An Optimal Node Localization in WSN Based on Siege Whale Optimization Algorithm

Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times,wireless sensor network(WSN)finds their suitability in several application areas,ranging from military to commercial ones.Since nodes in WSN are placed arbitrarily in the target field,node localization(NL)becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes.The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate.With this motivation,this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme(IAOAB-NLS)for WSN.The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes.In addition,the IAOAB-NLS model is stimulated by the behaviour of Aquila.The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network.For guaranteeing the proficient NL process of the IAOAB-NLS model,widespread experimentation takes place to assure the betterment of the IAOAB-NLS model.The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

Topology and Shape Optimization of 2-D and 3-D Micro-ArchitecturedThermoelastic Metamaterials Using a Parametric Level Set Method

2-D and 3-D micro-architectured multiphase thermoelastic metamaterials are designed and analyzed using a parametric level set method for topology optimization and the finite element method.An asymptotic homogenization approach is employed to obtain the effective thermoelastic properties of the multiphase metamaterials.Theε-constraint multi-objective optimization method is adopted in the formulation.The coefficient of thermal expansion(CTE)and Poisson’s ratio(PR)are chosen as two objective functions,with the CTE optimized and the PR treated as a constraint.The optimization problems are solved by using the method of moving asymptotes.Effective isotropic and anisotropic CTEs and stiffness constants are obtained for the topologically optimized metamaterials with prescribed values of PR under the constraints of specified effective bulk modulus,volume fractions and material symmetry.Two solid materials along with one additional void phase are involved in each of the 2-D and 3-D optimal design examples.The numerical results reveal that the newly proposed approach can integrate shape and topology optimizations and lead to optimal microstructures with distinct topological boundaries.The current method can topologically optimize metamaterials with a positive,negative or zero CTE and a positive,negative or zero Poisson’s ratio.

Numerical Simulation for Analysis and Optimization of the Elastic Properties of a C-spring Tube

In this paper we use the finite element analysis software ANSYS10.0 to establish a finite element model for pressure gauge C -spring tube, and the main factors affected the relation between the pressure and distortion displacement are analyzed. The results show that with the increasing of the spring tube wall thickness T and the minor axis length B, the elastic rigidity of spring tube increases, which is reduced with spring tube diameter D. Moreover, the major axis length H increases. Meanwhile, a pressure gauge spring tube has been optimized according to the simulation results bused on a model with special features, and the design results achieve the intended purpose. The analysis results provide a reliable method for the research and design of similar elastic elements.

Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm

Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.

3D elastic waveform modeling with an optimized equivalent staggered-grid finite-difference method

Equivalent staggered-grid(ESG) as a new family of schemes has been utilized in seismic modeling,imaging,and inversion.Traditionally,the Taylor series expansion is often applied to calculate finite-difference(FD) coefficients on spatial derivatives,but the simulation results suffer serious numerical dispersion on a large frequency zone.We develop an optimized equivalent staggered-grid(OESG) FD method that can simultaneously suppress temporal and spatial dispersion for solving the second-order system of the 3 D elastic wave equation.On the one hand,we consider the coupling relations between wave speeds and spatial derivatives in the elastic wave equation and give three sets of FD coefficients with respect to the P-wave,S-wave,and converted-wave(C-wave) terms.On the other hand,a novel plane wave solution for the 3 D elastic wave equation is derived from the matrix decomposition method to construct the time-space dispersion relations.FD coefficients of the OESG method can be acquired by solving the new dispersion equations based on the Newton iteration method.Finally,we construct a new objective function to analyze P-wave,S-wave,and C-wave dispersion concerning frequencies.The dispersion analyses show that the presented method produces less modeling errors than the traditional ESG method.The synthetic examples demonstrate the effectiveness and superiority of the presented method.

Research on the optimization strategy of customers’electricity consumption based on big data

Current power systems face significant challenges in supporting large-scale access to new energy sources,and the potential of existing flexible resources needs to be fully explored from the power supply,grid,and customer perspectives.This paper proposes a multi-objective electricity consumption optimization strategy considering the correlation between equipment and electricity consumption.It constructs a multi-objective electricity consumption optimization model that considers the correlation between equipment and electricity consumption to maximize economy and comfort.The results show that the proposed method can accurately assess the potential for electricity consumption optimization and obtain an optimal multi-objective electricity consumption strategy based on customers’actual electricity consumption demand.

MESHLESS ANALYSIS FOR THREE-DIMENSIONAL ELASTICITY WITH SINGULAR HYBRID BOUNDARY NODE METHOD

The singular hybrid boundary node method （SHBNM） is proposed for solving three-dimensional problems in linear elasticity. The SHBNM represents a coupling between the hybrid displacement variational formulations and moving least squares （MLS） approximation. The main idea is to reduce the dimensionality of the former and keep the meshless advantage of the later. The rigid movement method was employed to solve the hyper-singular integrations. The ＇boundary layer effect＇, which is the main drawback of the original Hybrid BNM, was overcome by an adaptive integration scheme. The source points of the fundamental solution were arranged directly on the boundary. Thus the uncertain scale factor taken in the regular hybrid boundary node method （RHBNM） can be avoided. Numerical examples for some 3D elastic problems were given to show the characteristics. The computation results obtained by the present method are in excellent agreement with the analytical solution. The parameters that influence the performance of this method were studied through the numerical examples.

Fuzzy Fruit Fly Optimized Node Quality-Based Clustering Algorithm for Network Load Balancing

Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of the network results in packet loss and Delay(DL).For optimal performance,it is important to load balance between different gateways.As a result,a stable load balancing procedure is implemented,which selects gateways based on Fuzzy Logic(FL)and increases the efficiency of the network.In this case,since gate-ways are selected based on the number of nodes,the Energy Consumption(EC)was high.This paper presents a novel Node Quality-based Clustering Algo-rithm(NQCA)based on Fuzzy-Genetic for Cluster Head and Gateway Selection(FGCHGS).This algorithm combines NQCA with the Improved Weighted Clus-tering Algorithm(IWCA).The NQCA algorithm divides the network into clusters based upon node priority,transmission range,and neighbourfidelity.In addition,the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC,packet loss rate(PLR),etc.

Implementing the Node Based Smoothed Finite Element Method as User Element in Abaqus for Linear and Nonlinear Elasticity

In this paper,the node based smoothed-strain Abaqus user element(UEL)in the framework of finite element method is introduced.The basic idea behind of the node based smoothed finite element(NSFEM)is that finite element cells are divided into subcells and subcells construct the smoothing domain associated with each node of a finite element cell[Liu,Dai and Nguyen-Thoi(2007)].Therefore,the numerical integration is globally performed over smoothing domains.It is demonstrated that the proposed UEL retains all the advantages of the NSFEM,i.e.,upper bound solution,overly soft stiffness and free from locking in compressible and nearly-incompressible media.In this work,the constant strain triangular(CST)elements are used to construct node based smoothing domains,since any complex two dimensional domains can be discretized using CST elements.This additional challenge is successfully addressed in this paper.The efficacy and robustness of the proposed work is obtained by several benchmark problems in both linear and nonlinear elasticity.The developed UEL and the associated files can be downloaded from https://github.com/nsundar/NSFEM.

Geographic Drone-based Route Optimization Approach for Emergency Area Ad-Hoc Network

Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,localization,heterogeneous network,self-organization,and self-sufficient operation.In this background,the current study focuses on specially-designed communication link establishment for high connection stability of wireless mobile sensor networks,especially in disaster area network.Existing protocols focus on location-dependent communications and use networks based on typically-used Internet Protocol(IP)architecture.However,IP-based communications have a few limitations such as inefficient bandwidth utilization,high processing,less transfer speeds,and excessive memory intake.To overcome these challenges,the number of neighbors(Node Density)is minimized and high Mobility Nodes(Node Speed)are avoided.The proposed Geographic Drone Based Route Optimization(GDRO)method reduces the entire overhead to a considerable level in an efficient manner and significantly improves the overall performance by identifying the disaster region.This drone communicates with anchor node periodically and shares the information to it so as to introduce a drone-based disaster network in an area.Geographic routing is a promising approach to enhance the routing efficiency in MANET.This algorithm helps in reaching the anchor(target)node with the help of Geographical Graph-Based Mapping(GGM).Global Positioning System(GPS)is enabled on mobile network of the anchor node which regularly broadcasts its location information that helps in finding the location.In first step,the node searches for local and remote anticipated Expected Transmission Count(ETX),thereby calculating the estimated distance.Received Signal Strength Indicator(RSSI)results are stored in the local memory of the node.Then,the node calculates the least remote anticipated ETX,Link Loss Rate,and information to the new location.Freeway Heuristic algorithm improves the data speed,efficiency and determines the path and optimization problem.In comparison with other models,the proposed method yielded an efficient communication,increased the throughput,and reduced the end-to-end delay,energy consumption and packet loss performance in disaster area networks.

Exploiting Virtual Elasticity of Production Systems for Respecting OTD-Part 1: Post-Optimality Conditions for Ergodic Order Arrivals in Fixed Capacity Regimes

Respecting the on-time-delivery (OTD) for manufacturing orders is mandatory. This depends, however, on the probability distribution of incoming order rate. The case of non-equal distribution, such as aggregated arrivals, may compromise the observance of on-time supplies for some orders. The purpose of this paper is to evaluate the conditions of post-optimality for stochastic order rate governed production systems in order to observe OTD. Instead of a heuristic or a simulative exploration, a Cartesian-based approach is applied to developing the necessary and sufficient mathematical condition to solve the problem statement. The research result demonstrates that increasing speed of throughput reveals a latent capacity, which allows arrival orders above capacity limits to be backlog-buffered and rescheduled for OTD, exploiting the virtual manufacturing elasticity inherent to all production systems to increase OTD reliability of non JIT-based production systems.

Node deployment strategy optimization for wireless sensor network with mobile base station

The optimization of network performance in a movement-assisted data gathering scheme was studied by analyzing the energy consumption of wireless sensor network with node uniform distribution. A theoretically analytical method for avoiding energy hole was proposed. It is proved that if the densities of sensor nodes working at the same time are alternate between dormancy and work with non-uniform node distribution. The efficiency of network can increase by several times and the residual energy of network is nearly zero when the network lifetime ends.

Ordinal Outcome Modeling: The Application of the Adaptive Moment Estimation Optimizer to the Elastic Net Penalized Stereotype Logit

Penalized ordinal outcome models were developed to model high dimensional data with ordinal outcomes. One option is the penalized stereotype logit, which includes nonlinear combinations of parameter estimates. Optimization algorithms assuming linearity and function convexity were applied to fit this model. In this study the application of the adaptive moment estimation (Adam) optimizer, suited for nonlinear optimization, to the elastic net penalized stereotype logit model is proposed. The proposed model is compared to the L1 penalized ordinalgmifs stereotype model. Both methods were applied to simulated and real data, with non-Hodgkin lymphoma (NHL) cancer subtypes as the outcome, with results presented and discussed.

AModified Search and Rescue Optimization Based Node Localization Technique inWSN

Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,etc.Several issues that exist in the designing of WSN are node localization,coverage,energy efficiency,security,and so on.In spite of the issues,node localization is considered an important issue,which intends to calculate the coordinate points of unknown nodes with the assistance of anchors.The efficiency of the WSN can be considerably influenced by the node localization accuracy.Therefore,this paper presents a modified search and rescue optimization based node localization technique(MSRONLT)forWSN.The major aim of theMSRO-NLT technique is to determine the positioning of the unknown nodes in theWSN.Since the traditional search and rescue optimization(SRO)algorithm suffers from the local optima problemwith an increase in number of iterations,MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the technique.The application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO algorithm.In order to validate the effective node localization performance of the MSRO-NLT algorithm,a set of simulations were performed to highlight the supremacy of the presented model.A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.

A LOCKING-FREE ANISOTROPIC NONCONFORMING FINITE ELEMENT FOR PLANAR LINEAR ELASTICITY PROBLEM

The main aim of this article is to study the approximation of a locking-free anisotropic nonconforming finite element for the pure displacement boundary value problem of planar linear elasticity. The optimal error estimates are obtained by using some novel approaches and techniques. The method proposed in this article is robust in the sense that the convergence estimates in the energy and L^2-norms are independent-of the Lame parameter λ.

Self-Organized Optimization of Transport on Complex Networks

We propose a self-organized optimization mechanism to improve the transport capacity of complex gradient networks. We find that, regardless of network topology, the congestion pressure can be strongly reduced by the self-organized optimization mechanism. Furthermore, the random scale-free topology is more efficient to reduce congestion compared with the random Poisson topology under the optimization mechanism. The reason is that the optimization mechanism introduces the correlations between the gradient field and the local topology of the substrate network. Due to the correlations, the cutoff degree of the gradient network is strongly reduced and the number of the nodes exerting their maximal transport capacity consumedly increases. Our work presents evidence supporting the idea that scale-free networks can efficiently improve their transport capacity by self- organized mechanism under gradient-driven transport mode.

Endoscopic ultrasound elastography for evaluation of lymph nodes and pancreatic masses:A multicenter study

AIM:To evaluate the ability of endoscopic ultrasound(EUS) elastography to distinguish benign from malignant pancreatic masses and lymph nodes.METHODS:A multicenter study was conducted and included 222 patients who underwent EUS examination with assessment of a pancreatic mass(n=121) or lymph node(n=101).The classification as benign or malignant,based on the real time elastography pattern,was compared with the classif ication based on the B-mode EUS images and with the fi nal diagnosis obtained by EUS-guided fi ne needle aspiration(EUS-FNA) and/or by surgical pathology.An interobserver study was performed.RESULTS:The sensitivity and specificity of EUS elastography to differentiate benign from malignant pancreatic lesions are 92.3% and 80.0%,respectively,compared to 92.3% and 68.9%,respectively,for the conventional B-mode images.The sensitivity and specificity of EUS elastography to differentiate benign from malignant lymph nodes was 91.8% and 82.5%,respectively,compared to 78.6% and 50.0%,respectively,for the B-mode images.The kappa coefficient was 0.785 for the pancreatic masses and 0.657 for the lymph nodes.CONCLUSION:EUS elastography is superior compared to conventional B-mode imaging and appears to be able to distinguish benign from malignant pancreatic masses and lymph nodes with a high sensitivity,specificity and accuracy.It might be reserved as a second line examination to help characterise pancreatic masses after negative EUS-FNA and might increase the yield of EUS-FNA for lymph nodes.

A meshless Galerkin method with moving least square approximations for infinite elastic solids

Combining moving least square approximations and boundary integral equations, a meshless Galerkin method, which is the Galerkin boundary node method （GBNM）, for twoand three-dimensional infinite elastic solid mechanics problems with traction boundary conditions is discussed. In this numerical method, the resulting formulation inherits the symmetry and positive definiteness of variational problems, and boundary conditions can be applied directly and easily. A rigorous error analysis and convergence study for both displacement and stress is presented in Sobolev spaces. The capability of this method is illustrated and assessed by some numerical examples.

Optimization of Communication for Commanding and Controlling Systems

This paper mainly discussed some special requiremen ts for communication system and specific implement methods during the cause of cam paign cooperation in modern times. That system is the Advanced Field Tactical Da ta System which was developed to meet the requirements for controlling every fir e nodes, through which Army can achieve their campaign and tactical goals. The c ourse of dealing with communication message mainly include Emery’s situation rep orted, Message change (generally referred to taxis and how to choice best ways), Fire distribute, Evaluating for enemy’s casualty, cooperation among all fire no des, etc. The primary focus for this paper is how to deal with overall throughp ut, message latency, immunity from transmission errors, terrain effects on links and jamming. The system must balance all of these factors to provide within lit tle delay and that some messages are received with a specified time, finally we can make all fire nodes have best control in order to realize the aim of make th is system more optimized.