ABSTRACT This paper focuses on developing an innovative Color‐Coded Sliding Window Random Linear Network Coding (CCSW‐RLNC) strategy, where the sliding window size dynamically adapts based on network conditions, including intermediate node availability, power limitations, channel noise, and multihop transmission characteristics. The system features a special two‐direction mechanism, where data windows are moved from left to right as color priority is shifted from right to left. The central color has the highest weightage to ensure data integrity. The enhancement of this approach is through a deep learning model, which optimizes the window parameters and adaptation process. The primary targets are to enhance energy efficiency and maximize throughput through the optimal synchronization of encoder and decoder and to achieve minimum latency. The color‐coding scheme serves as both a data representation system and a priority management system, enabling the more accurate reconstruction of original data packets. This new hybrid, combining RLNC, color‐coding, and deep learning adaptability addresses realistic network limitations while enhancing system performance and overall reliability.

ABSTRACT This article presents a new approach for linearly polarized cavity‐backed antennas. The design utilizes the advanced half‐mode substrate‐integrated waveguide (HM‐SIW) approach to form a radiating aperture instead of minimizing the structure. The HM‐SIW method reduces the antenna size by constructing a semicircular cavity. A 50‐ feedline is used to stimulate the cavity and ensure compatibility with the planar circuit. A single‐sided printed circuit board (PCB) layer is sufficient for implementation. The radiation characteristics of a proposed antenna have been measured. In addition, the radiation patterns of the proposed antenna were estimated using the antenna theory concept in an anechoic chamber. There is good agreement between the simulation and the observed measured results. These results show that the proposed hybrid resonator antenna (HRA) also has the advantages of conventional cavity‐backed antennas, including a wider fractional bandwidth (FBW) of 12.91% (7.56–8.62 GHz), overall gain of 7.05 dBi, radiation efficiency of 95.25%, and a simulated front‐to‐back ratio (FTBR) of 18.49 dB. The whole size of the LP proposed antenna is . This antenna offers several benefits: compactness, low profile, lightweight, low fabrication cost, and easy integration with planar circuits. It is mainly used for satellite communication systems.

ABSTRACT Accurate and efficient beam prediction is essential for reliable millimeter‐wave communication in emerging sixth‐generation (6G) networks. However, high‐dimensional channel data, noise sensitivity, and computational complexity limit the real‐time applicability of existing deep learning models. This paper proposes a lightweight and robust beam prediction framework that integrates curve‐based signal preprocessing, hybrid feature selection, and a dual‐stream Kolmogorov–Arnold transformer‐convolutional architecture optimized through Cleaner Fish Optimization. The preprocessing stage enhances signal stability, whereas the hybrid optimization strategy selects compact and informative features to reduce redundancy. The proposed dual‐stream architecture improves nonlinear representation efficiency without incurring high computational overhead. Experimental results demonstrate superior prediction accuracy, reduced inference time, and lower computational cost compared with conventional convolutional neural network, recurrent, and transformer‐based models. The framework achieves near‐perfect accuracy while maintaining real‐time suitability, making it a promising solution for scalable and trustworthy 6G millimeter‐wave beam management.

ABSTRACT Wireless sensor networks (WSNs) are commonly utilized in various application areas, ranging from environmental monitoring to industrial automation. In WSNs, energy efficiency (EE) is a crucial feature due to sensor nodes' (SNs) limited power resources. Unequal clustering (UC) and routing techniques have emerged as effective approaches to balance energy consumption (ECON) and prolong network lifetime (NLT). However, finding optimal solutions for UC and routing optimization problems in large‐scale WSNs remains challenging. Recently, metaheuristic algorithms have been widely employed to determine the appropriate cluster sizes, cluster heads (CHs), and routing paths that minimize ECON imbalances while maximizing NLT or throughput using various parameters and objectives. Therefore, this study proposes a new bonobo optimization algorithm based on UC with a pelican optimization‐based routing (BOAUC‐POR) technique for energy‐efficient WSNs. The projected BOAUC‐POR technique involves two significant phases: UC construction and routing. In the preliminary phase, the BOAUC‐POR technique uses the BOAUC technique to explore the search space effectively for optimal CH selection and cluster size, considering factors like node density, residual energy (RE), and distance. Besides, the BOAUC‐POR technique follows the POR model for the optimum selection of routes to BS using RE, node degree (ND), and distance. The proposed BOAUC‐POR technique effectively maximizes the EE and NLT in WSN. The simulation values stated that the BOAUC‐POR technique accomplishes superior performance in terms of EE and NLT. The BOAUC‐POR technique exhibits promising results for enhancing the EE and prolonging the lifetime of WSNs, thus enabling sustainable and reliable operation in resource‐constrained environments. The comparison study of the BOAUC‐POR technique portrayed superior values of 510 for HND in the 500‐node scenario and 124 s for CT at 700 rounds.

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 In a healthcare Internet of Things (IoT) system, generally, data from sensors are to be communicated to cloud/edge servers for analysis. However, increasing reliance on healthcare IoT has increased the demand for efficient and intelligent data routing solutions to address challenges such as energy consumption, latency, and reliability. This work proposes an advanced routing framework that leverages software‐defined networking (SDN) to enhance adaptability and control in healthcare networks. The system integrates a graph convolutional network–gated recurrent unit (GCN‐GRU) model for accurate network state prediction and a soft actor‐critic (SAC) reinforcement learning approach for dynamic routing decisions. By combining traffic prediction with multi‐objective optimization, the proposed method minimizes energy consumption, reduces end‐to‐end latency, and improves the packet delivery ratio. This framework prioritizes critical healthcare data, ensures efficient resource utilization, and accommodates the unique requirements of wearable and bedside devices. Mininet simulations show the GCN‐GRU‐SAC framework achieves a throughput of 57 pkts/s, residual energy of 58 J, an average end‐to‐end delay of 25 ms, and a packet delivery ratio (PDR) of 96.5%, outperforming protocols like AODV (32 ms, 96.0% PDR), DDSRP (35 ms, 93.0% PDR), RLBEEP (30 ms, 96.0% PDR), IARP (20 ms, 93.0% PDR), and others (e.g., DC‐ACOP: 55 pkts/s, 54 J). This framework improves energy efficiency, prioritizes critical data, and ensures reliable transmission, offering a scalable solution for dynamic healthcare IoT environments.

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.