International Journal of Communication Systems最新文献

This paper describes the design and implementation of a dual-layer frequency selective surface (FSS) based dual-band high-gain circularly polarized (CP) meander-shaped monopole antenna for the applications of Wi-Fi and C-band. To achieve the final configuration, the design process involves several steps: designing the meander-shaped antenna, modeling the FSS, circuit analysis, and practical realization. The antenna prototype (electrical dimension: 0.333 λ₀ × 0.266 λ₀, λ₀ at 2 GHz and physical dimension of 50 × 40 mm²) comprises a meander-shaped strip and a 3 × 3 matrix dual layer periodic FSS embedded under the antenna structure without disturbing the impedance bandwidth. The low-cost FR4 dielectric substrate is used to design both the antenna and FSS layer. Initially, only the antenna part was designed, which yielded −10 dB impedance bandwidths (IBWs) and 3-dB axial ratio bandwidths (ARBWs) of 1160 and 600 MHz in the lower band, and 560 and 700 MHz in the upper band, respectively. Without an FSS structure, the peak gains of the antenna are 3.6 dBi (lower band) and 3.8 dBi (upper band). The proposed FSS-based antenna is fabricated and measured in the microwave test bench. The measured results show 3-dB ARBWs of 350 MHz (2.35–2.7 GHz) and 600 MHz (3.9–4.5 GHz) with LHCP waves. The measured peak gains are 8 dBi in the lower band and 8.5 dBi in the upper band. With small tolerance, the measured results agree with simulations.

Sensor localization is a crucial factor in ensuring reliable operation in wireless sensor network (WSN). The existing research on localization focuses more under isotropic conditions, assuming homogeneous environments with uniformly distributed nodes. In contrast, real-world deployments frequently exhibit Anisotropic WSN (AWSN) characteristics, including irregular topologies, obstacles, and non-uniform connectivity, which significantly challenge the localization process. Among many localization methods, the DV-Hop algorithm is widely used approach for its cost-effectiveness and easy implementations. However, the DV-Hop algorithm performs poorly when there is an obstacle encountered; the average hop distance might deteriorate, and it is hard to locate the coordinates of the location of unknown nodes. In order to achieve better localization accuracy with cost-effective, this study proposes Domination-based Anchor Placement for AWSNs (DAPA) to obtain minimum anchor requirement and optimal anchor placement. Further, a Q-learning-based Crayfish Optimization Algorithm (QCOA) is proposed to enhance the DV-Hop algorithm localization accuracy. The proposed QCOA was compared using 10 benchmark functions with some similar existing optimization algorithms. The proposed DAPA approach was compared with random anchor deployment method using three variants of DV-Hop algorithms. DAPA outperform the random anchor deployment in each three comparisons. Further, the proposed DAPA-based QCOA algorithm simulated in C-, S-, and O-shaped topologies based on localization error from varying the AWSN metrics. The proposed algorithm achieves high localization accuracy among the existing algorithms.

This research presents a wearable radiator with dual-band and quad-terminal that is compact and made on polyimide substrate. Dual metallic rings integrated with the feed line create the band notch in between 2.66 and 3.28 GHz and convert the wideband into dual band feature. By taking use of spatial and polarization diversity, the separation level among various terminals is more than 25 dB. A single-negative (SNG) metamaterial-based metasurface (MS) reflector-cum-absorber surface located just below the radiator reduces the specific absorption rate (SAR) for the 1- and 10-g tissue models by more than 90%, while also increasing the radiator gain to 5.65 and 4.55 dBi. ANSYS HFSS 2023 R2 was used for full-wave simulations, and a Keysight E5071C VNA in both flat and bending configurations was used for experimental validation. Using a three-layer human tissue phantom (skin, fat, and muscle) with 1- and 10-g averages, SAR assessment was conducted in accordance with IEEE/IEC 62209-1 and ICNIRP recommendations. Its performance in two frequency bands, 2.3–2.65 and 3.3–3.65 GHz, is confirmed by measurement findings. All these features make the radiator applicable for WBAN application.

In wireless sensor networks (WSNs) used for continuous surveillance, the problem of monitoring critical data transmitted infrequently is an extreme challenge of energy usage and latency requirements. Current medium access control (MAC) protocols often have high energy consumption, primarily owing to idle listening, collision, and excessive data transmission, and as a result, are not suitable for such uses. This study proposes a novel protocol to optimize energy consumption and transmission delays in WSNs used to monitor infrequent critical data. This protocol is named OWuR-MAC, that is, “Optimized Wake-up Radio based Medium Access Control.” OWuR-MAC implements an event-driven wake-up strategy utilizing wake-up receivers so that devices can stay in the low-power sleep mode until data transmission is necessary. Sensor nodes use wake-up receivers, which allow them to remain at low-energy sleep times until there is relevant data transmission that can wake them up. However, OWuR-MAC dynamically modifies the wake-up receiver sensitivity and transmission timing based on the characteristics of networked activity and environmental conditions. The protocol was implemented and compared with Fully Asynchronous Wake-up Radio MAC (FAWR-MAC) and Opportunistic Wake-up Radio MAC (OPWUM) protocols of a similar category. The results indicate that OWuR-MAC achieves lower rates of energy consumption, lower latency, and higher packet delivery ratios than the other two protocols.

Vehicular Ad Hoc Networks, in short VANETs, a category of mobile ad hoc networks, facilitate Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication for improved traffic management, safety, and autonomous driving. This work proposes a traffic flow prediction model that integrates metaheuristic optimization techniques such as Sand Cat Swarm Optimization (SCSO) and Raven Roosting Optimization (RRO) with the deep learning model Bi-directional Long Short Term Memory with Stacked Autoencoder (Bi-LSTM-SA) to improve prediction accuracy. To optimize the Bi-LSTM-SA network, a hybrid SCSO-RRO approach encodes neuron parameters like weights, biases, and activation functions. The SCSO explores the search space while RRO refines solutions by having ravens follow high-fitness sand cats. A fitness function evaluates performance, and the process iterates until convergence, after which the optimized network is validated on a separate dataset. The proposed model Combined SCSO-RRO-Bi-LSTM-SA (C-SC-RRO-Bi-LSTM-SA) is compared with existing algorithms such as Random Forest (RF), Artificial Neural Network (ANN), Bi-LSTM-SA, and evaluation parameters utilized to quantify the network's performance include accuracy, prediction, recall, F1-score, and cross-entropy loss.

Autonomous vehicles (AVs) primarily depend on GPS for their location and navigation. However, GPS spoofing, where fake signals fool the receiver, poses a severe threat with incorrect localization, unsafe maneuvers, and navigation failures. This paper proposes a new approach: enhancing autonomous vehicle navigation in GPS-spoofed environments using quantum self-attention neural networks for robust positioning and path planning (EAVN-GPSSE-QSANN-RPPP). The proposed method uses a GPS spoofing dataset. It introduces the usage of a regularized bias-aware ensemble Kalman filter (RBEKF) for noise reduction and bias correction, a lotus effect optimizer (LEO) for selecting discriminative features, and a quantum self-attention neural network (QSANN) optimized with the Parrot Optimizer Algorithm for an accurate spoofing detection and classification task. The proposed EAVN-GPSSE-QSANN-RPPP approach attains 7.14%, 6.02%, and 8.27% higher accuracy and 7.36%, 5.48%, and 8.27% higher precision compared with existing techniques, respectively. This work confirms that the proposed architecture should be able to guarantee robust localization, improved path planning, and resilience against GPS spoofing, enhancing safety and reliability for the operation of AV navigation in adversarial environments.

Wireless Sensor Networks (WSNs) are a vital component in the Internet of Things (IoT) infrastructure for the purpose of real-time data gathering from varied and dispersed sensing areas. Security, energy efficiency, and maintenance of Quality of Service (QoS) in the event of resource scarcity are, however, a vital challenge. The conventional routing structures do not possess adaptive intelligence and lightweight cryptography capabilities to adapt to dynamic, high-density IoT scenarios. To overcome this, the current work proposes a novel architecture called Efficient QoS-Aware and Secure Routing in WSN with IoT Devices Using Snow Geese Optimized Gates-controlled Deep Unfolding Single-Head Vision Transformer Network (SGO-GcDUN-SiHViT). The architecture starts with node deployment using a Bi-Concentric Hexagonal (Bi-Hex) model and utilizing Honey Badger–Horse Herd Optimization Algorithm (HB-HHOA) for energy-efficient clustering. Sensor information is fused by the Fuzzy Min-Max Network (FM-MN) and encrypted using Lightweight Attribute-based Encryption (LAE). For improved route discovery, a deep learning model based on Gates-controlled Deep Unfolding Network (GcDUN) and Single-Head Vision Transformer (SiHViT) is optimized by Snow Geese Optimization (SGO). Finally, the Private Blockchain Voting Mechanism (PBVM) is employed for secure IoT user authentication. The experimental results show that the proposed model yields a high hash rate of 932 ops/s, low encryption time of 1.3 s, security strength of 99.3%, and routing overhead as low as 11.2%. Moreover, improvements in all aforementioned variables were statistically confirmed using ANOVA, showing p = 0.0001, and Cohen's d, which was 1.52, proving the superiority of the system in secure and efficient IoT-WSN communication.

Wireless sensor networks (WSNs) are important in real-time applications such as environmental monitoring, health, and automation in industries. Nevertheless, maintaining stable communication and energy efficiency during topology changes and node failures also comes as one of the major challenges. The majority of the currently existing frameworks, such as GSO, OEPO-FPA, and fuzzy-based clustering, specialize in either optimization of energy consumption or fault tolerance, yet many of them do not combine those two concepts effectively. Also, such approaches usually do not have adaptive intelligence to adapt to the evolving network conditions. In order to overcome these shortcomings, the present study is proposing a hybrid neuro-fuzzy optimization (NFO) framework, that is a synergistic combination of fuzzy inference to handle the uncertainty and multilayer perceptron (MLP) to learn fault patterns dynamically, and use particle swarm optimization (PSO) to optimize routing and duty cycles on a global scale. The implementation of the model took place with MATLAB R2023b and NS-3 and was tested on the WSN-DS dataset that includes the main network parameters of residual energy, PDR, and link quality. The proposed approach achieved 92.4% fault detection accuracy, 85% packet delivery ratio, 80% residual energy retention, and extended network lifetime up to 970 rounds, resulting in an improvement of over 15%–25% compared with existing methods. The inclusion of a dynamic feedback loop ensures continuous rule refinement and performance adaptation. This unified and lightweight solution offers a scalable, resilient, and intelligent architecture for self-healing WSNs, presenting a promising direction for future deployments in resource-constrained, mission-critical environments.

In the era of digital healthcare systems and cloud computing, securing the transmission and storage of sensitive medical data has become increasingly critical. Existing authentication protocols in cloud-based environments often suffer from significant limitations, such as high computational costs, vulnerability to insider or impersonation attacks, and insufficient guarantees of anonymity, traceability, and mutual authentication. In this work, we propose a lightweight and robust authentication scheme tailored for smart healthcare systems leveraging elliptic curve cryptography and secure hash functions. Our method ensures mutual authentication, anonymity, perfect forward secrecy, and strong resistance against various attack vectors including man-in-the-middle, replay, and insider attacks. Experimental evaluations demonstrate that our scheme outperforms existing approaches in terms of storage, communication, and computation efficiency, making it a promising solution for securing cloud-based healthcare infrastructures.

This article presents a novel compact printed monopole antenna with a gun-shaped design, developed for multiband operation targeting WLAN, WiMAX, and X-band applications. The antenna features a gun-shaped radiating patch integrated with two L-shaped structures and multiple stubs, which are optimized to support five distinct frequency bands. By introducing a rectangular cut into the patch and a rectangular slot, enhanced multiband performance with good impedance matching and radiation characteristics is achieved. The antenna is fabricated on a low-cost, low-profile FR4 substrate with a dielectric constant of 4.4, a loss tangent of 0.02, and a thickness of 0.8 mm. It has a compact overall size of 18 × 18 × 0.8 mm³ and operates across the following frequency bands: 2.3–2.84, 3.4–3.7, 4.8–5.2, 6.2–7.1, and 7.9–8.2 GHz, all with reflection coefficients (S₁₁) better than −10 dB. The measured peak gains at 2.4, 3.6, 5.0, 6.5, and 8.0 GHz are 2.4, 2.8, 2.9, 4.1, and 2.8 dBi, respectively. The antenna achieves impedance bandwidths of 22.5%, 8.33%, 8.00%, 13.84%, and 3.75% at the respective resonant frequencies of 2.4, 3.6, 5.0, 6.5, and 8.0 GHz. It also exhibits high radiation efficiency, exceeding 90% across all bands. A close agreement is observed between the simulated and measured results, confirming the antenna's suitability for multiband wireless communication systems.