Wireless Sensor Networks (WSNs) have evolved rapidly and are widely deployed in various applications such as defence, healthcare, automation, and traffic monitoring in smart cities. Due to the rapid development of emerging technologies such as the Internet of Everything (IoE), large volumes of data are transmitted from numerous mobile sensor nodes through various routing mechanisms. In WSNs, efficient data transmission significantly influences energy consumption, node mobility, network topology, and scalability, making routing protocols a critical component in network design. However, many existing routing protocols suffer from high energy consumption, inefficient next-hop selection, and limited adaptability to dynamic network topologies caused by node mobility. These limitations reduce network lifetime and degrade data transmission performance in highly dynamic WSN environments. To address these challenges, this paper proposes a novel Cooperative Transmission and Clustering-based Dynamic Directional Routing Protocol (CTC-DDRP) for WSNs. This protocol operates in three different phases: neighbour transmission area discovery, clustering-based election and selection, and cooperative data transmission. The proposed routing protocol dynamically selects the next forwarding node based on residual energy and mobility criteria, while employing a duty-cycling mechanism to balance energy consumption among the deployed sensor nodes. This highly dynamic and cooperative routing scheme improves reliability and extends network lifetime, even in high-mobility scenarios. The performance of the proposed CTC-DDRP protocol is evaluated through Monte Carlo simulations and compared with several benchmark routing protocols. Simulation results clearly indicate that the proposed CTC-DDRP achieves a 16% higher packet delivery ratio, a 6.0 ms lower end-to-end delay, a 24.05 kbps higher throughput, 2.85 nJ energy savings per round, and 1896.5 nJ higher residual energy after 10,000 rounds, compared with other existing routing protocols under identical simulation conditions. These results indicate that the proposed CTC-DDRP protocol provides an efficient and reliable routing framework for next-generation WSN applications.
NiO/rGO nanocomposites have been synthesized by the hydrothermal method. The various amounts of prepared NiO/rGO nanocomposite were incorporated with perovskite material for fabricating the perovskite solar cells (PSCs). The structure of NiO/rGO-CH3NH3PbI3 based PSC is FTO/c-TiO2/m-TiO2/NiO/rGO-CH3NH3PbI3/Carbon. The present NiO/rGO-CH3NH3PbI3 based PSC obtained a power conversion efficiency (PCE) of 15.27
In recent years, wearable antennas gain significant attention for use in healthcare applications, particularly in Implantable Medical Devices (IMDs) and Medical Body Area Networks. These antennas must be small, lightweight, and body-conforming to meet the needs of these applications. As the wearable antennas play a crucial role in Medical Body Area Networks (MBAN), facilitating continuous health monitoring. Their integration in IMDs requires minimizing electromagnetic interference and optimizing radiation characteristics. As healthcare systems demand more efficient and precise devices, the development of wearable antennas becomes essential for enhanced performance. The development of such antenna has gathered substantial awareness in recent years in the telemedicine industry. This study proposes a two-dimensional square loop-based antenna design, operating at 2.4 GHz with improved radiation properties and reduced backward radiation. The antenna dimensions are 60 × 40 × 0.7 mm3, making it compact and effective for body-worn applications. The antenna design process incorporates Machine Learning (ML) techniques to minimize simulation time, increase efficiency, and enhance design accuracy. ML algorithms optimize the antenna’s performance, particularly in terms of reflection coefficient, bandwidth, and gain. The proposed antenna design has tackled the issues faced by the conventional antenna and holds promise for real-time applications in healthcare, military, sports, and identification systems. It addresses critical challenges such as Specific Absorption Rate (SAR) and efficiency, ensuring optimal performance when interacting with human body tissues. Moreover, the experimental results demonstrate that the simulated and fabricated results exhibit similar deviations, confirming that the antenna is suitable for real-world applications.
The preparation of nanoparticles through sustainable chemical practices offers an eco-friendly and feasible route to advanced nanomaterials. In this study, gold nanoparticles (Au-NPs) were synthesized using Garcinia mangostana (GM) peel extract, which is rich in antioxidants and provides biomolecules acting as bio-reductants, capping, and stabilizing agents. The synthesized Au-NPs were characterized by X-Ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), UV–Vis spectroscopy, scanning electron microscopy (SEM), and photoluminescence (PL) spectroscopy. XRD confirmed the face-centered cubic structure of Au-NPs, while UV–Vis spectra exhibited a characteristic absorption peak around 540 nm. TEM images revealed polydispersed nanoparticles with diverse shapes and sizes. FTIR analysis indicated the presence of phenols, flavonoids, benzophenones, and anthocyanins, suggesting their role in the reduction and stabilization of Au-NPs. The catalytic activity of Au-NPs was demonstrated by the effective degradation of Fuchsin Basic dye, achieving up to 89
Accurate air pollution prediction needs systems that monitor diverse environmental data, adapt to changing pollution patterns, and ensure secure real-time communication. Existing systems struggle to capture distributed sensor interactions and dynamic environmental variations effectively. To address this problem, the study proposes an Adaptive and Efficient Air Pollution Monitoring Network (AEPM-Net) incorporating Metal Oxide Semiconductor (MOS) nanosensors with an average particle size ranging from 20 to 40 nm for sensitive air-quality monitoring. The system monitors levels of Carbon Monoxide CO , Nitrogen Dioxide NO_2 , Sulfur Dioxide SO_2 , Nitric Oxide NO , Volatile Organic Compound VOC , and Particulate Matter PM , which includes fine particulate matter PM2.5 and coarse particulate matter PM10 , in different locations. The system uses SPECK encryption and additive Lightweight Homomorphic Encryption (LHE) to protect collected pollutant data, while noise filtering and modified Z-score normalization methods help to stabilize the data. The Spatio-Temporal Data Processing Module (ST-DPM) analyzes nanosensor data across time and space by capturing correlations among distributed sensors and environmental variations. The Hierarchical Attention Mechanism (HAM) prioritizes important spatio-temporal features to improve prediction accuracy. The Dynamic Adaptation Module (DAM) adjusts parameters in real time, while the Pollution Level Prediction Module (PLPM) forecasts pollutant levels with uncertainty handling. Over 90 days, the model achieved R² values of 0.973 CO , 0.974 NO , 0.975 NO_2 , 0.978 SO_2 , 0.971 VOC , 0.969 PM2.5 , and 0.967 PM10 . The proposed framework supports environmental sustainability by enabling early detection of hazardous pollutants, improving urban air quality monitoring, and assisting effective pollution control to protect public health.
