users and enhancing the detection of unauthorized devices. Machine learning (ML) models are developed to authenticate devices without prior knowledge of malicious characteristics, resulting in robust and reliable device authentication capabilities. Experimental evaluations conducted on a commercial software-defined radio (SDR) platform demonstrate the effectiveness of the proposed approach under varying signal-to-noise ratio (SNR) conditions. The hybrid PLA scheme integrates advanced feature extraction methods with finely-tuned ML models, optimized through controlled experiments to ensure high performance across diverse network conditions and attack scenarios. Real experimental tests validate the efficacy of the proposed scheme, achieving high authentication rates exceeding 96% and reliable detection rates for malicious device attacks surpassing 95%. Additionally, the approach is highly efficient, with a mean inference time of less than 3.75 milliseconds (ms) and power consumption below 25.5 millijoules (mJ), confirming its suitability for real-time applications in energy-constrained environments.

This paper presents an innovative, yet secure, eHealth framework that leverages Explainable Artificial Intelligence (XAI) and blockchain technology to enhance transparency and security in the IoT-edge-cloud continuum. The framework incorporates SHapley Additive exPlanations (SHAP) to provide real-time, model-agnostic explanations for AI predictions, enabling personalized health monitoring and informed decisionmaking in healthcare. To strengthen data security, a consortium blockchain is employed, and AI is utilized to identify block data sensitivity at the edge within blockchain-integrated IoT architectures using Random Forest (RF) algorithm. This approach achieves high accuracy in validating block sensitivity, enabling efficient selection of authentication mechanisms in the proof of authentication (PoAh) consensus within the consortium blockchain. This ensures heightened protection for sensitive data and contributes to improved overall blockchain performance. The proposed framework is evaluated in an edge computing environment and demonstrates significant potential for advancing security and authentication in eHealth, representing a substantial advancement in healthcare technology.

Unmanned aerial vehicles (UAVs) offer a promising solution for enhancing network coverage, reliability, and data speed in future wireless network generations. However, deploying UAVs as aerial base stations requires careful consideration of crucial design factors, including three-dimensional (3D) placement and performance optimization tailored to specific applications. In this paper, the 3D placement of multiple UAVs, acting as aerial base stations, is investigated in a dynamic user scenario. First, a closed-form expression for the coverage probability is derived. Then, to maximize the network coverage and sum rate while ensuring reliable and energy efficient system, a joint multi-objective optimization problem is formulated considering the real-time user movements. To solve the problem, an improved Chaos-based Bonobo Optimizer (CBO) scheme is proposed which combines chaotic maps with the Bonobo Optimizer (BO) algorithm. The obtained results demonstrate the superior performance of the proposed approach compared with different benchmark algorithms. The results reveal that the proposed CBO algorithm offers a minimum of $\mathbf{1 5 \%}$ and $\mathbf{9 0} \mathbf{~ M b i t / s ~ i m p r o v e m e n t s ~}$ in coverage and sum rate, respectively.

Visible light communication (VLC) is a rapidly growing wireless communication technology for the Internet of Things (IoT) that offers high data rates and low latency, making it ideal for massive connectivity. Efficient resource allocation is essential in VLC networks to minimize inter-symbol and cochannel interferences, which can greatly improve network performance and user satisfaction. This paper focuses on an indoor IoT-based VLC system that utilizes photodetectors (PDs) on users’ cell phones as receivers, with the goal of maximizing system performances and reducing power consumption by selectively activating some PDs while deactivating others. However, this objective presents a challenge due to the inherent non-convex nature of the multi-objective optimization problem, which cannot be solved by analytical means. To address this, we propose an enhanced Aquila optimization (EAO) scheme that improves upon the Aquila Optimizer (AO) by incorporating a fitness distance balance (FDB) function. We evaluate our proposed EAO in various scenarios under different settings, considering both capacity and fairness metrics. Through simulations, we demonstrate the effectiveness of our approach and its superiority over classical algorithms such as Aquila Optimizer (AO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) in finding the optimal solution. Our results confirm that the proposed EAO algorithm can efficiently optimize the system capacity and ensure fairness among all users, providing a promising solution for indoor VLC systems.

This paper investigates streetlight-to-vehicle visible light communication (VLC) system performance for outdoor broadcasting applications. We adopt streetlight lamps as optical internet-of-thing (IoT) devices broadcasting internet services and safety messages to road vehicles. With their asymmetrical radiation patterns, Streetlight antennas are exceedingly different from indoor lighting modules, which deploy ceiling luminaries with ideal Lambertian ones. Therefore, a realistic channel modelling for streetlight-to-vehicle VLC system should be deployed for precise performance insights. We consider a streetlight-to-vehicle VLC system in a two-lane road with multiple light poles uniformly distributed on both sides. Based on that, we investigate the system performance of the streetlight-to-vehicle VLC system in terms of the bit-error-rate (BER) and outage distance and explore the effect of different transceivers and system parameters on the performance. These consider the transmission modulation order, receiver size, height of the streetlight poles, and their corresponding intermediate distances.

This paper proposes a physical layer-based authentication scheme that exploits multiple features from the RF-front-end for wireless mesh networks. Specifically, we engineer the in-phase and quadrature-phase (IQ) samples of the legitimate nodes by generating specific ranges of carrier frequency offset (CFO), phase offset (PO), and DC offset (DCO). This engineered IQ governs all multiple legitimate node transmissions (to cover the entire ranges of CFO, PO, and DCO) and follows a specific probability mass function (PMF). We then obtain an optimal function based on the MSE criterion that closely fits the engineered IQ data, which serves as a reference for authenticating network nodes. In the authentication phase, the optimal function obtained from the IQ data transmissions of the respective node requesting authentication is compared with the optimal reference function. Successful authentication occurs when the difference between the optimal function and reference optimal function falls within predefined thresholds of absolute difference, MSE, and correlation coefficient parameters. Specifically, a node is deemed legitimate only when all three criteria meet the threshold requirements. The node undergoes a second authentication check if only one or two criteria are met. Otherwise, it is marked as a possible intruder. We generated extensive I and Q datasets following the IEEE 802.11 standard waveform to validate the proposed scheme, and the necessary metrics were evaluated. The results showed that instead of being used individually when the underlying criteria of MSE, correlation coefficient, and absolute difference are used together can guarantee better authentication, detection, and false detection rates. The findings indicate that the proposed approach attains a 100% authentication rate at a 5 × 10–2threshold MSE, which represents a 20% improvement over the individual use of MSE.

The reliability of vehicle-to-vehicle (V2V) Visible Light Communication (VLC) systems is affected by several factors, such as car mobility and optics system design. Therefore, this paper focuses on the cars’ relative positions and the design of the optics on the receiving end. Instead of using the rectangle detector, commonly used in the literature, this paper proposes using the polar detector for V2V-VLC systems. We introduce using an imaging receiver with different kinds of optical lenses, such as Fresnel and Aspherical lenses to improve the performance of a V2V-VLC system. We perform a channel modeling study using the non-sequential ray-tracing approach, considering the possibility of horizontal and vertical movement between vehicles. A comprehensive performance comparison of these lenses assumes different vehicle positions on the road. We further investigate the impact of receiver type and lateral shift on the performance of the considered systems. The obtained results demonstrated that with a carefully chosen system and lens parameters, an enhancement of up to 7 dB in total received power could be achieved compared to the case without the lens.

The continued increase in several mobile applications forces to replace existing limited spectrum indoor radio‐frequency wireless connections with high‐speed ones. Visible light communication (VLC) is a promising license free indoor wireless technology that could offer high‐speed connections. However, both co‐channel interference (CCI) from more neighbour transmitters and inter‐symbol interferences (ISI) from multipath reflections limit the performance of VLC downlink channel. In this study, a constraint field of view angular diversity receiver (CFOV‐ADR) is proposed to mitigate these limitations. By optimising the photodetector's (PD) field of view (FOV) angle, the line of sight CCI could be totally eliminated and the ISI could be significantly reduced. The optimal range for FOV angle is calculated in a typical indoor scenario. Furthermore, the zero‐forcing (ZF) algorithm is applied to the conventional ADR (ADR‐ZF) which can significantly eliminate the ISI components. The received optical signal‐to‐interference‐plus‐noise ratio (SINR) is tested at various room positions. The simulation results show that the proposed CFOV‐ADR can achieve higher SINR performance than single receiver, conventional ADR, and ADR‐ZF at all positions and orientations.