Reconfigurable Intelligent Surfaces (RIS) are increasingly viewed as a key technology for re-shaping wireless propagation in 6G networks. While existing work has examined RIS theory, algorithms, and hardware prototypes, far less attention has been given to the simulation tools that support reproducible, deployment-oriented evaluation. This paper presents a systematic literature review (SLR) of RIS simulators with emphasis on their readiness for real-world integration. Following PRISMA 2020 guidelines, we analyze over 310 publications and classify 112 relevant studies into three core dimensions: mobility models, channel estimation techniques, and RIS control strategies. The survey provides a unified taxonomy, evaluates leading simulators and testbeds, and maps their capabilities to six representative deployment scenarios defined by ETSI ISG RIS and ITU IMT-2030. Key findings highlight critical gaps, including the lack of real-time evaluation frameworks, weak support for high-mobility or non-terrestrial settings, and limited mechanisms for multi-RIS coordination. We also identify opportunities for AI-driven RIS control, standardized benchmarking, and simulation-to-hardware integration. By consolidating tools, scenarios, and open challenges, this review offers a practical reference for researchers and engineers aiming to bridge the gap between theoretical RIS models and field-ready

Monitoring network traffic to maintain the quality of service (QoS) and to detect network intrusions in a timely and efficient manner is essential. As network traffic is sequential, recurrent neural networks (RNNs) such as long short-term memory (LSTM) are suitable for building network intrusion detection systems. However, in the case of a few dataset examples of the rare attack types, even these networks perform poorly. This paper proposes to use oversampling techniques along with appropriate loss functions to handle class imbalance for the detection of various types of network intrusions. Our deep learning model employs LSTM with fully connected layers to perform multi-class classification of rare network attacks. We enhance the representation of minority classes: i) through the application of the Synthetic Minority Over-sampling Technique (SMOTE), and ii) by employing categorical focal cross-entropy loss to apply a focal factor to down-weight examples of the majority classes and focus more on hard examples of the minority classes. Extensive experiments on KDD99 and CICIDS2017 datasets show promising results in detecting network intrusions (with many rare attack types, e. g., U2R, R2L, Probe, Infiltration, Hearbleed, etc.).

Joint stiffness affects over 350 million people worldwide, creating demand for intelligent systems for detection and monitoring. Patients with joint stiffness need continuous rehab guided by experts, highlighting the need for tele-rehabilitation. This work presents a flexible, biodegradable, and economical strain sensor for joint stiffness monitoring, fabricated through a simple, cost-effective method. The sensor uses cotton fabric as a substrate and conductive paste from recycled dry-cell electronic waste for electrodes. A modified interdigitated capacitive (MIDC) structure is used, achieving high sensitivity with a GF value of 1006, response and recovery times of 0.38 sec. On-body testing on wrist, knee, and elbow joints yielded a minimum resolution of 5°. The proposed MIDC strain sensor is ideal for joint stiffness monitoring in tele-rehabilitation.

This study presents a machine learning (ML)-based localization method for improving location estimation accuracy in wireless networks, especially in challenging environments where traditional techniques often fall short. Conventional methods rely on a limited number of multipath components (MPCs), leading to inaccurate localization in complex environments. By leveraging a novel dataset generated from ray-tracing simulations in urban and campus environments, we propose a deep neural network (DNN)-based method that incorporates rich channel metrics such as angle of arrival (AoA), time of arrival (ToA), and received signal strength (RSS). The DNN is trained on diverse scenarios, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, and outperforms traditional MPC-based methods, reducing localization error by up to 20%. Our approach challenges the conventional use of only 3 MPCs for localization and demonstrates that a larger number of MPCs enhances accuracy, particularly in urban and obstructed environments. This research provides important insights into the potential of ML-driven solutions for improving localization accuracy in next-generation wireless systems , such as 5G and beyond.

In the mobile network, digital twin (DT)-aided artificial intelligence (AI)-empowered link control is vital to enhance the performance of wireless communication. This paper proposes a deep reinforcement learning (DRL)-convex optimization enhanced time-frequency domain power allocation scheme to reduce the long-term average bit error rate (BER) in multi-user orthogonal frequency division multiplexing (OFDM) systems. To alleviate performance loss caused by trial-and-error during the training period of DRL algorithms, we design a novel practical DT-aided “prediction-then-decision” autonomous wireless link control framework considering the periodic interaction mechanism between the DT and its physical counterpart. A Transformer-based channel generator Mucomformer is implemented in the DT layer to generate large amounts of multi-user virtual channel state information (CSI) in future transmission frames. In addition, the DRL agent is trained over the DT channel in advance and executed in the real-world OFDM system to generate the optimal transmission strategy by considering the interaction mechanism between the DT and the physical counterpart. The simulation results demonstrate that the proposed Mucomformer has lower average prediction error of 2.51 dB compared to the Transformer baseline. The DRL and convex-based power allocation scheme further outperforms the classic strategy. Moreover, the practical DT-aided autonomous link control framework effectively mitigates the performance impairment, achieves an average BER performance gain 45.65% higher than that without DT and achieves faster convergence during the whole training period.

Compared to broader physical activities, detecting nuanced respiratory movements poses a significant challenge in indoor health monitoring systems. While respiratory activity can be conceptualized as periodic chest movements akin to mechanical vibrations, uncontrollable environmental factors often introduce noise into detected radar signals. The clutter in the field of view, especially metallic objects, such as hospital steel beds, degrades the performance of radar physiological monitoring: 1) amplifying noise of multipath effects and 2) misleading the informative localization module. In this article, we propose a preprocessing scheme of Search-Voxel Ellipse Normalization for respiratory detection system, including an ellipse normalization method combined with the fitting-cost voxel selection policy, to improve the respiration detection performance using MIMO frequency modulated continuous wave radar. This article provides an in-depth assessment of the designed system, including a metal-insulated room test, involving ten participants in different postures. The results show notable performance improvements of our proposed ENDTW-MVMD method, especially in lowering the mean absolute error from the best state-of-the-art 0.93–0.75 bpm and stabilization in voxel selection. The proposed approach is thoroughly evaluated against established methods across various dimensions, such as voxel selection, independent performance, frequency estimation, and ablation studies.

Intelligent transportation systems increasingly depend on wireless communication for broadcasting traffic messages and facilitating real-time vehicular communication. In this context, message authentication is crucial for establishing secure and reliable communication. However, security solutions must consider the dynamic nature of vehicular communication links, which fluctuate between line-of-sight (LoS) and non-line-of-sight (NLoS) due to obstructions. This paper proposes a lightweight cross-layer authentication scheme that employs public-key infrastructure (PKI)-based authentication for initial legitimacy detection/handshaking while using key-based physical-layer re-authentication for message verification. This approach reduces signature generation and signaling overheads associated with each transmission, thereby enhancing network scalability. However, the receiver operating characteristic (ROC;Pd: detection vs.PFA: false alarm probabilities) of the latter decreases with lower signal-to-noise ratio (SNR). To address this, we investigate the use of reconfigurable intelligent surfaces (RISs) to strengthen the SNR directed toward the designated vehicle in shadowed areas (i.e., NLoS scenarios), thereby improving the ROC. Theoretical analysis and practical implementation are conducted using a 1-bit RIS consisting of 64×64 reflective metasurfaces. Experimental results show a significant improvement inPd, increasing from 0.82 to 0.96 at SNR = −6 dB for an orthogonal frequency-division multiplexing (OFDM) system with 128 subcarriers. We also conducted informal and formal security analyses using Burrows-Abadi-Needham (BAN) logic to prove the scheme’s ability to resist passive and active attacks. Furthermore, the proposed scheme reduces computational and communication overheads by 43% and 13%, respectively, compared to traditional cryptographic methods, demonstrating its superiority for real-time, challenging communication scenarios.

Contactless vital signs detection has the potential to advance healthcare by offering precise and convenient patient monitoring. This groundbreaking approach not only streamlines the monitoring process, but also allows continuous, real-time assessment of vital signs, allowing early detection of anomalies and prompt intervention. This paper presents a novel framework for contactless vital sign s detection using continuous-wave (CW) radar and advanced signal processing techniques. We achieved unprecedented precision in capturing 1,261 samples for radar based heart sound waveforms compared to the ground truth ECG signal. Further, our heart sounds method yields highly accurate human heart pulse readings, surpassing previous benchmarks with a mean absolute percentage error (MAPE) of 0.0129 and mean absolute error (MAE) below one (0.8712). In addition, we derive d heart rates from the heart sound waveforms and compare them with conventional radar-derived heart rates and ground truth ECG signal. Through analysis, we identifi ed regions where conventional radar based methods exhibit limitations. Our approach demonstrates minimal errors and superior accuracy across all heart rate states, which can potentially set new standards for noninvasive vital sign monitoring.

Non-verbal communication is a crucial element of human interaction, serving as a powerful tool for expressing emotions, establishing connections, and understanding others beyond verbal language. The recognition of non-verbal cues has held significant importance in recent years, particularly for individuals with disabilities like deafness or muteness. This recognition plays a crucial role in enabling effective communication for these groups. However, existing camera-based systems for detecting non-verbal cues have drawbacks, including privacy concerns, difficulties in varying lighting conditions, the need for complex training, and operational range limitations. In this study, we employ contactless sensing technology to detect non-verbal cues such as Good Idea, Thinking, Worried, Normal, Shocked, and OK. This proof-of-concept study demonstrates the efficacy of this privacy-preserving system. The data was first transformed into spectrograms and then processed using deep learning models like ResNet50 and VGG16, achieving remarkable classification accuracy, notably 95.83% using ResNet50.

The research investigates the data rates of a wide-band multi-user system by optimizing the phases of the reconfigurable intelligent surfaces (RISs) elements and performing fair power allocation for subcarriers. A practical beamforming codebook is developed to select the RIS configuration that maximizes the signal-to-noise ratio (SNR). This guides the iterative power and semidefinite relaxation (SDR) algorithms for finding the optimal RIS configuration. The iterative power method is validated by comparing the achievable data rate and the computational complexity with the existing techniques in the literature. Simulation results revealed that the data rate performance of our proposed iterative power method is comparatively close to the well-known approaches such as the SDR and the strongest tap maximization (STM). Notably, our method exhibits superior performance to STM in non-line-of-sight (NLoS) channels, while also maintaining lower computational complexity compared to SDR.

This paper introduces a compact dual-polarized antenna array designed for the 28/38 GHz frequency bands. The dimensions of the antenna array are 32×20 mm2, offering two broad impedance bandwidths: 2.7 GHz (26.6‒29.3 GHz) and 5.2 GHz (35‒40.2 GHz). To achieve polarization diversity, the antenna is excited by two orthogonal feed lines. Each element of the antenna array comprises a fractal radiating patch, enabling multiple resonances, and a rectangular slot in the radiating patch, enhancing the antenna's impedance bandwidth. The proposed antenna array exhibits unidirectional radiation patterns with gains of 13 dBi and 12.5 dBi at the lower and upper frequency bands, respectively. Furthermore, the isolation between the ports exceeds −10 dB for both operating bands. With its simple structure, compact size, and high gain, the proposed antenna is deemed suitable for integration into 5G communication systems.

The asymptomatic nature of cancer in its early stages has been a major problem in medical research. Terahertz (THz) radiation has great potential in cancer detection due to its outstanding properties of non-ionization, non-invasion, and accuracy. As an auspicious THz radiation source, the photoconductive antenna (PCA) is now becoming adopted in investigating and analyzing THz applications. In this paper, a fabricable novel structure of THz PCA working on THz band is proposed. The design of plasmonic contact fingers is applied to the antenna gap in order to improve the efficiency by enhancing the absorption of surface current, leading to a larger THz radiation. The proposed design is analysed and compared to the conventional design, indicating that the directivity and E-field magnitude of the proposed work are significantly improved in range of 0.3 to 0.6 THz, up to 7.33 dBi and 20.33 V/m. The E-field distribution inside the structure and at the electrodes-substrate interface is provided to show the superior optical absorption by the plasmonic contact fingers. The proposed PCA fulfils the requirements of cancer detection and other medical biosensing scenarios.

This paper presents the RF performance analysis of various designs of RF - MEMS shunt capacitive switches in coplanar configuration. The switches consist of slotted bridges of a variety of shapes such as circular, hexagonal, and rectangular. The design consisting of the rectangular slotted bridge exhibits the best performance with a maximum insertion loss of 0.08 dB, return loss of greater than 20 dB and isolation of more than 30 dB. The design has an operational frequency range from DC-40 GHz. The designs are modelled and simulated using High Frequency Structure Simulator (HFSS).

This paper presents a broadband circularly polar-ized (CP) antenna array for millimeter-wave (mmWave) applications. The overall size of the$1 \times 4$antenna array is$28\times 23\text{mm}^{2}$and covers the 28 GHz to 38 GHz bands. Each element of the antenna array consists of a wide square slot (WSS) with a T-shaped stub extended in the ground plane. The impedance matching and the CP bandwidth is improved by introducing asymmetric fractals in the ground plane. The antenna array elements are fed by a simple 4-way power divider network. The simulated and measured impedance bandwidth of the array is 49% (26.6 GHz to 42.9 GHz) and 49% (26.6 GHz to 42.4 GHz) respectively. The simulated and measured 3dB axial ratio (AR) bandwidth is 29.5% (34 GHz to 44 GHz) and 28.5% (35 GHz to 42GHz) respectively. The peak gain of 8.5-11.2 dBi is measured throughout the bandwidth. The proposed antenna array is suitable for commercial use in future 5G communication systems.

Breathing pattern is a representation of human breathing in the rate, depth, and rhythm, which can reflect physical and mental health conditions. Capturing and identifying abnormal breathing patterns can help localize associated disorders and have important implications for the patient or the potential patient. In this paper, a breathing patterns recognition system is proposed to monitor and identify abnormal breathing patterns in a contactless, unobtrusive and comfortable way. The system utilize the designed prototype based on WiFi signal and deep learning architecture to achieve the reliable measurement and recognition of respiratory patterns. We first develop a series of data preprocessing method to capture accurately the time-domain breathing signal from received data. Then, we apply a combined convolutional–long short-term memory (CNN-LSTM) network model to classify six distinct respiratory patterns (Eupnea, Tachypnea, Bradypnea, Biots, Cheyne–Stokes, and Kussmaul). The experimental results demonstrate that the proposed system have the ability to effectively classify the afore-mentioned six breathing patterns, which combines a series of novel data processing methods with the obtained CNN-LSTM model. The accuracy, precision, recall and F1-scores obtained by the CNN-LSTM model on the collected test set were 97.8%, 97.9%, 97.8% and 97.8%, respectively. In addition, the proposed system achieved 96.7%, 97.5%, and 98.1% recognition accuracy in different indoor environments. Overall, the proposed contactless breathing patterns recognition system validates the feasibility of long-term continuous respiratory patterns recognition, and provides a potential solution for the auxiliary diagnosis of diseases.

Deforestation poses a significant global environmental challenge with far-reaching consequences for biodiversity, climate change, and livelihoods. In this context, applying advanced technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI) holds immense promise. This paper aims to comprehensively review and analyze the role of IoT, AI, and remote sensing technologies in monitoring, detecting, predicting, and preventing deforestation. By providing real-time data and enabling early detection, these technologies contribute to addressing activities like illegal logging, plant diseases, and forest fires. This review presents an overview of the advantages and limitations of these technologies, accompanied by an analysis of their current state and future potential. Key technologies covered include IoT, satellite imagery, drones, and AI algorithms, with each offering unique applications. Importantly, this paper underscores the significance of these technologies in protecting forests and the diverse species they support. The findings discussed herein aim to inform ongoing debates and provide a foundation for further research in this crucial domain. Ultimately, the knowledge gained from this research has the potential to guide practical interventions and policies for effective forest conservation.

Life on Earth depends on healthy oceans, which supply a large percentage of the planet’s oxygen, food, and energy. However, the oceans are under threat from climate change, which is devastating the marine ecosystem and the economic and social systems that depend on it. The Internet of Underwater Things (IoUT), a global interconnection of underwater objects, enables round-the-clock monitoring of the oceans. It provides high-resolution data for training machine learning (ML) algorithms for rapidly evaluating potential climate change solutions and speeding up decision making. The sensors in conventional IoUT are battery powered, which limits their lifetime, and constitutes environmental hazards when they die. In this article, we propose a sustainable scheme to improve the throughput and enable wireless charging of underwater networks, enabling them to potentially operate indefinitely. The scheme is based on simultaneous wireless information and power transfer (SWIPT) from an autonomous underwater vehicle (AUV) used for data collection. We model the problem of jointly maximizing throughput and harvested power as a Markov decision process (MDP), and develop a model-free reinforcement learning (RL) solution. The model’s reward function incentivises the AUV to find optimal trajectories that maximize throughput and power transfer to the underwater nodes while minimising its own energy consumption. To the best of our knowledge, this is the first attempt at using RL for this application. The scheme is implemented in an open 3-D RL environment specifically developed in MATLAB for this study. The performance results show up 207% improvement in energy efficiency compared to those of a random trajectory scheme used as a baseline model.

Reconfigurable intelligent surface (RIS) has been identified as a promising disruptive innovation to realize a faster, safer and more efficient communication system. In this paper, we study the broad beamwidth design of RIS. A problem is formulated to achieve broadbeam with maximum and equal power gain within a pre-defined angular region given constraints of the unit modulus weights of RIS. Since the formulated problem is non-convex, where the optimal solution cannot be analytically obtained, we propose the difference-of-convex-based semi-definite programming (DC-SDP) algorithm. In addition, as important guidance of signal coverage for arbitrary angular regions, we mathematically derive the relationship between the angular range of the spatial sector and the maximum average received power. The upper bounds of the average received power with different RIS configurations are also obtained, where uniform rectangular array (URA) and uniform linear array (ULA) are considered. Simulation results demonstrate the effectiveness of our derivations and verify that our proposed DC-SDP algorithm is applicable in practical applications and outperforms other baseline methods. Overall, this work can be viewed as a foundation for the practical implementation of RIS on coverage enhancement and can also be seen as an initial step towards achieving channel estimation.

Video inpainting aims to fill in missing regions of a video after any undesired contents are removed from it. This technique can be applied to repair the broken video or edit the video content. In this paper, we propose a depth-guided deep video inpainting network (DGDVI) and demonstrate its effectiveness in processing challenging broken areas crossing multiple depth layers. To achieve our goal, we divide the inpainting into depth completion, content reconstruction, and content enhancement. Three corresponding modules are designed to implement a process-flow. Firstly, we develop a depth completion module based upon the spatio-temporal Transformer which is used to obtain the completed depth information for each video frame. Secondly, we design a content reconstruction module to generate initially inpainted video. With this module, the contents of the missing regions are composed via the depth-guided feature propagation. Thirdly, we construct a content enhancement module to enhance the temporal coherence and texture quality for the inpainted video. All of proposed modules are jointly optimized to guarantee the high inpainting efficiency. The experimental results demonstrate that our proposed method provides better inpainting results, both qualitatively and quantitatively, compared with the previous state-of-the-art.

In this paper, we focus on reconfigurable intelligent surface (RIS)-aided resource allocation under base stations (BSs)‘ non-cooperation scheme, where the RIS is solely controlled by one BS and should not affect the communication of the adjacent BS. The minimum quality-of-service (QoS) of users served by the RIS-aided BS, minimum effects on the channel quality of the adjacent BS, the sub-channel assignment rule, and the total transmit power constraint are taken into account. Based on these constraints, the sum-rate of users served by the RIS-aided BS is maximized by jointly optimizing the RIS passive beamforming, power allocation, and sub-channel assignment. To tackle the non-convex problem, an efficient algorithm exploiting the techniques of block coordinate descent (BCD) is developed. A two-sided matching (TSM) algorithm is firstly applied to solve the discrete sub-channel assignment optimization. Then the power allocation and RIS passive beamforming are optimized iteratively. To address the non-convexity in optimizing the power allocation and RIS beamforming, a successive convex upper bound approx-imation method and a multi-ratio fractional programming (FP) with Taylor series approximation-based successive cancellation algorithm (SCA) are used, respectively. The convergence of simulation results proves the validity of our proposed algorithm, and the effects of the numbers of RIS elements and the total transmit power are studied.

Head movement holds significant importance in con-veying body language, expressing specific gestures, and reflecting emotional and character aspects. The detection of head movement in smart or assistive driving applications can play an important role in preventing major accidents and potentially saving lives. Additionally, it aids in identifying driver fatigue, a significant contributor to deadly road accidents worldwide. However, most existing head movement detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. This novel privacy-preserving system utilizes UWB-radar technology and leverages Deep Learning (DL) techniques to address the mentioned issues. The system focuses on classifying the five most common head gestures: Head 45L (HL45), Head 45R (HR45), Head 90L (HL90), Head 90R (HR90), and Head Down (HD). By processing the recorded data as spectrograms and leveraging the advanced DL model VGG16, the proposed system accurately detects these head gestures, achieving a maximum classification accuracy of 84.00% across all classes. This study presents a proof of concept for an effective and privacy-conscious approach to head position classification.

Ambient computing is gaining popularity as a major technological advancement for the future. The modern era has witnessed a surge in the advancement in healthcare systems, with viable radio frequency solutions proposed for remote and unobtrusive human activity recognition (HAR). Specifically, this study investigates the use of Wi-Fi channel state information (CSI) as a novel method of ambient sensing that can be employed as a contactless means of recognizing human activity in indoor environments. These methods avoid additional costly hardware required for vision-based systems, which are privacy-intrusive, by (re)using Wi-Fi CSI for various safety and security applications. During an experiment utilizing universal software-defined radio (USRP) to collect CSI samples, it was observed that a subject engaged in six distinct activities, which included no activity, standing, sitting, and leaning forward, across different areas of the room. Additionally, more CSI samples were collected when the subject walked in two different directions. This study presents a Wi-Fi CSI-based HAR system that assesses and contrasts deep learning approaches, namely convolutional neural network (CNN), long short-term memory (LSTM), and hybrid (LSTM+CNN), employed for accurate activity recognition. The experimental results indicate that LSTM surpasses current models and achieves an average accuracy of 95.3% in multi-activity classification when compared to CNN and hybrid techniques. In the future, research needs to study the significance of resilience in diverse and dynamic environments to identify the activity of multiple users.

6G is expected to revolutionize the Internet of things (IoT) applications toward a future of completely intelligent and autonomous systems. Conventional machine-learning approaches involve centralizing training data in a data center, where the algorithms can be used for data analysis and inference. To promote green computing in IoT applications, Machine-2-Machine (M2M) technologies are largely focused on lowering energy consumption and creating effective IT infrastructure. In this paper, we introduce an AI-enabled One-Shot Interference(O-SI) Knowledge-Driven unified model block sharing (K-Dumbs) framework in which actionable knowledge is aggregated from the training perception robots to facilitate others at the Edge in the vicinity. To demonstrate the practicality of the proposed concept, we explore a K-Dumb Fed-Average (FedAvg) algorithm to meet the massively distributed and unbalanced pattern and privacy requirement of the Internet of Robotic Things(IoRT). Simulation results show that, when compared to traditional Federated Learning (FL) systems, the proposed K-Dumb FedAvg architecture delivers higher information-sharing and learning quality. In addition, we validate our method using MNIST handwritten digits for training image processing with an accuracy that is close to the centralized solution for up to 80% reduction in the amount of exchange data with the O-SI method. Furthermore, the suggested solution reduces IoRT energy consumption by up to 10 times and protects privacy.

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are expected to be widely used in future wireless communication networks to improve spectrum and energy efficiency. In this paper, RIS-assisted UAV communication systems are studied and analysed by developing a comprehensive mathematical framework for examining their outage performance. In order to study the effect of the RIS on UAV communications, two system scenarios are considered: in the first scenario, the UAV acts as an aerial base station (BS) serving a ground user to offload the terrestrial network, and in the second system, the UAV acts as an aerial user served by a terrestrial BS. We present channel models considering the UAV’s unique characteristics, propose a closed-form approximation for the signal-to-noise-ratio (SNR) distribution, and derive an analytical expression for the relevant outage probability. Results show that RIS can significantly improve the performance of UAV communication systems by introducing energy-efficient and reliable links. This opens the door for UAV networks, which are highly scalable, adaptable, and robust to environmental changes. Furthermore, the results show that the UAV position and altitude optimisation significantly affects the outage performance.

Joint communication and sensing technique has been adopted for smart home design and other applications recently. WiFi sensing, which utilizes mutually orthogonal channel response to monitor the changes in the medium, is regarded as one of key techniques in this field. Human activity recognition using wireless communication systems is a key function of future internet of things systems. The effective and inexpensive WiFi sensing system can help people with device-free controlling, and healthcare monitoring without concern of image information leakage that uses a camera system. In this article, we proposed a continuous angle of arrival and time of flight (AoA-ToF) maps based method that adopts multiple signals classification analysis on commercial and off-the-shelf WiFi devices to detect human activities. Our experimental results ensure the effectiveness of the proposed system for the human activity recognition (HAR) task with 8 activities among 5 users in three directions. The performance of our system achieves 85.6% accuracy on average. Meanwhile, we evaluate the performance of our system under different conditions, including direction and user identity. The results show the system’s robustness for human activity recognition under such conditions.

In wireless communications, various study findings have shown that a reconfigurable intelligent surface (RIS) may successfully alter wireless wave parameters like phase and amplitude without requiring sophisticated signal processing and decoding at the receiver. However, it is necessary to take into account designing the surface under a realistic frequency selective fading channel. Because of this, we chose a wideband OFDM multi-user communication system based on an actual RIS setup that considers mutual coupling (MC) and electromagnetic interference (EMI). We used Hadamard matrix in the pilot transmissions to estimate the uncontrollable and the controllable channels. The best pilot configuration was selected to initialize the gradient descent method in order to calculate the optimal reflection coefficient that maximize the data rate for each user in the presence of EMI and MC. Simulation results revealed that the data rate has been degraded when considering EMI and MC for around 30 Mbits/s for each user. This confirms that both EMI and MC must be given considerable attention in our research due to their inevitable effects on the system performance.

The spatial distribution of cellular networks has made them very promising to use for localization. By knowing the location of a user, cellular networks can provide context-aware services customized to that user. Objects and the dynamic nature of indoor locations result in lots of multipath and non-line-of-sight (NLOS) propagations. In this work, we carry out a novel experimental investigation to improve indoor localization using a grid approach with channel state information (CSI) fingerprinting and artificial intelligence (AI)/ machine learning (ML) methods for determining the location of a mobile device. Experiments are conducted in a standard indoor setting. This paper compares a method for indoor positioning based on received signal strength identifier (RSSI), phase, and CSI using ML to show how the accuracy of indoor localization can be improved. Compared to heuristic approaches like DOA estimation, the precision of ML is superior.

Intelligent transportation systems require a reliable exchange of information between network terminals in different vehicular communication environments. Making effective use of the dedicated spectrum is crucial to maximizing communication performance. This requires optimising the modulation order according to different channel conditions. This paper proposes a lightweight spectrum awareness methodology that uses wideband spectrum monitoring and deep learning-based modulation classification techniques to optimise the modulation order. We introduce a channel quality indicator block in which the classifier’s accuracy of detection is used as a forward indicator for the choice of the best modulation type for transmission. By using a 3D stochastic vehicular channel, we evaluate the classification performance at different channel parameter settings, including, speed, variance, and signal-to-noise ratio in urban and rural areas. The experimental analyses demonstrate the capability of the proposed approach to supporting a high detection probability for acceptable false decision-making ≤ 20%.

Abstract Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS‐assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS‐assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi‐users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed.

The increasing demand for considering multisensor data fusion technology has drawn attention for precise human activity recognition (HAR) over standalone technology due to its reliability and robustness. This article presents a framework that fuses data from multiple sensing systems and applies neuromorphic computing to sense and classify human activities. The data is collected by utilizing inertial measurement unit (IMU) sensors, software-defined radios, and radars, and feature extraction and selection are performed on the data. For each of the actions, such as sitting and standing, an activity matrix is generated, which is then fed into a discrete Hopfield neural network as a binary feature pattern for one-shot learning. Following the Hopfield network neurons’ feedback output, the conformity to the standard activity feature pattern is also determined. Following the Hopfield network neurons’ feedback output, the training of neurons is completed after two steps under the Hebbian learning law, and the conformity to the standard activity feature pattern is also determined. According to the probabilistic statistics on inference predictions, the proposed method, that is the neuromorphic computing of the three data fused framework, achieved the box plot for the highest lower quartile output of 95.34%, while the confusion matrix classification accuracy of the two activities was 98.98%. The results have shown that neuromorphic computing is most capable of multisensor data-fusion-based HAR. Furthermore, the proposed method can be enhanced by incorporating additional hardware signal processing in the system to enable the flexible integration of human activity data.

Intelligent transportation systems are an emerging technology that facilitates real-time vehicle-to-everything communication. Hence, securing and authenticating data packets for intra- and inter-vehicle communication are fundamental security services in vehicular ad-hoc networks (VANETs). However, public-key cryptography (PKC) is commonly used in signature-based authentication, which consumes significant computation resources and communication bandwidth for signatures generation and verification, and key distribution. Therefore, physical layer-based secret key extraction has emerged as an effective candidate for key agreement, exploiting the randomness and reciprocity features of wireless channels. However, the imperfect channel reciprocity generates discrepancies in the extracted key, and existing reconciliation algorithms suffer from significant communication costs and security issues. In this paper, PKC-based authentication is used for initial legitimacy detection and exchanging authenticated probing packets. Accordingly, we propose a blockchain-based reconciliation technique that allows the trusted third party (TTP) to publish the correction sequence of the mismatched bits through a transaction using a smart contract. The smart contract functions enable the TTP to map the transaction address to vehicle-related information and allow vehicles to obtain the transaction contents securely. The obtained shared key is then used for symmetric key cryptography (SKC)-based authentication for subsequent transmissions, saving significant computation and communication costs. The correctness and security robustness of the scheme are proved using Burrows–Abadi–Needham (BAN)-logic and Automated Validation of Internet Security Protocols and Applications (AVISPA) simulator. We also discussed the scheme’s resistance to typical attacks. The scheme’s performance in terms of packet delay and loss ratio is evaluated using the network simulator (OMNeT++). Finally, the computation analysis shows that the scheme saves ∼99% of the time required to verify 1000 messages compared to existing PKC-based schemes.

Sign language is utilized by deaf-mute to communicate through hand movements, body postures, and facial emotions. The motions in sign language comprise a range of distinct hand and finger articulations that are occasionally synchronized with the head, face, and body. Automatic sign language recognition (SLR) is a highly challenging area and still remains in its infancy compared with speech recognition after almost three decades of research. Current wearable and vision-based systems for SLR are intrusive and suffer from the limitations of ambient lighting and privacy concerns. To the best of our knowledge, our work proposes the first contactless British sign language (BSL) recognition system using radar and deep learning (DL) algorithms. Our proposed system extracts the 2-D spatiotemporal features from the radar data and applies the state-of-the-art DL models to classify spatiotemporal features from BSL signs to different verbs and emotions, such as Help, Drink, Eat, Happy, Hate, and Sad. We collected and annotated a large-scale benchmark BSL dataset covering 15 different types of BSL signs. Our proposed system demonstrates highest classification performance with a multiclass accuracy of up to 90.07% at a distance of 141 cm from the subject using the VGGNet model.

Industry 4.0 and 5.0 are paradigms of digitalization and intelligentization. The huge available bandwidth and the least spectral interference in the millimeter-wave (mmWave) band can pave the way for a wide range of new industrial automation capabilities. Sophisticated industrial applications such as industrial Internet of Things, time-sensitive networking (TSN), intelligent logistics, product tracking, remote visual monitoring and surveillance, image-guided automated assembly and automatic fault detection require high bandwidth, reliability and low latency which can be ensured using the mmWave band. In this paper, we address the physical layer (PHY) requirements of industrial communication from the viewpoint of Industry 4.0 and beyond, while highlighting the key performance indicators. This paper proposes a 60 GHz mmWave antenna system with high reliability and low latency, making it ideally suited for industrial IoT and communication. The proposed novel antenna system is designed to cover the entire 9 GHz bandwidth of the 60 GHz standard spectrum (57 to 66 GHz) with a single element peak gain of 8.2 dBi. It provides high gain and efficiency across all four channels of 60 GHz communication from 57.24 GHz to 65.88 GHz, each channel with 2.16 GHz of bandwidth. Moreover, the simulated achieved beamforming gain of the proposed antenna system reaches 16.1 dBi, which satisfies the high gain requirement for 60 GHz multi-gigabit industrial communication. The proposed antenna system is a promising physical layer candidate suitable for communication standards such as WiGig IEEE802.11ay, IEEE802.11ad, IEEE802.15.3c, ECMA-387 and WirelessHD to ensure multi-gigabit wireless communication at 60 GHz ISM band for factory automation and industrial applications.

In recent years, research has focused on exploiting the inherent physical (PHY) characteristics of wireless channels to discriminate between different spatially separated network terminals, mitigating the significant costs of signature-based techniques. In this paper, the legitimacy of the corresponding terminal is firstly verified at the protocol stack’s upper layers, and then the re-authentication process is performed at the PHY-layer. In the latter, a unique PHY-layer signature is created for each transmission based on the spatially and temporally correlated channel attributes within the coherence time interval. As part of the verification process, the PHY-layer signature can be used as a message authentication code to prove the packet’s authenticity. Extensive simulation has shown the capability of the proposed scheme to support high detection probability at small signal-to-noise ratios. In addition, security evaluation is conducted against passive and active attacks. Computation and communication comparisons are performed to demonstrate that the proposed scheme provides superior performance compared to conventional cryptographic approaches.

In a LoRaWAN network one of the main reasons of packet outage is the destructive interference that is caused by colliding packets. As the network operates with an ALOHA-like channel access setup, there is no easy way of preventing two or more devices transmitting at the same time, possibly generating interference to each other. Different methods are proposed in literature that can be used to decrease this chance. However, most of them require extensive use of downlink messages coupled with involved algorithms at the network side, often for only a marginal improvement in performance. In this paper we analyse some ways to optimise the Packet Delivery Ratio (PDR) of a LoRaWAN network that can be used when setting up a node or a group of nodes, do not involve downlink and can operate without knowledge of other devices in the same network. These are shown to provide a small boost in performance of maximum 10%, which is akin to that of more complex, downlink-dependant schemes, while decreasing the set up complexity considerably.

Securing data exchange between intercommunicating terminals, e.g., vehicle-to-everything, constitutes a technological challenge that needs to be addressed. Security solutions must be computationally efficient and flexible enough to be implemented in any wireless propagation environment. Recently, physical layer security has gained popularity, which exploits the randomness of wireless channel responses for extracting high entropy secret cryptographic keys. The current state-of-the-art relies on the independently varying channel sources of randomness, e.g., received signal strength (RSS) and phase. However, the limited capability of RSS-based extraction techniques has motivated researchers to investigate alternative approaches. Although phase-based approaches have emerged in many studies, optimising the extraction performance by adapting the algorithm to the non-reciprocal components of static and dynamic channels remains a challenge. In this paper, we propose an adaptive multilevel quantisation approach that adjusts the size of the quantisation region to the channel responses’ non-reciprocity parameters, thus optimising the trade-off between the bit generation rate (BGR) and the bit mismatch rate (BMR). The probability of error has been theoretically formulated. Accordingly, the order of the quantisation process is adapted for acceptable mismatching probability. Moreover, simulation analysis is conducted to prove the ability of the proposed approach to provide flexible adaptation of the quantisation order at different signal-to-noise ratios (SNRs), achieving fast secret bit generation rates 1. 1$\sim$2.85bits/packet at SNRs of 10$\sim$25 dB for acceptable BMR $\leq 0.1$.

Abstract In less than three years, more than six million fatalities have been reported worldwide due to the coronavirus pandemic. COVID-19 has been contained within a broad range due to restrictions and effective vaccinations. However, there is a greater risk of pandemics in the future, which can cause similar circumstances as the coronavirus. One of the most serious symptoms of coronavirus is rapid respiration decline that can lead to mortality in a short period. This situation, along with other respiratory conditions such as asthma and pneumonia, can be fatal. Such a condition requires a reliable, intelligent, and secure system that is not only contactless but also lightweight to be executed in real-time. Wireless sensing technology is the ultimate solution for modern healthcare systems as it eliminates close interactions with infected individuals. In this paper, a lightweight real-time solution for anomalous respiration identification is provided using the radio-frequency sensing device USRP and the ensemble learning approach extra-trees. A wireless software-defined radio platform is used to acquire human respiration data based on the change in the channel state information. To improve the performance of the trained models, the respiration data is utilised to produce large simulated data sets using the curve fitting technique. The final data set consists of eight distinct types of respiration: eupnea, bradypnea, tachypnea, sighing, biot, Cheyne-stokes, Kussmaul, and central sleep apnea. The ensemble learning approach: extra-trees are trained, validated, and tested. The results showed that the proposed platform is lightweight and highly accurate in identifying several respirations in a static setting.

The Internet of Things (IoT) devices, networks, and applications have become an integral part of modern societies. Despite their social, economic, and industrial benefits, these devices and networks are frequently targeted by cybercriminals. Hence, IoT applications and networks demand lightweight, fast, and flexible security solutions to overcome these challenges. In this regard, artificial-intelligence-based solutions with Big Data analytics can produce promising results in the field of cybersecurity. This article proposes a lightweight dense random neural network (DnRaNN) for intrusion detection in the IoT. The proposed scheme is well suited for implementation in resource-constrained IoT networks due to its inherent improved generalization capabilities and distributed nature. The suggested model was evaluated by conducting extensive experiments on a new generation IoT security dataset ToN_IoT. All the experiments were conducted under different hyperparameters and the efficiency of the proposed DnRaNN was evaluated through multiple performance metrics. The findings of the proposed study provide recommendations and insights in binary class and multiclass scenarios. The proposed DnRaNN model attained attack detection accuracy of 99.14% and 99.05% for binary class and multiclass classifications, respectively.

Abstract In vehicular ad hoc networks (VANETs), transmission power is a key factor in several performance measures, such as throughput, delay, and energy efficiency. Vehicle mobility in VANETs creates a highly dynamic topology that leads to a nontrivial task of maintaining connectivity due to rapid topology changes. Therefore, using fixed transmission power adversely affects VANET connectivity and leads to network performance degradation. New cross‐layer power control algorithms called (BL‐TPC 802.11MAC and DTPC 802.11 MAC) are designed, modeled, and evaluated in this paper. The designed algorithms can be deployed in smart cities, highway, and urban city roads. The designed algorithms improve VANET performance by adapting transmission power dynamically to improve network connectivity. The power adaptation is based on inspecting some network parameters, such as node density, network load, and media access control (MAC) queue state, and then deciding on the required power level. Obtained results indicate that the designed power control algorithm outperforms the traditional 802.11p MAC considering the number of received safety messages, network connectivity, network throughput, and the number of dropped safety messages. Consequently, improving network performance means enhancing the safety of vehicle drivers in smart cities, highway, and urban city.

The convergence of artificial intelligence (AI) and the Internet of Things (IoT) promotes energy-efficient communication in smart homes. Quality-of-Service (QoS) optimization during video streaming through wireless micro medical devices (WMMDs) in smart healthcare homes is the main purpose of this research. This article contributes in four distinct ways. First, to propose a novel lazy video transmission algorithm (LVTA). Second, a novel video transmission rate control algorithm (VTRCA) is proposed. Third, a novel cloud-based video transmission framework is developed. Fourth, the relationship between buffer size and performance indicators, i.e., peak-to-mean ratio (PMR), energy (i.e., encoding and transmission), and standard deviation, is investigated while comparing LVTA, VTRCA, and baseline approaches. The experimental results demonstrate that the reduction in encoding (32% and 35.4%) and transmission (37% and 39%) energy drains, PMR (5 and 4), and standard deviation (3 and 4 dB) for VTRCA and LVTA, respectively, is greater than that obtained by baseline during video streaming through WMMD.

In this paper, we analyze various kinds of hand tremors in the time and frequency domain, that are induced by performing a set of hand actions. We collected the tremor data using a simple, wearable accelerometer from 15 healthy individuals that had varying levels of athleticism. The overall results presented here show that the physiologic tremors in range of 8-14 Hz are most noticeable under fatigue.

The radio frequency identification (RFID) has emerged Internet of Things (IoT) into the identification of things. This paper presents, a low-cost smart refrigerator system for future IoT applications. The proposed smart refrigerator is used for automatic billing and restoring of beverage metallic cans. The metallic cans can be restored by generating a product shortage alert message to a nearby retailer. To design a low-cost and low-profile tag antenna for metallic items is very challenging, especially when mass production is required for item-level tagging. Therefore, a novel ultrahigh frequency (UHF) RFID tag antenna is designed for metallic cans by exploiting the metallic structure as the main radiator. Applying characteristics mode analysis, we observed that some characteristic modes associated with the metallic structure could be exploited to radiate more effectively by placing a suitable inductive load. Moreover, a low cost, printed (using conductive ink) small loop integrated with meandered dipole used as an inductive load, which was also connected with RFID chip. The 3-dB bandwidth of the proposed tag covers the whole UHF band ranging from 860 to 960 MHz when embedded with metal cans. The measured read range of the RFID tag is more than 2.5 m in all directions to check the robustness of the proposed solution. To prove the concept, a case study was performed by placing the tagged metallic cans inside a refrigerator for automatic billing, 97.5% tags are read and billed successfully. This paper paves the way for tagging metallic bodies for tracking applications in domains ranging from consumer devices to infotainment solutions, which enlightens a vital aspect for the IoT.

This paper demonstrates a low profile, compact and conformal antenna design for 5thgeneration (5G) wireless devices. The antenna design incorporates two bandwidth-enhancement techniques of fractal geometry and defected ground structure (DGS). The Koch fractal radiating patch is developed with a DGS coupled coplanar waveguide (CPW) feeding. Highly precise fabrication is performed on a thin Polyethylene Terephthalate (PET) film using the additive manufacturing process of conductive ink-based inkjet printing. The results show that the antenna offers a bandwidth which covers Ka-band (26.5-40 GHz) and a peak realised gain of 9.7 dBi at 39.7 GHz. The integrated aperture in the ground improves the directivity by converging the radiation in the axis orthogonal to the antenna's plane. The proposed flexible antenna is well suited for integration in millimetre-wave (MMW) based wearable and conformal wireless devices.

As the communication industry is progressing towards fifth generation (5G) of cellular networks, the traffic it carries is also shifting from high data rate traffic from cellular users to a mixture of high data rate and low data rate traffic from Internet of Things (IoT) applications. Moreover, the need to efficiently access Internet data is also increasing across 5G networks. Caching contents at the network edge is considered as a promising approach to reduce the delivery time. In this paper, we propose a marketplace for providing a number of caching options for a broad range of applications. In addition, we propose a security scheme to secure the caching contents with a simultaneous potential of reducing the duplicate contents from the caching server by dividing a file into smaller chunks. We model different caching scenarios in NS-3 and present the performance evaluation of our proposal in terms of latency and throughput gains for various chunk sizes.

This paper studies a system where a set of $N$ relay nodes harvest energy from the signal received from a source to later utilize it when forwarding the source's data to a destination node via distributed beamforming. To this end, we derive (approximate) analytical expressions for the mean SNR at destination node when relays employ: i) time-switching based energy harvesting policy, ii) power- splitting based energy harvesting policy. The obtained results facilitate the study of the interplay between the energy harvesting parameters and the synchronization error, and their combined impact on mean SNR. Simulation results indicate that i) the derived approximate expressions are very accurate even for small $N$ (e.g., $N=15$), ii) time-switching policy by the relays outperforms power-splitting policy by at least $3$ dB.

In this preliminary work, we study the problem of distributed authentication in wireless networks. Specifically, we consider a system where multiple Bob (sensor) nodes listen to a channel and report their correlated measurements to a Fusion Center (FC) which makes the ultimate authentication decision. For the feature-based authentication at the FC, channel impulse response has been utilized as the device fingerprint. Additionally, the correlated measurements by the Bob nodes allow us to invoke Compressed sensing to significantly reduce the reporting overhead to the FC. Numerical results show that: i) the detection performance of the FC is superior to that of a single Bob-node, ii) compressed sensing leads to at least 20% overhead reduction on the reporting channel at the expense of a small (<;1 dB) SNR margin to achieve the same detection performance.

This paper proposes to exploit the so-called {\it reciprocity parameters} (modelling non-reciprocal communication hardware) to use them as decision metric for binary hypothesis testing based authentication framework at a receiver node Bob. Specifically, Bob first learns the reciprocity parameters of the legitimate sender Alice via initial training. Then, during the test phase, Bob first obtains a measurement of reciprocity parameters of channel occupier (Alice, or, the intruder Eve). Then, with ground truth and current measurement both in hand, Bob carries out the hypothesis testing to automatically accept (reject) the packets sent by Alice (Eve). For the proposed scheme, we provide its success rate (the detection probability of Eve), and its performance comparison with other schemes.

In vivo wireless body area networks and their associated technologies are shaping the future of healthcare by providing continuous health monitoring and noninvasive surgical capabilities, in addition to remote diagnostic and treatment of diseases. To fully exploit the potential of such devices, it is necessary to characterize the communication channel, which will help to build reliable and high-performance communication systems. This paper presents an in vivo wireless communication channel characterization for male torso both numerically and experimentally (on a human cadaver) considering various organs at 915 MHz and 2.4 GHz. A statistical path loss (PL) model is introduced, and the anatomical region-specific parameters are provided. It is found that the mean PL in decibel scale exhibits a linear decaying characteristic rather than an exponential decaying profile inside the body, and the power decay rate is approximately twice at 2.4 GHz as compared to 915 MHz. Moreover, the variance of shadowing increases significantly as the in vivo antenna is placed deeper inside the body since the main scatterers are present in the vicinity of the antenna. Multipath propagation characteristics are also investigated to facilitate proper waveform designs in the future wireless healthcare systems, and a root-mean-square delay spread of 2.76 ns is observed at 5 cm depth. Results show that the in vivo channel exhibit different characteristics than the classical communication channels, and location dependence is very critical for accurate, reliable, and energy-efficient link budget calculations.

In this paper, the channel model is proposed to compute the path loss, delay and noise at THz band of interest (0.5 THz ~ 1.5 THz). The results shows that THz band channel is strongly dependent on both the type of the medium and the distance while the concentration of water have a lot of influence because it not only causes attenuation to the THz wave but also introduces non-white noise. Therefore, the proposed model paves the way to the further studies considering more body structures and tissue properties.

In vivo wireless medical devices have a critical role in healthcare technologies due to their continuous health monitoring and noninvasive surgery capabilities. In order to fully exploit the potential of such devices, it is necessary to characterize the in vivo wireless communication channel which will help to build reliable and high-performance communication systems. This paper presents preliminary results of experimental characterization for this fascinating communications medium on a human cadaver and compares the results with numerical studies.

The paper presents an initial study of analyzing the propagation of electromagnetic waves at terahertz frequencies inside the human skin tissues in the frequency range 0.8-1.2 THz. The skin model is represented by three layers: stratum corneum (SC), epidermis, and dermis, and the effect of the presence of sweat ducts is also studied. The path loss resulting from the signal propagation between a transmitting antenna, located at the epidermis layer, and a receiving antenna, located at the dermis layer, is analyzed for different distances and different frequencies, in addition to changing the number of sweat ducts. Results show that the path loss through the skin tissues depends on the distance between the transmitter and the receiver, the frequency used, and the number of sweat ducts as the path loss decreases by approximately 1 dB for increasing the number of sweat duct from one duct to two ducts. Moreover, the path loss is statistically studied and its distribution is approximately Gaussian.

A novel frequency reconfigurable microstrip patch antenna has been presented for 3.6 GHz and 5 GHz. Compared to the traditional, complicated and high cost frequency reconfigurable antennas, our work is featured by a simple and concise design. The frequency reconfiguration is obtained by using layers of mercury and liquid crystal polymer (LCP) on conventional patch antenna. The proposed structure was modelled and simulated using CST Microwave Studio. The antenna was first simulated in free space to check the antenna parameters such as return loss, gain, radiation pattern and efficiency. After obtaining the results, the antenna was simulated for analysing the on-body performance by using numerical model of human body. The simulated return loss for both the configurations is less than -10 dB at the radiating frequencies. The free space simulated results show the close agreement with the on-body test results.

Body-centric wireless networks are used for connectivity between on-body and on-off body communications for various applications for rescue, diagnostics and medical usage. Multiple features of modern portable and wearable devices necessitate antenna operation at a number of frequencies. A compact, low profile and multi-band antenna is presented for body-centric wireless networks in this study. The conventional microstrip rectangular patch antenna has been converted into a multi-band antenna by using layers of mercury and liquid crystal polymer (LCP). The antenna performance in free space and in body-mounted configurations are evaluated and compared using computer simulations. The proposed antenna supports six frequencies for operation at ISM/Wi-Fi/C band. A minimal shift in the operating frequencies while operating in on-body configuration makes this the proposed antenna very resilient to frequency de-tuning caused by the human body presence. The antenna also offers high peak gain values (>7.68 dBi) in the two configurations at all of the operating frequencies.

This paper investigates energy efficient in vivo communication with multi-source nodes. In such a network, the in vivo sensor nodes transmit their sensing information to an on-body destination node. Due to the associated high path loss with implant devices, on-body relay nodes can be used to convey the information bits from the in vivo source nodes to the on-body destination node in an energy efficient manner. In this context, the paper objective is to select the optimal on-body relay locations that result in the minimum per bit average energy consumption for the in vivo networks using the minimum number of on-body relay nodes. The problem is formulated as an integer program that can be efficiently solved using commercial optimization solvers. Numerical results demonstrate the significant improvement in energy consumption and quality-of-service (QoS) support, when on-body relays are optimally located.

Parametric probability models are common references for channel characterization. However, the limited number of samples and uncertainty of the propagation scenario affect the characterization accuracy of parametric models for body area networks. In this paper, we propose a sparse nonparametric probability model for body area wireless channel characterization. The path loss and root-mean-square delay, which are significant wireless channel parameters, can be learned from this nonparametric model. A comparison with available parametric models shows that the proposed model is very feasible for the body area propagation environment and can be seen as a significant supplement to parametric approaches.

Enquiry-based student-centered learning activities in engineering classrooms may lose its focus and success if the activities in classroom are biased due to the failure in forming mixed-ability oriented teams or groups of students. This paper proposes a technology driven team-building methodology to enhance enquiry-based learning in the conventional engineering classrooms, which is referred to as Heterogeneous Ability-Centered Team Building (H-ACT-B) method. The H-ACT-B method guarantees mixed-ability based team or group formation in classroom to promote effective communication, collaboration and critical thinking based on the programmed evaluation of individual student's aptitude. The proposed team building method is expected to aid the individual student and the team or group to ensure their progress toward achieving the common team task efficiently. Moreover, the study is strongly formulated by considering the useful insights about the current team building practices and methods by conducting survey among the engineering faculty and students.

The paper presents the improvement offered by spatial diversity to reduce fading in ultra wideband (UWB) body-centric wireless communication channel in context of second order statistics for five different measured links, i.e., waist-to-chest, waist-to-head, waist-to-wrist, waist-to-ankle and waist-to-back. Average fade duration (AFD) and level crossing rate (LCR) values are presented and compared for diversity combined signal with respect to branch signals. In addition AFD and LCR of diversity combined signals are also compared for five different on-body channels and for three different locations in an indoor environment. Result and analysis of second order channel parameters shows the importance of considering these parameters when designing an enhanced body-area network system.

The paper presents a subject-specific radio propagation study and system modeling in wireless body area networks using a simulation tool based on the parallel finite-difference time-domain technique. This technique is well suited to model the radio propagation around complex, inhomogeneous objects such as the human body. The impact of different digital phantoms in on-body radio channel and system performance was studied. Simulations were performed at the frequency of 3-10 GHz considering a typical hospital environment, and were validated by on-site measurements with reasonably good agreement. The analysis demonstrated that the characteristics of the on-body radio channel and system performance are subject-specific and are associated with human genders, height, and body mass index. Maximum variations of almost 18.51% are observed in path loss exponent due to change of subject, which gives variations of above 50% in system bit error rate performance. Therefore, careful consideration of subject-specific parameters are necessary for achieving energy efficient and reliable radio links and system performance for body-centric wireless network.

The paper aims to investigate the optimum locations for ultra wideband (UWB) nodes placement on the body. The performance of UWB radio propagation links is experimentally investigated by considering communications between body-worn base units and off-body access points. Radio channel parameters are derived and analysed for different sectors of the human body. System-level modelling, based on signal to noise ratio (SNR) and bit error rate (BER) is analytically performed on the basis of multi band orthogonal frequency division multiplexed (OFDM) system.