Epidemic Models of Malicious-Code Propagation and Control in Wireless Sensor Networks: An Indepth Review

Wireless Personal Communications - Besides anti-malware usage for the eradication of malicious attacks, researchers have developed epidemic models in order to gain more insights into the spread...     

Green synthesis of highly stable carbon nanodots and their photocatalytic performance

The present study reports a novel, facile, biosynthesis route for the synthesis of carbon nanodots (CDs) with an approximate quantum yield of 38.5%, using Musk melon extract as a naturally derived‐precursor material. The synthesis of CDs was established by using ultraviolet–visible (UV–vis) spectroscopy, Dynamic light scattering, photoluminescence spectroscopy, X‐ray diffraction, transmission electron microscopy and Fourier transform infrared (FTIR) spectroscopy. The as‐prepared CDs possess an eminent fluorescence under UV–light (λ _ex  = 365 nm). The size range of CDs was found to be in the range of 5–10 nm. The authors further explored the use of such biosynthesised CDs as a photocatalyst material for removal of industrial dye. Degradation of methylene blue dye was performed in a photocatalytic reactor and monitored using UV–vis spectroscopy. The CDs show excellent dye degradation capability of 37.08% in 60 min and reaction rate of 0.0032 min⁻¹. This study shows that synthesised CDs are highly stable in nature, and possess potential application in wastewater treatment.Inspec keywords: carbon, nanostructured materials, nanofabrication, catalysis, photochemistry, ultraviolet spectra, visible spectra, photoluminescence, X‐ray diffraction, transmission electron microscopy, Fourier transform infrared spectra, fluorescence, dyesOther keywords: green synthesis, highly stable CD, photocatalytic performance, biosynthesis route, carbon nanodots, quantum yield, Musk melon extract, naturally derived‐precursor material, ultraviolet‐visible spectroscopy, dynamic light scattering, photoluminescence spectroscopy, X‐ray diffraction, transmission electron microscopy, Fourier transform infrared spectroscopy, FTIR spectroscopy, fluorescence, biosynthesised CD, photocatalyst material, industrial dye, methylene blue dye degradation, photocatalytic reactor, UV‐vis spectroscopy, wastewater treatment, size 5 nm to 10 nm, time 60 min     

Density functional theory and machine learning guided search for RE2Si2O7 with targeted coefficient of thermal expansion

Density functional theory (DFT) calculations and machine learning (ML) methods are used to establish a relationship between the crystal structures of rare-earth (RE) disilicates (RE₂Si₂O₇) and their coefficient of thermal expansion (CTE). The DFT total energy data predict the presence of several energetically competing crystal structures, which is rationalized as one of the reasons for observing polymorphism. An ensemble of support vector regression models is trained to rapidly predict the CTE as a function of RE₂Si₂O₇ crystal chemistry. Experiments subsequently validated the structure and CTE predictions for Sm₂Si₂O₇.     

Adaptive Beamformers for High-Speed Mobile Communication

In this work, we propose a fast conjugate gradient method (CGM) for beamforming, after thoroughly analyzing the performances of the least mean square (LMS), the recursive least square (RLS), and the sample matrix inversion (SMI) adaptive beamforming algorithms. Various experiments are carried out to analyze the performances of each beamformer in detail. The proposed conjugate gradient method does not use the Eigen spread of the signal correlation matrix as in the case of the LMS and the RLS methods. It computes antenna array weights orthogonally for each iteration. Hence the convergence rate and the null depths of the proposed method are much better than the LMS, the SMI the RLS and the classical CGM. Also, the simulation results confirm that this method has a speed improvement of about 60% over the classical conjugate gradient method. This aspect significantly reduces the processor burden and saves a lot of power during the beamforming process. Hence the proposed method is superior compared to the LMS, the RLS, the SMI, and classical CGM and most suitable for high-speed mobile communication.

     

Influence of Clamor on the Transmission of Worms in Remote Sensor Network

Wireless Sensor Network (WSN) with remote sensing capability is gaining popularity in many of the real time applications such as military, healthcare, environment, home and other commercial applications.WSN is typically composed of various components such as sensor node, relay node, cluster head, gateway, base station. Such a critical network is vulnerable to most dangerous threats caused by worms towards the integrity and confidentiality of information passed through it. The study of the influence of clamor in propagation of potential of worm in WSN is of more significance. In this paper, a logical model is proposed that is reliant on pandemic theory. It is an improvement of the SIRS, SEIS and models. We propose an altered SEIRS (Susceptible-Exposed-Infectious-Recovered-Susceptible) model with the added substance background noise that overcomes the drawbacks of the existing models. The close by adequacy of the model has been affirmed using Lyapunov’s work. We similarly address the effect of node fluctuations in the model through numerical simulations that is carried out to prove that our proposed system is mean square stable and resistance against fluctuations with respect to the spread of worms.

     

Momentum Search Algorithm for Analysis of Fuel Cell Vehicle-to-Grid System with Large-Scale Buildings

The design and analysis of a fuel cell vehicle-to-grid (FCV2G) system with a high voltage conversion interface is proposed. The system aims to maximize the utilization of fuel cell vehicles (FCVs) as distributed energy resources, allowing them to actively participate in the energy market. The proposed FCV2G system has FCVs, power electronics interfaces, and the electrical grid. The power electronics interfaces are responsible for converting the low-voltage output of the fuel cell stack into high-voltage DC power, and ensuring efficient power transfer between the FCVs and the grid. To optimize the operation of the FCV2G system, the momentum search algorithm (MSA) is employed. By applying MSA, the FCV2G system can achieve optimal power dispatch, considering factors such as energy efficiency, grid stability, and economic feasibility. The proposed method is tested in MATLAB. The best MSA and dynamic load profile solutions are run for 24 h and the results show that 100% import of FCVs 51.0% more than 100% electric vehicle. Peak-cutting and vehicle-to-grid service revenue are 30.5% and 95.0% greater, respectively. Low discharge loss, high capacity, and high discharge power are the main advantages of FCVs. The benchmark FCVs ratio of 15% is used for sensitivity analysis. The findings reveal that the overall advantages of FCV2G are improved.     

Effect of Noise on Pandemic Structure for Proliferation of Malevolent Nodes in Remote Sensor Network

Wireless Personal Communications - Remote sensor systems experience basic investigation of network security because of inexplicable operational imperatives. The challenge flinches with the fragment...     

Intelligence Framework Based Analysis of Spatial–Temporal Data with Compressive Sensing Using Wireless Sensor Networks

Wireless sensor networks produce immense sensor readings within a report interval to the sink. So transfer of information in a resource constrained wireless environment is difficult. Compressive sensing overcomes the resource constrains in wireless environment by exploiting sparsity in transfer with fewer measurement and recovery of original signal. In this research Intelligent Neighbor Aided Compressive Sensing (INACS) scheme is proposed for efficient data assembly in spatial and temporal correlated WSNs. Sparse Matrix has been formed with spatial and temporal coordinates for data transfer. In every sensing period, the sensor node just sends the readings within the sensing period to uniquely selected neighbour based on a correlation. The transmission period provides significant improvement with compressed data using INACS with the measurement matrix. Thus INACS provides reduction in number of transmission and higher reconstruction accuracy. INACS has been compared with Compressive wireless sensing for reduction in number of transmissions achieved. The time series analysis with INACS has been done to validate the simultaneous association between number of transmissions and time period.