Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple dat...Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.展开更多
Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines A...Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.展开更多
Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as ...Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.展开更多
In Corley′s algorithm for all efficient spanning trees, final solutions include many spanning trees, which are not all efficient. In this paper, a new algorithm is presented, which corrects and modifies Corley′s alg...In Corley′s algorithm for all efficient spanning trees, final solutions include many spanning trees, which are not all efficient. In this paper, a new algorithm is presented, which corrects and modifies Corley′s algorithm. A necessary condition is developed for the subtree of an efficient spanning tree. According to the condition the new algorithm is established and its efficiency is proved.展开更多
Compared with vertical and horizontal wells, the solution and computation of transient pressure responses of slanted wells are more complex. Vertical and horizontal wells are both simplified cases of slanted wells at ...Compared with vertical and horizontal wells, the solution and computation of transient pressure responses of slanted wells are more complex. Vertical and horizontal wells are both simplified cases of slanted wells at particular inclination, so the model for slanted wells is more general and more complex than other models for vertical and horizontal wells. Many authors have studied unsteady-state flow of fluids in slanted wells and various solutions have been proposed. However, until now, few of the published results pertain to the computational efficiency. Whether in the time domain or in the Laplace domain, the computation of integration of complex functions is necessary in obtaining pressure responses of slanted wells, while the computation of the integration is complex and time-consuming. To obtain a perfect type curve the computation time is unacceptable even with an aid of high-speed computers. The purpose of this paper is to present an efficient algorithm to compute transient pressure distributions caused by slanted wells in reservoirs. Based on rigorous derivation, the transient pressure solution for slanted wells of any inclination angle is presented. Assuming an infinite-conductivity wellbore, the location of the equivalent-pressure point is determined. More importantly, according to the characteristics of the integrand in a transient pressure solution for slanted wells, the whole integral interval is partitioned into several small integral intervals, and then the method of variable substitution and the variable step-size piecewise numerical integration are employed. The amount of computation is significantly reduced and the computational efficiency is greatly improved. The algorithm proposed in this paper thoroughly solved the difficulty in the efficient and high-speed computation of transient pressure distribution of slanted wells with any inclination angle.展开更多
Energy efficiency(EE)is a critical design when taking into account circuit power consumption(CPC)in fifth-generation cellular networks.These problems arise because of the increasing number of antennas in massive multi...Energy efficiency(EE)is a critical design when taking into account circuit power consumption(CPC)in fifth-generation cellular networks.These problems arise because of the increasing number of antennas in massive multiple-input multiple-output(MIMO)systems,attributable to inter-cell interference for channel state information.Apart from that,a higher number of radio frequency(RF)chains at the base station and active users consume more power due to the processing activities in digital-to-analogue converters and power amplifiers.Therefore,antenna selection,user selection,optimal transmission power,and pilot reuse power are important aspects in improving energy efficiency in massive MIMO systems.This work aims to investigate joint antenna selection,optimal transmit power and joint user selection based on deriving the closed-form of the maximal EE,with complete knowledge of large-scale fading with maximum ratio transmission.It also accounts for channel estimation and eliminating pilot contamination as antennas M→∞.This formulates the optimization problem of joint optimal antenna selection,transmits power allocation and joint user selection to mitigate inter-cellinterference in downlink multi-cell massive MIMO systems under minimized reuse of pilot sequences based on a novel iterative low-complexity algorithm(LCA)for Newton’s methods and Lagrange multipliers.To analyze the precise power consumption,a novel power consumption scheme is proposed for each individual antenna,based on the transmit power amplifier and CPC.Simulation results demonstrate that the maximal EE was achieved using the iterative LCA based on reasonable maximum transmit power,in the case the noise power is less than the received power pilot.The maximum EE was achieved with the desired maximum transmit power threshold by minimizing pilot reuse,in the case the transmit power allocationρd=40 dBm,and the optimal EE=71.232 Mb/j.展开更多
In order to address typical problems due to the huge demand of oil for consumption in traditional internal combustion engines,a new more efficient combustion mode is proposed and studied in the framework of Computatio...In order to address typical problems due to the huge demand of oil for consumption in traditional internal combustion engines,a new more efficient combustion mode is proposed and studied in the framework of Computational Fluid Dynamics(CFD).Moreover,a Non-dominated Sorting Genetic Algorithm(NSGA-Ⅱ)is applied to optimize the related parameters,namely,the engine methanol ratio,the fuel injection time,the initial temperature,the Exhaust Gas Re-Circulation(EGR)rate,and the initial pressure.The so-called Conventional Diesel Combustion(CDC),Homogeneous Charge Compression Ignition(HCCI)and the Reactivity Controlled Compression Ignition(RCCI)combustion modes are compared.The results show that RCCI has a higher methanol ratio and an earlier injection timing with moderate EGR rate and higher initial pressure.The initial temperature increases as the methanol ratio increases.In comparison,CDC has the lowest hydrocarbon and CO emissions and the highest combustion efficiency.At different crankshaft rotation angles corresponding to 50%of the combustion amount(CA50),the combustion temperature and boundary layer temperature of HCCI change significantly,while those of RCCI undergo limited variations.At the same CA50,the exergy losses of HCCI and RCCI are lower than that of the CDC.On the basis of these findings,it can be concluded that the methanol/diesel RCCI engine can be used to obtain a clean and efficient combustion process,which should be regarded as a promising combustion mode.展开更多
As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphas...As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems(ITS).This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering(GOAVNET)algorithm,an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks(VANETs),leveraging the Grasshopper Optimization Algorithm(GOA)to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems(ITS).The proposed GOA-VNET employs an iterative and interactive optimization mechanism to dynamically adjust node positions and cluster configurations,ensuring robust adaptability to varying vehicular densities and transmission ranges.Key features of GOA-VNET include the utilization of attraction zone,repulsion zone,and comfort zone parameters,which collectively enhance clustering efficiency and minimize congestion within Regions of Interest(ROI).By managing cluster configurations and node densities effectively,GOA-VNET ensures balanced load distribution and seamless data transmission,even in scenarios with high vehicular densities and varying transmission ranges.Comparative evaluations against the Whale Optimization Algorithm(WOA)and Grey Wolf Optimization(GWO)demonstrate that GOA-VNET consistently outperforms these methods by achieving superior clustering efficiency,reducing the number of clusters by up to 10%in high-density scenarios,and improving data transmission reliability.Simulation results reveal that under a 100-600 m transmission range,GOA-VNET achieves an average reduction of 8%-15%in the number of clusters and maintains a 5%-10%improvement in packet delivery ratio(PDR)compared to baseline algorithms.Additionally,the algorithm incorporates a heat transfer-inspired load-balancing mechanism,ensuring equitable distribution of nodes among cluster leaders(CLs)and maintaining a stable network environment.These results validate GOA-VNET as a reliable and scalable solution for VANETs,with significant potential to support next-generation ITS.Future research could further enhance the algorithm by integrating multi-objective optimization techniques and exploring broader applications in complex traffic scenarios.展开更多
Nowadays,energy consumption which closely contacts with environmental impacts of manufacturing processes has been highly commented as a new productivity criterion.However,little attention has paid to the development o...Nowadays,energy consumption which closely contacts with environmental impacts of manufacturing processes has been highly commented as a new productivity criterion.However,little attention has paid to the development of process planning methods that take energy consumption into account.An energy-efficient process planning model that incorporates manufacturing time and energy consumption is proposed.For solving the problem,an improved genetic algorithm method is employed to explore the optimal solution.Finally,a case study for process planning is given.The experimental result generates interesting effort,and therefore allows improving the energy efficiency of manufacturing processes in process planning.展开更多
Due to the wide range of applications,Wireless Sensor Networks(WSN)are increased in day to day life and becomes popular.WSN has marked its importance in both practical and research domains.Energy is the most significa...Due to the wide range of applications,Wireless Sensor Networks(WSN)are increased in day to day life and becomes popular.WSN has marked its importance in both practical and research domains.Energy is the most significant resource,the important challenge in WSN is to extend its lifetime.The energy reduction is a key to extend the network’s lifetime.Clustering of sensor nodes is one of the well-known and proved methods for achieving scalable and energy conserving WSN.In this paper,an energy efficient protocol is proposed using metaheuristic Echo location-based BAT algorithm(ECHO-BAT).ECHO-BAT works in two stages.First Stage clusters the sensor nodes and identifies tentativeCluster Head(CH)along with the entropy value using BAT algorithm.The second stage aims to find the nodes if any,with high residual energy within each cluster.CHs will be replaced by the member node with high residual energy with an objective to choose the CH with high energy to prolong the network’s lifetime.The performance of the proposed work is compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Zoning Clustering Algorithm(PEZCA)and Chaotic Firefly Algorithm CH(CFACH)in terms of lifetime of network,death of first nodes,death of 125th node,death of the last node,network throughput and execution time.Simulation results show that ECHO-BAT outperforms the other methods in all the considered measures.The overall delivery ratio has also significantly optimized and improved by approximately 8%,proving the proposed approach to be an energy efficient WSN.展开更多
In this,communication world, the Network Function Virtualization concept is utilized for many businesses, small services to virtualize the network nodefunction and to build a block that may connect the chain, communic...In this,communication world, the Network Function Virtualization concept is utilized for many businesses, small services to virtualize the network nodefunction and to build a block that may connect the chain, communication services.Mainly, Virtualized Network Function Forwarding Graph (VNF-FG) has beenused to define the connection between the VNF and to give the best end-to-endservices. In the existing method, VNF mapping and backup VNF were proposedbut there was no profit and reliability improvement of the backup and mapping ofthe primary VNF. As a consequence, this paper offers a Hybrid Hexagon-CostEfficient algorithm for determining the best VNF among multiple VNF and backing up the best VNF, lowering backup costs while increasing dependability. TheVNF is chosen based on the highest cost-aware important measure (CIM) rate,which is used to assess the relevance of the VNF forwarding graph.To achieveoptimal cost-efficiency, VNF with the maximum CIM is selected. After the selection process, updating is processed by three steps which include one backup VNFfrom one SFC, two backup VNF from one Service Function Chain (SFC),and twobackup VNF from different SFC. Finally, this proposed method is compared withCERA, MinCost, MaxRbyInr based on backup cost, number of used PN nodes,SFC request utility, and latency. The simulation result shows that the proposedmethod cuts down the backup cost and computation time by 57% and 45% compared with the CER scheme and improves the cost-efficiency. As a result, this proposed system achieves less backup cost, high reliability, and low timeconsumption which can improve the Virtualized Network Function operation.展开更多
A finite difference method for computing the axisymmetric, transonic flows over a nacelle is presented in this paper. By use of the conservative full-potential equation, body-fitted grid, and the exact boundary condit...A finite difference method for computing the axisymmetric, transonic flows over a nacelle is presented in this paper. By use of the conservative full-potential equation, body-fitted grid, and the exact boundary conditions, a new AF scheme is constructed according to the criterion of optimum convergence. The proposed scheme has been applied to transonic nacelle flow problems. Computation for several nacelles shows the rapid convergence of this scheme and excellent agreement with the experimental results.展开更多
Collision detection mechanisms in Wireless Sensor Networks (WSNs) have largely been revolving around direct demodulation and decoding of received packets and deciding on a collision based on some form of a frame error...Collision detection mechanisms in Wireless Sensor Networks (WSNs) have largely been revolving around direct demodulation and decoding of received packets and deciding on a collision based on some form of a frame error detection mechanism, such as a CRC check. The obvious drawback of full detection of a received packet is the need to expend a significant amount of energy and processing complexity in order to fully decode a packet, only to discover the packet is illegible due to a collision. In this paper, we propose a suite of novel, yet simple and power-efficient algorithms to detect a collision without the need for full-decoding of the received packet. Our novel algorithms aim at detecting collision through fast examination of the signal statistics of a short snippet of the received packet via a relatively small number of computations over a small number of received IQ samples. Hence, the proposed algorithms operate directly at the output of the receiver's analog-to-digital converter and eliminate the need to pass the signal through the entire. In addition, we present a complexity and power-saving comparison between our novel algorithms and conventional full-decoding (for select coding schemes) to demonstrate the significant power and complexity saving advantage of our algorithms.展开更多
By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite co...By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [-1,1].Some measures in the optimization algorithm,such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline,were taken to ensure its speediness and veracity in seeking the optimization process.The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision.Furthermore,the average truncated generations,the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally.It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.展开更多
Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise...Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.展开更多
The path planning problem of complex wild environment with multiple elements still poses challenges.This paper designs an algorithm that integrates global and local planning to apply to the wild environmental path pla...The path planning problem of complex wild environment with multiple elements still poses challenges.This paper designs an algorithm that integrates global and local planning to apply to the wild environmental path planning.The modeling process of wild environment map is designed.Three optimization strategies are designed to improve the A-Star in overcoming the problems of touching the edge of obstacles,redundant nodes and twisting paths.A new weighted cost function is designed to achieve different planning modes.Furthermore,the improved dynamic window approach(DWA)is designed to avoid local optimality and improve time efficiency compared to traditional DWA.For the necessary path re-planning of wild environment,the improved A-Star is integrated with the improved DWA to solve re-planning problem of unknown and moving obstacles in wild environment with multiple elements.The improved fusion algorithm effectively solves problems and consumes less time,and the simulation results verify the effectiveness of improved algorithms above.展开更多
The issue of finding available parking spaces and mitigating conges-tion during parking is a persistent challenge for numerous car owners in urban areas.In this paper,we propose a novel method based on the A-star algo...The issue of finding available parking spaces and mitigating conges-tion during parking is a persistent challenge for numerous car owners in urban areas.In this paper,we propose a novel method based on the A-star algorithm to calculate the optimal parking path to address this issue.We integrate a road impedance function into the conventional A-star algorithm to compute path duration and adopt a fusion function composed of path length and duration as the weight matrix for the A-star algorithm to achieve optimal path planning.Furthermore,we conduct simulations using parking lot modeling to validate the effectiveness of our approach,which can provide car drivers with a reliable optimal parking navigation route,reduce their parking costs,and enhance their parking experience.展开更多
This paper mainly studied the problem of energy conserving in wireless sensor networks for target tracking in defensing combats. Firstly, the structures of wireless sensor nodes and networks were illustrated;Secondly,...This paper mainly studied the problem of energy conserving in wireless sensor networks for target tracking in defensing combats. Firstly, the structures of wireless sensor nodes and networks were illustrated;Secondly, the analysis of existing energy consuming in the sensing layer and its calculation method were provided to build the energy conserving objective function;What’s more, the other two indicators in target tracking, including target detection probability and tracking accuracy, were combined to be regarded as the constraints of the energy conserving objective function. Fourthly, the three energy conserving approaches, containing optimizing the management scheme, prolonging the time interval between two adjacent observations, and transmitting the observations selectively, were introduced;In addition, the improved lion algorithm combined with the Logistic chaos sequence was proposed to obtain sensor management schemes. Finally, simulations had been made to prove the effectiveness of the proposed methods and algorithm.展开更多
One of the major constraints of wireless sensor networks is limited energy available to sensor nodes because of the small size of the batteries they use as source of power. Clustering is one of the routing techniques ...One of the major constraints of wireless sensor networks is limited energy available to sensor nodes because of the small size of the batteries they use as source of power. Clustering is one of the routing techniques that have been using to minimize sensor nodes’ energy consumption during operation. In this paper, A Novel Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks (ANCAEE) has been proposed. The algorithm achieves good performance in terms of minimizing energy consumption during data transmission and energy consumptions are distributed uniformly among all nodes. ANCAEE uses a new method of clusters formation and election of cluster heads. The algorithm ensures that a node transmits its data to the cluster head with a single hop transmission and cluster heads forward their data to the base station with multi-hop transmissions. Simulation results show that our approach consumes less energy and effectively extends network utilization.展开更多
文摘Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.
文摘Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.
基金funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region:No.22D01B148Bidding Topics for the Center for Integration of Education and Production and Development of New Business in 2024:No.2024-KYJD05+1 种基金Basic Scientific Research Business Fee Project of Colleges and Universities in Autonomous Region:No.XJEDU2025P126Xinjiang College of Science&Technology School-level Scientific Research Fund Project:No.2024-KYTD01.
文摘Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.
文摘In Corley′s algorithm for all efficient spanning trees, final solutions include many spanning trees, which are not all efficient. In this paper, a new algorithm is presented, which corrects and modifies Corley′s algorithm. A necessary condition is developed for the subtree of an efficient spanning tree. According to the condition the new algorithm is established and its efficiency is proved.
基金financial support from the special fund of China’s central government for the development of local colleges and universities―the project of national first-level discipline in Oil and Gas Engineering, the National Science Fund for Distinguished Young Scholars of China (Grant No. 51125019)the National Program on Key fundamental Research Project (973 Program, Grant No. 2011CB201005)
文摘Compared with vertical and horizontal wells, the solution and computation of transient pressure responses of slanted wells are more complex. Vertical and horizontal wells are both simplified cases of slanted wells at particular inclination, so the model for slanted wells is more general and more complex than other models for vertical and horizontal wells. Many authors have studied unsteady-state flow of fluids in slanted wells and various solutions have been proposed. However, until now, few of the published results pertain to the computational efficiency. Whether in the time domain or in the Laplace domain, the computation of integration of complex functions is necessary in obtaining pressure responses of slanted wells, while the computation of the integration is complex and time-consuming. To obtain a perfect type curve the computation time is unacceptable even with an aid of high-speed computers. The purpose of this paper is to present an efficient algorithm to compute transient pressure distributions caused by slanted wells in reservoirs. Based on rigorous derivation, the transient pressure solution for slanted wells of any inclination angle is presented. Assuming an infinite-conductivity wellbore, the location of the equivalent-pressure point is determined. More importantly, according to the characteristics of the integrand in a transient pressure solution for slanted wells, the whole integral interval is partitioned into several small integral intervals, and then the method of variable substitution and the variable step-size piecewise numerical integration are employed. The amount of computation is significantly reduced and the computational efficiency is greatly improved. The algorithm proposed in this paper thoroughly solved the difficulty in the efficient and high-speed computation of transient pressure distribution of slanted wells with any inclination angle.
基金support under the Multi-Disciplinary Research(MDR)Grant(H470)the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme(FRGS/1/2019/TK04/UTHM/02/8).
文摘Energy efficiency(EE)is a critical design when taking into account circuit power consumption(CPC)in fifth-generation cellular networks.These problems arise because of the increasing number of antennas in massive multiple-input multiple-output(MIMO)systems,attributable to inter-cell interference for channel state information.Apart from that,a higher number of radio frequency(RF)chains at the base station and active users consume more power due to the processing activities in digital-to-analogue converters and power amplifiers.Therefore,antenna selection,user selection,optimal transmission power,and pilot reuse power are important aspects in improving energy efficiency in massive MIMO systems.This work aims to investigate joint antenna selection,optimal transmit power and joint user selection based on deriving the closed-form of the maximal EE,with complete knowledge of large-scale fading with maximum ratio transmission.It also accounts for channel estimation and eliminating pilot contamination as antennas M→∞.This formulates the optimization problem of joint optimal antenna selection,transmits power allocation and joint user selection to mitigate inter-cellinterference in downlink multi-cell massive MIMO systems under minimized reuse of pilot sequences based on a novel iterative low-complexity algorithm(LCA)for Newton’s methods and Lagrange multipliers.To analyze the precise power consumption,a novel power consumption scheme is proposed for each individual antenna,based on the transmit power amplifier and CPC.Simulation results demonstrate that the maximal EE was achieved using the iterative LCA based on reasonable maximum transmit power,in the case the noise power is less than the received power pilot.The maximum EE was achieved with the desired maximum transmit power threshold by minimizing pilot reuse,in the case the transmit power allocationρd=40 dBm,and the optimal EE=71.232 Mb/j.
文摘In order to address typical problems due to the huge demand of oil for consumption in traditional internal combustion engines,a new more efficient combustion mode is proposed and studied in the framework of Computational Fluid Dynamics(CFD).Moreover,a Non-dominated Sorting Genetic Algorithm(NSGA-Ⅱ)is applied to optimize the related parameters,namely,the engine methanol ratio,the fuel injection time,the initial temperature,the Exhaust Gas Re-Circulation(EGR)rate,and the initial pressure.The so-called Conventional Diesel Combustion(CDC),Homogeneous Charge Compression Ignition(HCCI)and the Reactivity Controlled Compression Ignition(RCCI)combustion modes are compared.The results show that RCCI has a higher methanol ratio and an earlier injection timing with moderate EGR rate and higher initial pressure.The initial temperature increases as the methanol ratio increases.In comparison,CDC has the lowest hydrocarbon and CO emissions and the highest combustion efficiency.At different crankshaft rotation angles corresponding to 50%of the combustion amount(CA50),the combustion temperature and boundary layer temperature of HCCI change significantly,while those of RCCI undergo limited variations.At the same CA50,the exergy losses of HCCI and RCCI are lower than that of the CDC.On the basis of these findings,it can be concluded that the methanol/diesel RCCI engine can be used to obtain a clean and efficient combustion process,which should be regarded as a promising combustion mode.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2024-00337489Development of Data Drift Management Technology to Overcome Performance Degradation of AI Analysis Models).
文摘As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems(ITS).This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering(GOAVNET)algorithm,an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks(VANETs),leveraging the Grasshopper Optimization Algorithm(GOA)to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems(ITS).The proposed GOA-VNET employs an iterative and interactive optimization mechanism to dynamically adjust node positions and cluster configurations,ensuring robust adaptability to varying vehicular densities and transmission ranges.Key features of GOA-VNET include the utilization of attraction zone,repulsion zone,and comfort zone parameters,which collectively enhance clustering efficiency and minimize congestion within Regions of Interest(ROI).By managing cluster configurations and node densities effectively,GOA-VNET ensures balanced load distribution and seamless data transmission,even in scenarios with high vehicular densities and varying transmission ranges.Comparative evaluations against the Whale Optimization Algorithm(WOA)and Grey Wolf Optimization(GWO)demonstrate that GOA-VNET consistently outperforms these methods by achieving superior clustering efficiency,reducing the number of clusters by up to 10%in high-density scenarios,and improving data transmission reliability.Simulation results reveal that under a 100-600 m transmission range,GOA-VNET achieves an average reduction of 8%-15%in the number of clusters and maintains a 5%-10%improvement in packet delivery ratio(PDR)compared to baseline algorithms.Additionally,the algorithm incorporates a heat transfer-inspired load-balancing mechanism,ensuring equitable distribution of nodes among cluster leaders(CLs)and maintaining a stable network environment.These results validate GOA-VNET as a reliable and scalable solution for VANETs,with significant potential to support next-generation ITS.Future research could further enhance the algorithm by integrating multi-objective optimization techniques and exploring broader applications in complex traffic scenarios.
基金supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme(No.294931)the National Science Foundation of China (No.51175262)+1 种基金Jiangsu Province Science Foundation for Excellent Youths(No.BK2012032)Jiangsu Province Industry-Academy-Research Grant(No.BY201220116)
文摘Nowadays,energy consumption which closely contacts with environmental impacts of manufacturing processes has been highly commented as a new productivity criterion.However,little attention has paid to the development of process planning methods that take energy consumption into account.An energy-efficient process planning model that incorporates manufacturing time and energy consumption is proposed.For solving the problem,an improved genetic algorithm method is employed to explore the optimal solution.Finally,a case study for process planning is given.The experimental result generates interesting effort,and therefore allows improving the energy efficiency of manufacturing processes in process planning.
基金This work was supported by Taif University Researchers Supporting Program(project number:TURSP-2020/195),Taif University,Saudi Arabia.
文摘Due to the wide range of applications,Wireless Sensor Networks(WSN)are increased in day to day life and becomes popular.WSN has marked its importance in both practical and research domains.Energy is the most significant resource,the important challenge in WSN is to extend its lifetime.The energy reduction is a key to extend the network’s lifetime.Clustering of sensor nodes is one of the well-known and proved methods for achieving scalable and energy conserving WSN.In this paper,an energy efficient protocol is proposed using metaheuristic Echo location-based BAT algorithm(ECHO-BAT).ECHO-BAT works in two stages.First Stage clusters the sensor nodes and identifies tentativeCluster Head(CH)along with the entropy value using BAT algorithm.The second stage aims to find the nodes if any,with high residual energy within each cluster.CHs will be replaced by the member node with high residual energy with an objective to choose the CH with high energy to prolong the network’s lifetime.The performance of the proposed work is compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Zoning Clustering Algorithm(PEZCA)and Chaotic Firefly Algorithm CH(CFACH)in terms of lifetime of network,death of first nodes,death of 125th node,death of the last node,network throughput and execution time.Simulation results show that ECHO-BAT outperforms the other methods in all the considered measures.The overall delivery ratio has also significantly optimized and improved by approximately 8%,proving the proposed approach to be an energy efficient WSN.
文摘In this,communication world, the Network Function Virtualization concept is utilized for many businesses, small services to virtualize the network nodefunction and to build a block that may connect the chain, communication services.Mainly, Virtualized Network Function Forwarding Graph (VNF-FG) has beenused to define the connection between the VNF and to give the best end-to-endservices. In the existing method, VNF mapping and backup VNF were proposedbut there was no profit and reliability improvement of the backup and mapping ofthe primary VNF. As a consequence, this paper offers a Hybrid Hexagon-CostEfficient algorithm for determining the best VNF among multiple VNF and backing up the best VNF, lowering backup costs while increasing dependability. TheVNF is chosen based on the highest cost-aware important measure (CIM) rate,which is used to assess the relevance of the VNF forwarding graph.To achieveoptimal cost-efficiency, VNF with the maximum CIM is selected. After the selection process, updating is processed by three steps which include one backup VNFfrom one SFC, two backup VNF from one Service Function Chain (SFC),and twobackup VNF from different SFC. Finally, this proposed method is compared withCERA, MinCost, MaxRbyInr based on backup cost, number of used PN nodes,SFC request utility, and latency. The simulation result shows that the proposedmethod cuts down the backup cost and computation time by 57% and 45% compared with the CER scheme and improves the cost-efficiency. As a result, this proposed system achieves less backup cost, high reliability, and low timeconsumption which can improve the Virtualized Network Function operation.
文摘A finite difference method for computing the axisymmetric, transonic flows over a nacelle is presented in this paper. By use of the conservative full-potential equation, body-fitted grid, and the exact boundary conditions, a new AF scheme is constructed according to the criterion of optimum convergence. The proposed scheme has been applied to transonic nacelle flow problems. Computation for several nacelles shows the rapid convergence of this scheme and excellent agreement with the experimental results.
文摘Collision detection mechanisms in Wireless Sensor Networks (WSNs) have largely been revolving around direct demodulation and decoding of received packets and deciding on a collision based on some form of a frame error detection mechanism, such as a CRC check. The obvious drawback of full detection of a received packet is the need to expend a significant amount of energy and processing complexity in order to fully decode a packet, only to discover the packet is illegible due to a collision. In this paper, we propose a suite of novel, yet simple and power-efficient algorithms to detect a collision without the need for full-decoding of the received packet. Our novel algorithms aim at detecting collision through fast examination of the signal statistics of a short snippet of the received packet via a relatively small number of computations over a small number of received IQ samples. Hence, the proposed algorithms operate directly at the output of the receiver's analog-to-digital converter and eliminate the need to pass the signal through the entire. In addition, we present a complexity and power-saving comparison between our novel algorithms and conventional full-decoding (for select coding schemes) to demonstrate the significant power and complexity saving advantage of our algorithms.
基金Project(60874114) supported by the National Natural Science Foundation of China
文摘By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [-1,1].Some measures in the optimization algorithm,such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline,were taken to ensure its speediness and veracity in seeking the optimization process.The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision.Furthermore,the average truncated generations,the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally.It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.
文摘Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.
基金Industry-University-Research Cooperation Fund Project of the Eighth Research Institute of China Aerospace Science and Technology Corporation(No.USCAST2022-11)。
文摘The path planning problem of complex wild environment with multiple elements still poses challenges.This paper designs an algorithm that integrates global and local planning to apply to the wild environmental path planning.The modeling process of wild environment map is designed.Three optimization strategies are designed to improve the A-Star in overcoming the problems of touching the edge of obstacles,redundant nodes and twisting paths.A new weighted cost function is designed to achieve different planning modes.Furthermore,the improved dynamic window approach(DWA)is designed to avoid local optimality and improve time efficiency compared to traditional DWA.For the necessary path re-planning of wild environment,the improved A-Star is integrated with the improved DWA to solve re-planning problem of unknown and moving obstacles in wild environment with multiple elements.The improved fusion algorithm effectively solves problems and consumes less time,and the simulation results verify the effectiveness of improved algorithms above.
文摘The issue of finding available parking spaces and mitigating conges-tion during parking is a persistent challenge for numerous car owners in urban areas.In this paper,we propose a novel method based on the A-star algorithm to calculate the optimal parking path to address this issue.We integrate a road impedance function into the conventional A-star algorithm to compute path duration and adopt a fusion function composed of path length and duration as the weight matrix for the A-star algorithm to achieve optimal path planning.Furthermore,we conduct simulations using parking lot modeling to validate the effectiveness of our approach,which can provide car drivers with a reliable optimal parking navigation route,reduce their parking costs,and enhance their parking experience.
基金funded by (Defense Pre-Research Fund Project of China), grant number 012015012600A2203NSFC (Natural Science Foundation of China), grant number 61573374。
文摘This paper mainly studied the problem of energy conserving in wireless sensor networks for target tracking in defensing combats. Firstly, the structures of wireless sensor nodes and networks were illustrated;Secondly, the analysis of existing energy consuming in the sensing layer and its calculation method were provided to build the energy conserving objective function;What’s more, the other two indicators in target tracking, including target detection probability and tracking accuracy, were combined to be regarded as the constraints of the energy conserving objective function. Fourthly, the three energy conserving approaches, containing optimizing the management scheme, prolonging the time interval between two adjacent observations, and transmitting the observations selectively, were introduced;In addition, the improved lion algorithm combined with the Logistic chaos sequence was proposed to obtain sensor management schemes. Finally, simulations had been made to prove the effectiveness of the proposed methods and algorithm.
文摘One of the major constraints of wireless sensor networks is limited energy available to sensor nodes because of the small size of the batteries they use as source of power. Clustering is one of the routing techniques that have been using to minimize sensor nodes’ energy consumption during operation. In this paper, A Novel Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks (ANCAEE) has been proposed. The algorithm achieves good performance in terms of minimizing energy consumption during data transmission and energy consumptions are distributed uniformly among all nodes. ANCAEE uses a new method of clusters formation and election of cluster heads. The algorithm ensures that a node transmits its data to the cluster head with a single hop transmission and cluster heads forward their data to the base station with multi-hop transmissions. Simulation results show that our approach consumes less energy and effectively extends network utilization.
