Search Results (8,851)

Data of operational conditions of structural components, acquired, e.g., in structural health monitoring (SHM), is of great interest to optimise products from one generation to the next, for example, by adapting them to occurring operational loads. To acquire data for this purpose in the desired quality, an optimal sensor placement for so-called shape and load sensing is required. In the case of large-scale structural components, wireless sensor networks (WSN) could be used to process and transmit the acquired data for real-time monitoring, which furthermore requires an optimisation of sensor node positions. Since most publications focus only on the optimal sensor placement or the optimisation of sensor node positions, a methodology for both is implemented in a Python tool, and an optimised WSN is realised on a demonstration part, loaded at a test bench. For this purpose, the modal method is applied for shape sensing as well as a physics-informed neural network for solving inverse problems in shape sensing (iPINN). The WSN is realised with strain gauges, HX711 analogue-digital (A/D) converters, and Arduino Nano 33 IoT microprocessors for data submission to a server, which allows real-time visualisation and data processing on a Python Flask server. The results demonstrate the applicability of the presented methodology and its implementation in the Python tool for achieving high-accuracy shape sensing with WSNs. Full article

Electromagnetic field (EMF) exposure mapping is increasingly important for ensuring compliance with safety regulations, supporting the deployment of next-generation wireless networks, and addressing public health concerns. While numerous surveys have addressed specific aspects of radio propagation or radio environment maps, a comprehensive and unified overview of EMF mapping methodologies has been lacking. This review bridges that gap by systematically analyzing computational, geospatial, and machine learning approaches used for EMF exposure mapping across both wireless communication engineering and public health domains. A novel taxonomy is introduced to clarify overlapping terminology—encompassing radio maps, radio environment maps, and EMF exposure maps—and to classify construction methods, including analytical models, model-based interpolation, and data-driven learning techniques. In addition, the review highlights domain-specific challenges such as indoor versus outdoor mapping, data sparsity, and model generalization, while identifying emerging opportunities in hybrid modeling, big data integration, and explainable AI. By combining perspectives from communication engineering and public health, this work provides a broader and more interdisciplinary synthesis than previous surveys, offering a structured reference and roadmap for advancing robust, scalable, and socially relevant EMF mapping frameworks. Full article

To foster the performance of wireless communication while saving energy, the integration of Intelligent Reflecting Surfaces (IRS) into autonomous vehicle (AV) communication networks is considered a powerful technique. This paper proposes a novel IRS-assisted vehicular communication model that combines Lagrange optimization and Gradient-Based Phase Optimization to determine the optimal transmission power, optimal interference transmission power, and IRS phase shifts. Additionally, the proposed model help increase the Signal-to-Interference-plus-Noise Ratio (SINR) by utilizing IRS, which leads to maximizes energy efficiency and the achievable data rate under a variety of environmental conditions, while guaranteeing that resource limits are satisfied. In order to represent dense vehicular environments, practical constraints for the system model, such as IRS reflection efficiency and interference, have been incorporated from multiple sources, namely, Device-to-Device (D2D), Vehicle-to-Vehicle (V2V), Vehicle-to-Base Station (V2B), and Cellular User Equipment (CUE). A Lagrangian optimization approach has been implemented to determine the required transmission interference power and the best IRS phase designs in order to enhance the system performance. Consequently, a one-dimensional convolutional neural network has been implemented for the optimized data provided by this framework as training input. This deep learning algorithm learns to predict the required optimal IRS settings quickly, allowing for real-time adaptation in dynamic wireless environments. The obtained results from the simulation show that the combined optimization and prediction strategy considerably enhances the system reliability and energy efficiency over baseline techniques. This study lays a solid foundation for implementing IRS-assisted AV networks in real-world settings, hence facilitating the development of next-generation vehicular communication systems that are both performance-driven and energy-efficient. Full article

The exponential growth in wireless communication demands intelligent and adaptive spectrum-sharing solutions, especially within dynamic and densely populated 6G Cognitive Radio Networks (CRNs). This paper introduces a novel hybrid framework combing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Proximal Policy Optimisation (PPO) for multi-objective optimisation in spectrum management. The proposed model balances spectrum efficiency, interference mitigation, energy conservation, collision rate reduction, and QoS maintenance. Evaluation on synthetic and ns-3 datasets shows that the NSGA-II and PPO hybrid consistently outperforms the random, greedy, and stand-alone PPO strategies, achieving higher cumulative reward, perfect fairness (Jain’s Fairness Index = 1.0), robust hypervolume convergence (65.1%), up to 12% reduction in PU collision rate, 20% lower interference, and approximately 40% improvement in energy efficiency. These findings validate the framework’s effectiveness in promoting fairness, reliability, and efficiency in 6G wireless communication systems. Full article

Modern wireless communication systems increasingly rely on Automatic Modulation Classification (AMC) to enhance reliability and adaptability, especially in the presence of severe signal degradation. However, despite significant progress driven by deep learning, many AMC models still struggle with high computational overhead, suboptimal performance under low-signal-to-noise conditions, and limited interpretability, factors that hinder their deployment in real-time, resource-constrained environments. To address these challenges, we propose the Convolutional Random Fourier Features with Denoising Thresholding Network (CRFFDT-Net), a compact and interpretable deep kernel architecture that integrates Convolutional Random Fourier Features (CRFFSinCos), an automatic threshold-based denoising module, and a hybrid time-domain feature extractor composed of CNN and GRU layers. Our approach is validated on the RadioML 2016.10A benchmark dataset, encompassing eleven modulation types across a wide signal-to-noise ratio (SNR) spectrum. Experimental results demonstrate that CRFFDT-Net achieves an average classification accuracy that is statistically comparable to state-of-the-art models, while requiring significantly fewer parameters and offering lower inference latency. This highlights an exceptional accuracy–complexity trade-off. Moreover, interpretability analysis using GradCAM++ highlights the pivotal role of the Convolutional Random Fourier Features in the representation learning process, providing valuable insight into the model’s decision-making. These results underscore the promise of CRFFDT-Net as a lightweight and explainable solution for AMC in real-world, low-power communication systems. Full article

Optical intrabody wireless nanosensor networks (OiWNSNs) enable groundbreaking biomedical applications via optical nanocommunication within biological tissues. Synchronization is critical for accurate data recovery in these energy- and size-constrained nanonetworks. In this study, we investigate timing synchronization in a highly dispersive and noisy intravascular optical channel, particularly under an on–off keying preamble comprising Gaussian optical pulses. We proposed a synchronization scheme based on the repetitive transmission of a preamble and noncoherent detection using continuous-time moving average filters with multiple integrator windows. The simulation results reveal that increasing the communication distance degrades the synchronization performance. To counter this degradation, we can increase the number of preamble repetitions, which markedly improves the system reliability and timing accuracy due to the averaging gain, although the performance saturates due to the dispersion floor inherent in the blood channel. Moreover, we found that low-resolution nanoreceivers with fewer integrators outperform high-resolution designs in dispersive environments, as they mitigate the energy-splitting problem due to pulse broadening. This tradeoff between temporal resolution and robustness highlights the importance of channel-aware receiver design. Overall, this study provides key insights into the physical layer design of OiWNSNs and offers practical guidelines for achieving robust synchronization under realistic biological conditions. Full article

Background: Quantum machine learning (QML) holds significant promise for advancing medical image classification. However, its practical application to large-scale, high-resolution datasets is constrained by the limited number of qubits and the inherent noise in current quantum hardware. Methods: In this study, we propose the Fused Quantum Dual-Backbone Network (FQDN), a novel hybrid architecture that integrates classical convolutional neural networks (CNNs) with quantum circuits. This design is optimized for the noisy intermediate-scale quantum (NISQ), enabling efficient computation despite hardware limitations. We evaluate FQDN on the task of gastrointestinal (GI) disease classification using wireless capsule endoscopy (WCE) images. Results: The proposed model achieves a substantial reduction in parameter complexity, with a 29.04% decrease in total parameters and a 94.44% reduction in trainable parameters, while outperforming its classical counterpart. FQDN achieves an accuracy of 95.80% on the validation set and 95.42% on the test set. Conclusions: These results demonstrate the potential of QML to enhance diagnostic accuracy in medical imaging. Full article

In this paper, we propose a deterministic epidemic model for malware propagation in wireless mobile environments, taking into account the presence of hotspots. Conventional epidemic models for wireless mobile environments typically assume that mobile hosts randomly move in a given area. However, in real-world scenarios, hotspots such as restaurants and universities often attract large gatherings of people. Therefore, the assumption of purely random movement is not suitable for modeling these scenarios. To realistically represent the movement patterns of mobile hosts, we incorporate the influence of hotspots into our model. Specifically, we formulate a system of Ordinary Differential Equations (ODEs) that captures the infection dynamics in scenarios where one or more hotspots affect host mobility. Our model introduces non-uniform movement probabilities based on cosine similarity, allowing us to reflect the tendency of mobile hosts to cluster around specific locations. By applying these movement probabilities to the ODEs, our epidemic model provides a more accurate representation of malware spreading behavior in hotspot-affected environments. We investigate how malware propagates through mobile hosts and compare the dynamics between the conventional and our proposed models. Numerical experiments demonstrate that our epidemic model effectively captures malware spreading behavior in mobile scenarios with hotspots. Full article

Next-generation wireless communication networks are developing across the world day by day; this requires high data rate transportation over the systems. Millimeter-wave (mm-wave) spectrum with terahertz (THz) bands is a promising solution for next-generation systems that are able to meet these requirements effectively. For such networks, designing new waveforms, providing high-quality service, reliability, energy efficiency, and many other specifications are taking on important roles in adapting to high-performance communication systems. Recently, artificial intelligence (AI) and machine learning (ML) methods have proved their effectiveness in predicting. and optimizing nonlinear characteristics of high-dimensional systems with enhanced capability along with rich convergence outcomes. Thus, there is a strong need for the use of these intelligence-based methods to achieve higher bandwidths along with the targeted outcomes in comparison with the traditional designs. In this work, we provide an overview of the recently published works on the utilization of mm-wave and THz frequencies for designing and implementing various designs to carry out the targeted key specifications. Moreover, by considering various newly published works, some open challenges are identified. Hence, we provide our view about these concepts, which will pave the way for readers to get a general overview and ideas around the various mm-wave and THz-based designs with the use of AI methods. Full article

The rapid development of wireless communication technology is leading to increasingly scarce spectrum resources, making efficient utilization a critical challenge. This paper proposes a Convolutional Neural Network–Long Short-Term Memory-Integrated Gradient-Weighted Class Activation Mapping (GC-CNN-LSTM) model, aimed at enhancing the accuracy of long-term spectrum prediction across multiple frequency bands and improving model interpretability. First, we achieve multi-frequency long-term spectrum prediction using a CNN-LSTM and compare its performance against models including LSTM, GRU, CNN, Transformer, and CNN-LSTM-Attention. Next, we use an improved Grad-CAM method to explain the model and obtain global heatmaps in the time–frequency domain. Finally, based on these interpretable results, we optimize the input data by selecting high-importance frequency points and removing low-importance time segments, thereby enhancing prediction accuracy. The simulation results show that the Grad-CAM-based approach achieves good interpretability, reducing RMSE and MAPE by 6.22% and 4.25%, respectively, compared to CNN-LSTM, while a similar optimization using SHapley Additive exPlanations (SHAP) achieves reductions of 0.86% and 3.55%. Full article

Despite advances in precision irrigation, no systematic review has yet integrated the roles of remote sensing, agro-meteorological data, and wireless sensor networks in high-value, water-sensitive crops such as mango, avocado, and vineyards. Existing research often isolates technologies or crop types, overlooking their convergence and joint performance in the field. This review fills that gap by examining how these tools estimate crop water demand and support sustainable, site-specific irrigation under variable climate conditions. A structured search across major databases yielded 365 articles, of which 92 met the inclusion criteria. Studies were grouped into four categories: remote sensing, agro-meteorology, wireless sensor networks, and integrated approaches. Remote sensing techniques, including multispectral and thermal imaging, enable the spatial monitoring of vegetation indices and stress indicators, such as the Crop Water Stress Index. Agro-meteorological data feed evapotranspiration models using temperature, humidity, wind, and radiation inputs. Wireless sensor networks provide continuous, localized data on soil moisture and canopy temperature. Integrated approaches combine these sources to improve irrigation recommendations. Findings suggest that combining remote sensing, wireless sensor networks, and agro-meteorological inputs can reduce water use by up to 30% without yield loss. Challenges include sensor calibration, data integration complexity, and limited scalability. This review also compares methodologies and highlights future directions, including artificial intelligence systems, digital twins, and affordable Internet of Things platforms for irrigation optimization. Full article

The Extensible Authentication Protocol–Transport Layer Security (EAP-TLS) is a critical authentication protocol for wireless networks and secure IoT communications. However, it faces significant challenges from man-in-the-middle attacks, including message tampering, replay, and certificate forgery. Although model checking techniques have been applied to verify its security properties, the complexity of the EAP-TLS handshake often prevents accurate formal modeling; existing studies rarely assess the communication overhead of protocol enhancements. Moreover, traditional Logic of Events Theory (LoET) struggles to handle transport-layer protocols like EAP-TLS due to their intricate interaction processes. This study proposes a novel formal analysis approach, extending LoET by expanding five event classes, formulating corresponding rules, and introducing new axioms. Formal verification reveals attack paths involving plaintext theft, message tampering, and entity impersonation. The research proposes an enhanced strategy to mitigate these vulnerabilities through hash merging, encryption, and signature methods, alongside analyzing their communication costs to ensure feasibility. Using the extended LoET, the improved protocol is rigorously proven to satisfy strong authentication, thereby enhancing practical security. The proposed method achieves a time complexity of O(n²) and demonstrates superior performance in resisting state explosion compared with related approaches, thus establishing a more efficient and robust framework for EAP-TLS security analysis. Full article

This paper explores the feasibility of using a wireless Inductive Resonant Link (IRL) for wearable posture monitoring. The proposed system is based on magnetically coupled textile resonators and is implemented using a Single Input Multiple Output (SIMO) configuration. In particular, the setup consists of four inductively coupled resonators: one transmitting coil integrated into a textile structure and positioned on the back of the neck, and three receiving coils placed on the shoulders. The magnetic coupling between these elements varies as a function of the user’s posture, making it possible to monitor postural changes by analyzing variations in the transmission coefficients of the link. Unlike traditional sensor-based systems that require multiple components and data processing, the proposed method uses the inherent response of the inductive link to detect posture in a simple and efficient way. To validate the concept, experimental measurements of the scattering parameters were carried out using a compact and low-power vector network analyzer. The results show a consistent and measurable relationship between postural changes and variations in the transmission coefficients, demonstrating the effectiveness of the proposed system in distinguishing between different postures. The findings suggest that inductive resonant wireless links, especially when implemented with textile components, represent a promising alternative to traditional wearable sensor technologies for posture tracking. The approach offers significant advantages in terms of wearability, power consumption, and simplicity, making it suitable for applications in ergonomics, rehabilitation, occupational health, and smart clothing. Full article

The rapid expansion of broadband wireless communication systems, including 5G, satellite networks, and next-generation IoT platforms, has created a strong demand for antenna architectures capable of real-time beam control, compact integration, and broad frequency coverage. Traditional reflectarrays, while effective for narrowband applications, often face inherent limitations such as fixed beam direction, high insertion loss, and complex phase-shifting networks, making them less viable for modern adaptive and reconfigurable systems. Addressing these challenges, this work presents a novel wideband planar metasurface that operates as a polarization rotation reflective metasurface (PRRM), combining 90° polarization conversion with 1-bit reconfigurable phase modulation. The metasurface employs a mirror-symmetric unit cell structure, incorporating a cross-shaped patch with fan-shaped stub loading and integrated PIN diodes, connected through vertical interconnect accesses (VIAs). This design enables stable binary phase control with minimal loss across a significantly wide frequency range. Full-wave electromagnetic simulations confirm that the proposed unit cell maintains consistent cross-polarized reflection performance and phase switching from 3.83 GHz to 15.06 GHz, achieving a remarkable fractional bandwidth of 118.89%. To verify its applicability, the full-wave simulation analysis of a 16 × 16 array was conducted, demonstrating dynamic two-dimensional beam steering up to ±60° and maintaining a 3 dB gain bandwidth of 55.3%. These results establish the metasurface’s suitability for advanced beamforming, making it a strong candidate for compact, electronically reconfigurable antennas in high-speed wireless communication, radar imaging, and sensing systems. Full article

Low-power microcontrollers, wireless sensors, and embedded gateways form the backbone of many Internet of Things (IoT) deployments. However, their limited memory, constrained energy budgets, and lack of standardized firmware make them attractive targets for diverse attacks, including bootloader backdoors, hardcoded keys, unpatched CVE exploits, and code-reuse attacks, while traditional single-layer defenses are insufficient as they often assume abundant resources. This paper presents a Systematic Literature Review (SLR) conducted according to the PRISMA 2020 guidelines, covering 196 peer-reviewed studies on cross-layer security for resource-constrained IoT and Industrial IoT environments, and introduces a four-axis taxonomy—system level, algorithmic paradigm, data granularity, and hardware budget—to structure and compare prior work. At the firmware level, we analyze static analysis, symbolic execution, and machine learning-based binary similarity detection that operate without requiring source code or a full runtime; at the network and behavioral levels, we review lightweight and graph-based intrusion detection systems (IDS), including single-packet authorization, unsupervised anomaly detection, RF spectrum monitoring, and sensor–actuator anomaly analysis bridging cyber-physical security; and at the policy level, we survey identity management, micro-segmentation, and zero-trust enforcement mechanisms supported by blockchain-based authentication and programmable policy enforcement points (PEPs). Our review identifies current strengths, limitations, and open challenges—including scalable firmware reverse engineering, efficient cross-ISA symbolic learning, and practical spectrum anomaly detection under constrained computing environments—and by integrating diverse security layers within a unified taxonomy, this SLR highlights both the state-of-the-art and promising research directions for advancing IoT security. Full article