ABSTRACT An inverted L‐shaped dual band monopole antenna with a parasitic octagonal split ring resonator (OSRR) is proposed for multiple modern wireless communication systems. The proposed antenna is designed and evaluated with the help of CST Microwave Studio. The antenna is fabricated on an FR4 substrate with an overall footprint of 19 mm × 19.5 mm × 1.6 mm. The proposed antenna has two operating bands, which extends from 2.70 to 4.18 GHz and 4.92 to 5.20 GHz, supporting various wireless applications such as wireless local area network (WLAN), wireless interoperability for microwave access (WiMAX), long‐term evolution (LTE), and sub‐6 GHz new radio (NR) bands (n48/n77/n78/n79). The metamaterial characteristics are analyzed and validated with the NRW method of parameter extraction. Further, the implemented design is analyzed with various dielectric materials, including ARLON AD 250C, ARLON AD 300D, FR‐4, ROGER RO 3003, ROGER RO 3006, and ROGER TMM 10 to show the impact of substrate material on the characteristics of the proposed antenna. The prototype of the proposed antenna is fabricated and experimentally tested for validating the simulated outcomes. The measured results demonstrate strong agreement with the simulated results. Due to its compactness, the proposed antenna proves itself as a potential choice for the next generation wireless communication systems.

ABSTRACT A growing number of healthcare sensors are attracting researchers' attention because of their irreplaceable role in numerous applications and new challenges. Flexible sensors transmit health information from the field to healthcare centers via the IoMT network, helping to supply timely assistance to patients. Ensuring secure data collection and transmission to centralized servers is quite difficult. To ensure the secure transmission and collection of medical data, various routing protocols have been proposed. However, these still have high overheads in storage, communication, and energy, which lead to security breaches. In order to deal with multiple attacks in the context of healthcare, security routing protocols must be developed that are efficient. This research proposes SOptC, a secure optimal cluster–based routing protocol designed for secure medical data transmission within IoMT‐WSN environments. In SOptC routing protocol, an efficient clustering is done by using chaotic mine blast optimization (CMBO) algorithm with the environmental information of sensor nodes. Then, we formalize the set of novel design constraints for cluster head (CH) computation, which can be optimized through planet optimization (CH‐PO) algorithm. In addition, we design an improved lightweight crypto algorithm, which combines two block ciphers (RING‐Simon tiered optimizations) for medical data encryption and decryption. The RING‐Simon framework aims to provide better security and reliability to data handled in this wireless sensor network (WSN). Furthermore, a modified group searching (MGS) algorithm is employed for selecting the optimal path between CHs and the sink, providing efficient multipath routing. With this effective approach, energy consumption is reduced by 68.9% and extends network life by 13.4%. It also boosts data throughput by over 27% compared with existing methods. Finally, simulation results demonstrate the effectiveness of our SOptC protocol in excess of the state‐of‐the‐art existing methodologies.

No abstract is available for this article.

ABSTRACT Underwater wireless sensor networks (UWSNs) have been used increasingly for critical tasks such as environment surveillance and underwater exploration. Nevertheless, their peculiar working environment, which entails their use of acoustic communication, high message latency, low bandwidth, mobility, stringent energy resources, and noisy communication channels, renders them susceptible to complex cyber threats including Sybil, Denial of Service (DoS), and traffic analysis attacks. This reality is a major drawback to the use of traditional intrusion detection systems used in other wireless communication networks. This manuscript addresses these issues through the presentation of an optimized cyberattack detection framework in UWSNs using an equivariant quantum convolutional neural network integrated with flamingo jellyfish search optimization (CD‐UWSN‐EQCNN‐FJSO). In this approach, network traffic data from the NSL‐KDD dataset are normalized using Bayesian boundary trend filtering (BBTF) to handle noise and uncertainty. Bitterling fish optimization (BFO) is then applied for feature selection, with further statistical feature extraction made by the double probability integral transform (DPIT). Here, the designed equivariant quantum convolutional neural network (EQCNN) is well leverage equivariance properties to perform robust detection under dynamic underwater network conditions, while the flamingo jellyfish search optimization (FJFO) approach dynamically fine‐tunes the weights within the network for improved detection accuracy and lower false alarm rates. The experimental results indicate that the proposed CD‐UWSNs‐EQCNN‐FJSO approach is able to provide much 27.15%, 26.09%, and 28.10% higher accuracy and 29.03%, 25.23%, and 29.1% higher recall capabilities, as well as much lower rates of 20.09%, 22.24%, and 20.01% lower false positive rate than other UWSN‐based cyber security solutions available in the existing methods.

ABSTRACT This work presents an integrated framework that combines polar coding, spatial modulation, and symbol‐level guessing random additive noise decoding (symbol‐level GRAND) aided detection to enhance the reliability and latency performance of CRC‐aided polar codes in 6G ultrareliable low‐latency communication (URLLC) scenarios. The proposed polar‐coded spatial modulation (PCSM) scheme utilizes (CA‐polar) codes to encode information bits transmitted as antenna index and symbol bits over a MIMO Rayleigh fading channel. The receiver employs a two‐stage detection scheme comprising initial spatial signal processing for antenna and symbol detection, followed by symbol refinement through symbol‐level GRAND with final error correction achieved with CRC‐aided successive cancellation list (CA‐SCL) decoding. A sequential detection and decoding strategy is adopted, in which symbol‐level GRAND refines the detected bits before CA‐SCL polar decoding, reducing error propagation into the polar decoder and thereby lowering the overall decoding complexity. Performance of the scheme is evaluated for polar codeword length of 128 bits for modulation orders of 16‐QAM and 64‐QAM, highlighting the scheme's parametric flexibility under short codeword constraints. Simulations carried out in MATLAB demonstrate that error correction with symbol‐level GRAND‐aided detection achieves an improved bit error rate (BER) and a lower system complexity facilitating real‐time hardware realization.

ABSTRACT The sixth‐generation (6G) wireless networks are envisioned to deliver unprecedented capabilities, including ultra‐high data rates, ultra‐low latency, and massive connectivity. Among the emerging technologies, the terahertz (THz) spectrum is a key enabler for achieving terabit‐per‐second transmission speeds. However, THz communication faces significant obstacles, such as severe path loss, frequent signal blockage, and high channel variability. To address these challenges, this paper introduces an AI‐driven resource allocation and beamforming in 6G terahertz networks (AI‐DRAB‐6G‐THz) that integrates dynamic resource allocation and adaptive beamforming for enhanced performance in 6G THz networks. The framework employs deep reinforcement learning (DRL) to intelligently manage spectrum and power resources in real time, thereby optimizing both spectral efficiency and energy consumption. Simultaneously, a neural network–based beamforming model predicts optimal beam angles and alignment strategies by learning from user mobility patterns and channel state information (CSI). A MATLAB‐based 0–1000 rounds simulation environment, incorporating a realistic THz channel model and environmental constraints, is developed to assess system performance. Evaluation across multiple key metrics, including throughput, latency, spectral efficiency, and beam alignment accuracy, demonstrates that the proposed AI‐driven approach significantly outperforms traditional heuristic methods. Overall, this work underscores the feasibility and effectiveness of integrating AI into the physical layer of 6G systems, paving the way for intelligent, adaptive, and energy‐efficient wireless communication in future 6G networks.

ABSTRACT The significant increase in network users has driven the evolution of networking models like virtualization of the network and cloud computing, resulting in a significant increase in heterogeneous network traffic. Ensuring quality of service (QoS) for real‐time and latency‐sensitive traffic, such as audio conferencing, requires intelligent routing mechanisms that manage congestion and resource allocation effectively. Therefore, this paper proposes a multipath congestion‐aware routing based on the Aquila Wild Geese Optimization (AWGO) model for feasible and reliable data delivery for audio conferencing data in Software‐Defined Networking (SDN) environments. The proposed AWGO is a hybrid optimization approach that integrates the strong global exploration and fast exploitation capabilities of the Aquila Optimizer (AO) with the stability, robustness, and scalability of the Wild Geese Algorithm (WGA) for exploring and optimizing multiple routing paths. Here, an audio conferencing application‐based Software‐Defined Networking (SDN) simulation with OpenFlow is implemented, wherein audio conferencing data are considered as sensitive data. The multiobjective fitness function evaluates candidate paths based on residual energy, distance, link quality, delay, traffic bandwidth, congestion level, data priority, and throughput. A fault‐tolerance mechanism is further incorporated to ensure routing robustness under node failure conditions. The performance of the AWGO is evaluated under different traffic conditions including best effort traffic, sensitive traffic, and unwanted traffic. The AWGO demonstrates enhanced performance, achieving a maximum throughput of 430.71 Kbps, computation time of 9.408 s, bandwidth utilization of 96% under best effort traffic, minimum packet loss of 0.101% and delay of 0.087 ms with sensitive traffic, and a jitter of 5.938 ms for unwanted traffic. This demonstrates that the proposed AWGO model is an efficient and scalable solution for reliable data delivery in SDN.

ABSTRACT Software‐defined networking (SDN) is revolutionizing network architecture by offering incredible scalability, flexibility, and programmability. SDN's applications have expanded into various domains, including cloud computing, edge computing, enterprise networks, data centers, and the Internet of Things. One of its standout features is its ability to enhance traffic engineering. Traffic engineering involves optimizing and dynamically controlling traffic flows to reduce congestion and minimize the maximum link utilization, all while managing network traffic from a centralized point. SDN boosts overall network performance, improves user experience, and ensures that resources are used effectively. Minimizing maximum link utilization means distributing network traffic across different links or paths to make use of the most available resources and avoid congestion. This process is often referred to as load balancing or congestion minimization within the network. The primary aim is to prevent any single link from becoming overloaded. Over the years, various algorithms have been proposed to tackle this issue, but still such algorithms struggle with effectively minimizing the maximum link utilization. The proposed work achieves approximately 24% and 16% improvement in load balancing; it reduces congestion by 94% and 60% when compared to tabu search and enhanced tabu search.

ABSTRACT Social multimedia (SM) platforms such as Instagram, LinkedIn, YouTube, Facebook, and Twitter enable users to communicate, share ideas, and exchange information within virtual networks. The widespread use of multimedia‐based applications has led to a substantial increase in social media traffic, often containing sensitive personal and interaction data. These data are vulnerable to security threats, including identity theft and information misuse, highlighting the need for effective anomaly detection mechanisms. This research proposes an enhanced binary spotted hyena optimized dependent linear regression (EBSHO‐DLR) method to detect anomalies in SM networks, ensuring secure data transmission. The framework begins with collecting a Twitter dataset from India, followed by pre‐processing using Gaussian blur (GB) to filter anomalies and histogram of oriented gradients (HOG) for feature extraction, removing irrelevant information. The proposed method integrates software‐defined networking (SDN) to provide dynamic, programmatically efficient network management, improving performance and monitoring compared to conventional approaches. Experimental evaluation demonstrates the effectiveness of the method in detecting fraudulent activities such as identity theft, profile cloning, and unauthorized data collection. Key performance metrics include precision (96.7%) and accuracy (97.8%), confirming the robustness and reliability of the EBSHO‐DLR approach for securing social multimedia communication systems.

ABSTRACT This paper presents a linearly polarized, ground‐defected 2 × 2 rectangular patch antenna array with microstrip inset feeding. A simple structure is realized by placing the rectangular patches and the feeding network on the same layer. The proposed microstrip inset‐fed configuration facilitates proper impedance matching and easy array formation. This work provides an in‐depth examination of the antenna array's characteristics and performance. The antenna array is fabricated on an RT Duroid substrate and integrates a dumbbell‐shaped defected ground structure (DGS) in a triangular form with four radiating elements. The primary objective of incorporating the DGS is to improve the return loss. The proposed compact antenna array has overall dimensions of 106.3 × 112 × 1.575 mm 3 and is intended for wireless communication applications. The designed array operates over the desired frequency band of 3.52–3.61 GHz, achieving a reflection coefficient better than −10 dB and an impedance bandwidth of 25.09%. The antenna exhibits stable radiation characteristics with a peak gain of approximately 13.08 dBi across the operating band. The array is analyzed using HFSS, and fabrication and measurements are carried out to validate the simulated results. The measured outcomes show good agreement with the simulations, demonstrating the suitability of the proposed antenna for 5G midband mobile broadband wireless communication applications.