Editorial: Special issue on “Internet of things and edge computing in the new 5G era”

1.Introduction Recently,edge computing has evolved from concept to early-stage implementations because new use cases require a more remote computing and networking approach than a conventional,fully cloud-based model.Many companies,network providers and cloud companies are testing and launching early commercial products in the developed markets of the United States,Europe,China and Asia Pacific.Although progress in edge trials continues,key issues remain to be resolved around the most viable location of the edge,the scale of the required edge investment,and the actual business models.

Yield of Ozone,Nitrite Nitrogen and Hydrogen Peroxide Versus Discharge Parameter Using APPJ Under Water

Discharge plasma in and in contact with water can be accompanied with ultraviolet radiation and electron impact, thus can generate hydroxyl radicals, ozone, nitrite nitrogen and hydrogen peroxide. In this paper, a non-equilibrium plasma processing system was established by means of an atmospheric pressure plasma jet immersed in water. The hydroxyl intensities and discharge energy waveforms were tested. The results show that the positive and negative discharge energy peaks were asymmetric, where the positive discharge energy peak was greater than the negative one. Meanwhile, the yield of ozone and nitrite nitrogen was enhanced with the increase of both the treatment time and the discharge energy. Moreover, the pH value of treated water was reduced rapidly and maintained at a lower level. The residual concentration of hydrogen peroxide in APPJ treated water was kept at a low level. Additionally, both the efficiency energy ratio of the yield of ozone and nitrite nitrogen and that of the removal of p-nitrophenol increased as a function of discharge energy and discharge voltage. The experimental results were fully analyzed and the chemical reaction equations and the physical processes of discharges in water were given.

Simulation of Life Game Based on Cellular Automata

Vs2010 is used as the development environment, so as to realize the visual programming of Game of Life, and explore the life evolution process of cell group in different sizes and states. According to cell forms such as circulation and disappearance, it reflects the complex changes of Game of Life. Setting different initial states through the code and observing the final generated graphics, you can see that the complex and simple initial states can achieve the same result. It is also concluded that a suitable initial state can reach the final state in fewer steps, which can greatly simplify the evolution process. The entire system is completely closed and has certain limitations. Meanwhile, the evolution process of symmetrical initial state is also symmetrically distributed. I introduce random quantities into the system to make the simulation results closer to the actual situation. By setting a random initial state to make the chaotic and disorderly situation simple, the concept of “determinism and randomness” can be better expressed. In the process of change, some local structures remain fixed, and some local structures present periodic cycles. These structures interact in complex ways to understand the concept of “whole and part”. The game of life enlightens us: the simplest logical rules can produce complex and interesting activities, and a complex system may be iterated by simple rules.

Congestion Relief in Downlink OFDMA Cellular System Using Inter-Cell Relay

This paper proposes the concept of inter-cell relay for downlink orthogonal frequency division multiple access(OFDMA) cellular systems, which uses multi-hop to relay calls from overloaded cells to light-load neighboring cells. It is shown that when using inter-cell relay, the number of calls in the congestion cell can be significantly increased. The congestion cell is divided into two parts. One is called non-relay area(NRA), in which a call directly communicates with the base station(BS) of a congested cell. The other is called relay area(RA), in which a call communicates with the BS of a neighboring cell through a relay station(RS). The two parts have different user-call densities. By adjusting the densities of two parts, we will maximize the number of supported calls inside a congested cell. The results show the benefits gained from inter-cell relay in congestion relief, which can reduce cell congestion by fully utilizing the available resources in the neighboring cells.

Effect of porous surface layer on wave propagation in elastic cylinder immersed in fluid

To study the damage to an elastic cylinder immersed in fluid, a model of an elastic cylinder wrapped with a porous medium immersed in fluid is designed. This structure can both identify the properties of guided waves in a more practical model and address the relationship between the cylinder damage degree and the surface and surrounding medium. The principal motivation is to perform a detailed quantitative analysis of the longitudinal mode and flexural mode in an elastic cylinder wrapped with a porous medium immersed in fluid. The frequency equations for the propagation of waves are derived each for a pervious surface and an impervious surface by employing Biot theory. The influences of the various parameters of the porous medium wrapping layer on the phase velocity and attenuation are discussed. The results show that the influences of porosity on the dispersion curves of guided waves are much more significant than those of thickness,whereas the phase velocity is independent of the static permeability. There is an apparent “mode switching” between the two low-order modes. The characteristics of attenuation are in good agreement with the results from the dispersion curves.This work can support future studies for optimizing the theory on detecting the damage to cylinder or pipeline.

Capacity of a Class of Dirty-Tape Gaussian Orthogonal Relay Channel with State Information at the Source and Relay

A Gaussian channel with additive interference that is causally known to the transmitter is called a Dirty-Tape Channel(DTC).In this paper,we consider a state-dependent dirty-tape Gaussian relay channel with orthogonal channels from the source to the relay and from the source and relay to the destination.The orthogonal channels are corrupted by two independent additive interferences causally known to both the source and relay.The lower and upper bounds of the channel capacity are established.The lower bound is obtained by employing superposition coding at the source,Partial Decode-and-Forward(PDF)relaying at the relay,and a strategy similar to that used by Shannon at the source and relay.The explicit capacity is characterised when the power of the relay is sufficiently large.Finally,several numerical examples are provided to illustrate the impact of additive interferences and the role of the relay in information transmission and in removing the interference.

Reversible Watermarking Method with Low Distortion for the Secure Transmission of Medical Images

In telemedicine,the realization of reversible watermarking through information security is an emerging research field.However,adding watermarks hinders the distribution of pixels in the cover image because it creates distortions(which lead to an increase in the detection probability).In this article,we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high security.The proposed method selects two adjacent gray pixels whose least significant bit(LSB)is different from the relevant message bit and then calculates the distortion degree.We use the LSB pairing method to embed the secret matrix of patient record into the cover image and exchange pixel values.Experimental results show that the designed method is robust to different attacks and has a high PSNR(peak signal-to-noise ratio)value.The MRI image quality and imperceptibility are verified by embedding a secret matrix of up to 262,688 bits to achieve an average PSNR of 51.657 dB.In addition,the proposed algorithm is tested against the latest technology on standard images,and it is found that the average PSNR of our proposed reversible watermarking technology is higher(i.e.,51.71 dB).Numerical results show that the algorithm can be extended to normal images and medical images.

Surface lattice resonance of circular nano-array integrated on optical fiber tips

As metallic nanoparticles are arranged to form a 2D periodic nano-array,the coupling of the localized surface plasmonic resonance(LSPR)results in the well-known phenomenon of surface lattice resonances(SLRs).We theoretically investigate the SLR effect of the circular nano-array fabricated on the fiber tips.The difference between the 2D periodic and circular periodic arrays results in different resonant characteristics.For both structures,the resonant peaks due to the SLRs shift continuously as the array structures are adjusted.For some specific arrangements,the circular nano-array may generate a single sharp resonant peak with extremely high enhancement,which originates from the collective coupling of the whole array.More interestingly,the spatial pattern of the vector near-field corresponding to the sharp peak is independent of the polarization state of the incidence,facilitating its excitation and regulation.This finding may be helpful for designing multifunctional all-fiber devices.

Conversion of adverse data corpus to shrewd output using sampling metrics

An imbalanced dataset is commonly found in at least one class,which are typically exceeded by the other ones.A machine learning algorithm(classifier)trained with an imbalanced dataset predicts the majority class(frequently occurring)more than the other minority classes(rarely occurring).Training with an imbalanced dataset poses challenges for classifiers;however,applying suitable techniques for reducing class imbalance issues can enhance classifiers’performance.In this study,we consider an imbalanced dataset from an educational context.Initially,we examine all shortcomings regarding the classification of an imbalanced dataset.Then,we apply data-level algorithms for class balancing and compare the performance of classifiers.The performance of the classifiers is measured using the underlying information in their confusion matrices,such as accuracy,precision,recall,and F measure.The results show that classification with an imbalanced dataset may produce high accuracy but low precision and recall for the minority class.The analysis confirms that undersampling and oversampling are effective for balancing datasets,but the latter dominates.

Effect of viscosity on stability and accuracy of the two-component lattice Boltzmann method with a multiple-relaxation-time collision operator investigated by the acoustic attenuation model

A two-component lattice Boltzmann method(LBM) with a multiple-relaxation-time(MRT) collision operator is presented to improve the numerical stability of the single relaxation time(SRT) model. The macroscopic and the momentum conservation equations can be retrieved through the Chapman–Enskog(C-E) expansion analysis. The equilibrium moment with the diffusion term is calculated, a diffusion phenomenon is simulated by utilizing the developed model, and the numerical stability is verified. Furthermore, the binary mixture channel model is designed to simulate the sound attenuation phenomenon, and the obtained simulation results are found to be consistent with the analytical solutions. The sound attenuation model is used to study the numerical stability and calculation accuracy of the LBM model. The simulation results show the stability and accuracy of the MRT model and the SRT model under different viscosity conditions. Finally,we study the influence of the error between the macroscopic equation of the MRT model and the standard incompressible Navier–Stokes equation on the calculation accuracy of the model to demonstrate the general applicability of the conclusions drawn by the sound attenuation model in the present study.

Design and Optimization of Actuator for HT-25 Multifunctional Loader

To break down the development interaction of the working gadget of the multi-practical wheel loader and to compute the heap of each part, the Denavit-Hartenberg strategy was applied to build up the kinematics of the instrument model. Also, all the while, set up the elements model of dynamic framework. A multi-body element programming MSC, ADAMS and its active module were applied to assemble component power through a pressure framework reenactment model. An entirety working cycle interaction of the functioning gadget of the wheel loader was mimicked, and the investigation results thoroughly show the development interaction of the functional device and the stacked state of each part, and check the mechanical properties of the working gadget and dynamic execution water-driven framework effectively.

AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks Performance

In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality.This research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in WSNs.The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination.Employing a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication chain.Through comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced performance.Furthermore,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.

Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.

A General Framework for Intelligent IoT Data Acquisition and Sharing in an Untrusted Environment Based on Blockchain

Traditional Io T systems suffer from high equipment management costs and difficulty in trustworthy data sharing caused by centralization.Blockchain provides a feasible research direction to solve these problems. The main challenge at this stage is to integrate the blockchain from the resourceconstrained Io T devices and ensure the data of Io T system is credible. We provide a general framework for intelligent Io T data acquisition and sharing in an untrusted environment based on the blockchain, where gateways become Oracles. A distributed Oracle network based on Byzantine Fault Tolerant algorithm is used to provide trusted data for the blockchain to make intelligent Io T data trustworthy. An aggregation contract is deployed to collect data from various Oracle and share the credible data to all on-chain users. We also propose a gateway data aggregation scheme based on the REST API event publishing/subscribing mechanism which uses SQL to achieve flexible data aggregation. The experimental results show that the proposed scheme can alleviate the problem of limited performance of Io T equipment, make data reliable, and meet the diverse data needs on the chain.

Investigation of cavitation bubble collapse near rigid boundary by lattice Boltzmann method

The dynamics of the bubble collapse near a rigid boundary is a fundamental issue for the bubble collapse application and prevention. In this paper, the bubble collapse is modeled by adopting the lattice Boltzmann method （LBM） and is verified, and then the dynamic characteristics of the collapsing bubble with the second collapse is investigated. The widely used Shan-Chen model in the LBM multiphase community is modified by coupling with the Carnahan-Starling equation of state （C-S EOS） and the exact difference method （EDM） for the forcing term treatment. The simulation results of the bubble profile evolution by the LBM are in excellent agreements with the theoretical and experimental results. From the two-dimensional pressure field evolution, the dynamic characteristics of the different parts during the bubble collapse stage are studied. The role of the second collapse in the rigid boundary damage is discussed, and the impeding effect between two collapses is demonstrated.

Pseudopotential multi-relaxation-time lattice Boltzmann model for cavitation bubble collapse with high density ratio

The dynamics of the cavitation bubble collapse is a fundamental issue for the bubble collapse application and prevention. In the present work, the modified forcing scheme for the pseudopotential multi-relaxation-time lattice Boltzmann model developed by Li Q et al. [ Li Q, Luo K H and Li X J 2013 Phys. Rev. E 87 053301] is adopted to develop a cavitation bubble collapse model. In the respects of coexistence curves and Laplace law verification, the improved pseudopotential multi-relaxation-time lattice Boltzmann model is investigated. It is found that the thermodynamic consistency and surface tension are independent of kinematic viscosity. By homogeneous and heterogeneous cavitation simulation, the ability of the present model to describe the cavitation bubble development as well as the cavitation inception is verified. The bubble collapse between two parallel walls is simulated. The dynamic process of a collapsing bubble is consistent with the results from experiments and simulations by other numerical methods. It is demonstrated that the present pseudopotential multirelaxation-time lattice Boltzmann model is applicable and efficient, and the lattice Boltzmann method is an alternative tool for collapsing bubble modeling.

Extended Kalman Filter-based localization algorithm by edge computing in Wireless Sensor Networks

The Extended Kalman Filter(EKF)has received abundant attention with the growing demands for robotic localization.The EKF algorithm is more realistic in non-linear systems,which has an autonomous white noise in both the system and the estimation model.Also,in the field of engineering,most systems are non-linear.Therefore,the EKF attracts more attention than the Kalman Filter(KF).In this paper,we propose an EKF-based localization algorithm by edge computing,and a mobile robot is used to update its location concerning the landmark.This localization algorithm aims to achieve a high level of accuracy and wider coverage.The proposed algorithm is helpful for the research related to the use of EKF localization algorithms.Simulation results demonstrate that,under the situations presented in the paper,the proposed localization algorithm is more accurate compared with the current state-of-the-art localization algorithms.

Adaptive neural network based boundary control of a flexible marine riser system with output constraints

In this study,we develop an adaptive neural network based boundary control method for a flexible marine riser system with unknown nonlinear disturbances and output constraints to suppress vibrations.We begin with describing the dynamic behavior of the riser system using a distributed parameter system with partial differential equations.To compensate for the effect of nonlinear disturbances,we construct a neural network based boundary controller using a radial basis neural network to reduce vibrations.Under the proposed boundary controller,the state of the riser is guaranteed to be uniformly bounded based on the Lyapunov method.The proposed methodology provides a way to integrate neural networks into boundary control for other flexible robotic manipulator systems.Finally,numerical simulations are given to demonstrate the effectiveness of the proposed control method.

Electric characteristic and cavitation bubble dynamics using underwater pulsed discharge

Underwater pulsed discharge is widely applied in medicine, machining, and material modification.The induced cavitation bubble and subsequent cavitation collapse are considered the major motivations behind these applications. This paper presents an underwater pulsed discharge system.The experimental setup is established to induce and investigate the cavitation bubble assisted with a high-speed camera. Three aspects, including the characteristic of the discharge with different applied voltages and conductivities, the evolution of the cavitation bubble profile, and the energy efficiency of cavitation bubble inducing, are investigated, respectively. Especially, the mechanism of pre-discharge time delay in the low field intensity case is explained using the Joule heat effect.The results show the validity of the underwater pulsed discharger and experimental setup. The present underwater pulsed discharger is proved to be a simple, portable, and easy-to-implement device for the investigation of cavitation bubble dynamics.