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This paper aims to provide prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). Forming clusters of mobile nodes is a great task owing to their dynamic nature. Such clustering has to be performed with a higher consumption of energy. Perhaps sensor nodes might be supplied with batteries that cannot be recharged or replaced while in the field of operation. One optimistic approach to handle the issue of energy consumption is an efficient way of cluster organization using the particle swarm optimization (PSO) technique.

In this paper two improved versions of centralized PSO, namely, unequal clustering PSO (UC-PSO) and hybrid K-means clustering PSO (KC-PSO), are proposed, with a focus of achieving various aspects of clustering parameters such as energy consumption, network lifetime and packet delivery ratio to achieve energy-efficient and reliable communication in MWSNs.

Theoretical analysis and simulation results show that improved PSO algorithms provide a balanced energy consumption among the cluster heads and increase the network lifetime effectively.

In this work, each sensor node transmits and receives packets at same energy level only. In this work, focus was on centralized clustering only.

To validate the proposed swarm optimization algorithm, a simulation-based performance analysis has been carried out using NS-2. In each scenario, a given number of sensors are randomly deployed and performed in a monitored area. In this work, simulations were carried out in a 100 × 100 m² network consisting 200 nodes by using a network simulator under various parameters. The coordinate of base station is assumed to be 50 × 175. The energy consumption due to communication is calculated using the first-order radio model. It is considered that all nodes have batteries with initial energy of 2 J, and the sensing range is fixed at 20 m. The transmission range of each node is up to 25 m and node mobility is set to 10 m/s.

This proposed work utilizes the swarm behaviors and targets the improvement of mobile nodes’ lifetime and energy consumption.

PSO algorithms have been implemented for dynamic sensor nodes, which optimize the clustering and CH selection in MWSNs. A new fitness function is evaluated to improve the network lifetime, energy consumption, cluster formation, packet transmissions and cluster head selection.

The purpose of this paper is to find the droplets impact on the airplane wing structure. Two kinds of characteristics of the droplet at different velocity and viscosity are assumed. The droplet is assumed to be spherical cubic form and it is injected from the convergent divergent nozzle with a passive control.

This paper presents the results of a numerical simulation of droplet impact on the horizontal surface. The effects of impact parameters are studied. The splash effect of the droplet also visualized. The results are presented in form of stress, strain, displacement magnitude of the droplet.

Crosswire is used as passive control. The behavior of the droplet impact is observed based on the kinetic energy and the gravitational forces.

The results predict that smooth particle hydrodynamic designed droplet not only depend on the equation of state of the droplet but also the injection velocity from the nozzle. It also determined that droplet velocity is depending on the viscosity of the fluid.

This paper aims to provide a prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). MWSNs have characteristics of dynamic topology due to the factors such as energy consumption and node movement that lead to create a problem in lifetime of the sensor network. Node clustering in wireless sensor networks (WSNs) helps in extending the network life time by reducing the nodes’ communication energy and balancing their remaining energy. It is necessary to have an effective clustering algorithm for adapting the topology changes and improve the network lifetime.

This work consists of two centralized dynamic genetic algorithm-constructed algorithms for achieving the objective in MWSNs. The first algorithm is based on improved Unequal Clustering-Genetic Algorithm, and the second algorithm is Hybrid K-means Clustering-Genetic Algorithm.

Simulation results show that improved genetic centralized clustering algorithm helps to find the good cluster configuration and number of cluster heads to limit the node energy consumption and enhance network lifetime.

In this work, each node transmits and receives packets at the same energy level throughout the solution. The proposed approach was implemented in centralized clustering only.

The main reason for the research efforts and rapid development of MWSNs occupies a broad range of circumstances in military operations.

The research highly gains impacts toward mobile-based applications.

A new fitness function is proposed to improve the network lifetime, energy consumption and packet transmissions of MWSNs.

Introduction: With many new technologies requiring real-time data processing, cloud computing has become challenging to implement due to high bandwidth and high latency requirements.

Purpose: To overcome this issue, edge computing is used to process data at the network’s edge. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed. It is used to process time-sensitive data.

Methodology: The authors implemented the model using Linux Foundation’s open-source platform EdgeX Foundry to create an edge-computing device. The model involved getting data from an on-board sensor (on-board diagnostics (OBD-II)) and the GPS sensor of a car. The data are then observed and computed to the EdgeX server. The single server will send data to serve three real-life internet of things (IoT) use cases: auto insurance, supporting a smart city, and building a personal driving record.

Findings: The main aim of this model is to illustrate how edge computing can improve both latency and bandwidth usage needed for real-world IoT applications.

Collaboration is an important emerging dimension of sustainable supply chain management. How to improve supply chain collaboration (SCC) by means of operational excellence approaches has become an important research topic. The Internet of things (IoT), an important means of operational excellence, has also received increased attention. For better collaboration by the IoT, this study proposes a novel methodology to evaluate the measures of IoT adoption in SCC.

Based on the six-domain model and the common classification of collaboration, the measures of the IoT and the criteria of SCC are developed, respectively. A hybrid multi-step methodology that combines neutrosophic set theory, analytic hierarchy process (AHP) and technology for order preference by similarity to an ideal solution (TOPSIS) is proposed to complete the evaluation.

The results show that improving information transparency, strengthening the integration of management information systems and improving large data processing abilities are the most important measures of the IoT in improving SCC. Measures such as introducing sensing technology and laser scanning technology rank at the bottom and are relatively unimportant.

The research results provide insights and references for firms to improve SCC by adopting appropriate IoT measures.

Most of existing studies indicate the significance of technology in SCC. But this study shows a different conclusion that technologies rank the bottom, while information transparency is more important. And a suitable explanation is given. It further enriches the theoretical studies in SCC field.

The purpose of this paper is to minimize corrosion-related pollution in the environment. From the lemongrass extract (LGE), the authors selected one of the best green inhibitors.

The corrosion and inhibition of mild steel in traditional acidification solutions were estimated by electrochemical measurements. The corrosion appearance was observed with scanning electron microscopy, atomic force microscopy micrographs and attenuated total reflectance infrared spectroscopy spectrum. The correlation was formed between the gained inhibition efficiency (IE)% from electrochemical measurements and certain quantum chemical parameters.

The results displayed that the IE was up to 90% when the LGE concentration was 300 ppm. The results confirmed that the theoretical experiments are very similar to the experimental observations.

For the first time, LGE was used as a cheap and safe corrosion inhibitor for mild steel corrosion in the acidification process. The mechanism of mild steel corrosion and anti-corrosion in acid solution has been suggested.

This paper aims to contribute novel insights into the analysis of thin functionally graded material (FGM) plates with variable thickness, considering both temperature-dependent and independent material properties, focusing on critical linear buckling temperature rise and the effect of critical linear moisture for various moisture concentrations.

The study derives stability and equilibrium equations for thin rectangular FGM plates under hygrothermal loading, employing classical plate theory (CPT). Buckling behavior is examined using Galerkin’s method to obtain pre-buckling force resultants.

The findings highlight significant increases in critical buckling temperature with aspect ratio, distinct temperature sensitivity between materials and increasing moisture susceptibility with larger aspect ratios. These insights inform material selection and design optimization for FGM plates under hygrothermal loading, enhancing engineering applications.

This research primarily focuses on hypothetical scenarios and mathematical model development and analysis.

This paper presents original contributions in the field by addressing the hygrothermal buckling analysis of thin FGM rectangular plates with variable thickness, utilizing CPT, thereby enriching the understanding of structural behavior in varying environmental conditions.

The Purpose of this study to examine the magneto hydrodynamics (MHD) using the analytical and numerical tool. In recent years, MHD growing tremendously due to the presence of multidisciplinary application in solving the tedious problems in the viscous flow.

The flows between the parallel plates under the steady inclined magneto hydrodynamic force were studied under the presence of different hall current and pressure gradient. The system was designed with the Darcian porous medium subjected to the incompressible flow. To analyse the flow reactions through stationary parallel plates, the governing equations were used using the integral transformation.

The velocity of the flows depends on the Hall parameter. As the intensity of the magnetic field increases the velocity of the flow is affected significantly. On the other hand, the radiation parameters also affect the flow of any medium through the porous medium.

Implementation of the Laplace and Fourier transform increases the reliability of the obtained results and further decreases the uncertainty during the measurement of the velocity of the flow without any restraints.

From the evident results, it is clear that the proposed MHD model can be applied to several operations of the fluid dynamic models. Further, the application of this technique will decrease the uncertainty in the results compared to the conventional computational models and other finite element and difference approaches.

Functionally graded materials are a special class of composites in which material are graded either continuously or layered wise depending upon its applications. With such variations of materials, the properties of structure vary either lengthwise or thickness wise. This paper aims to investigate models for effective estimation of material properties, as it is necessary for industries to identify the properties of composites or functionally graded materials (FGM’s) before manufacturing and also to develop novel material combinations.

Available models were compared for different material combinations and tested with experimental data for properties such as Young’s modulus, density, coefficient of thermal expansion (CTE) and thermal conductivity. Combinations of metal–ceramic and metal–metal were selected such that their ratios cover a wide range of materials.

This study reveals different models will be required depending on the material used and properties to be identified.

The results of the present work will help researchers in the effective modeling of composites or FGM’s for any analysis.

This paper presents a comparison and review of various analytical methods with experimental data graphically to find out the best suitable method. For the first time, the Halpin-Tsai model was extended in the analysis of the CTE which shows good approximations.

The demand for energy is increasing massively due to urbanization and industrialization. Due to the massive usage of diesel engines in the transportation sector, global warming is increasing rapidly. The purpose of this paper is to use hydrogen as the potential alternative for diesel engine.

A series of tests conducted in the twin cylinder four stroke diesel engine at various engine speeds. In addition to the hydrogen, the ultrasonication is applied to add the nanoparticles to the neat diesel. The role of nanoparticles on engine performance is effective owing to its physicochemical properties. Here, neat diesel mixed 30% of biodiesel along with the hydrogen at the concentration of 10%, 20% and 30% and 50 ppm of graphite oxide to form the blends DNH10, DNH20 and DNH30.

Inclusion of both hydrogen and nanoparticles increases the brake power and brake thermal efficiency (BTE) of the engine with relatively less fuel consumption. Compared to all blends, the maximum BTE of 33.3% has been reported by 30% hydrogen-based fuel. On the contrary, the production of the pollutants also reduces as the hydrogen concentration increases.

Majority of the pollutants such as carbon monoxide, carbon dioxide and hydrocarbon were dropped massively compared to diesel. On the contrary, there is no reduction in nitrogen of oxides (NOx). Highest production of NOx was witnessed for 30% hydrogen fuel due to the premixed combustion phase and cylinder temperatures.