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NPO-BAND:Number and placement optimization for backhaul-aware UAV network deployment 认领 引用
1
作者 Sining Yang Bo Zhang Jinshu Su 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期630-639,共10页
In emergency communication scenarios,exploiting Unmanned Aerial Vehicles(UAVs)as relays to provide wireless communication services for ground users has emerged as a promising application.A key challenge in this resour... In emergency communication scenarios,exploiting Unmanned Aerial Vehicles(UAVs)as relays to provide wireless communication services for ground users has emerged as a promising application.A key challenge in this resource-constrained application is deploying the minimum number of UAVs to form an aerial backhaul network to ensure coverage,which composes the Number and Placement Optimization for the Backhaul-Aware Network Deployment(NPO-BAND)problem.In this paper,we first formulate the NPO-BAND problem based on the geometric disk coverage model.Then,we propose a low-complexity heuristic method to solve this NP-hard problem.The proposed method contains a Very Important Point-Choosing(VIPC)strategy and a Backhaul-Aware Local Coverage(BALC)algorithm.Specifically,the VIPC strategy weighs up the backhaul connectivity constraint and the ground user coverage to choose the VIP,while the BALC algorithm solves the extended 1-center problem to determine the deployment location of each UAV.Simulation results show that the proposed method can effectively reduce the number of deployed UAVs,saving up to 25%-50%of that compared to existing methods across varying numbers and area sizes in clustered distribution patterns of ground users. 展开更多
关键词 Unmanned aerial vehicles Multi-UAV network Backhaul-aware deployment Number optimization
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Bi-objective optimization of orbital transfer vehicle based launch and deployment process for LEO constellation 认领 引用
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作者 Peng HAN Chao HUANG +1 位作者 Chuanjiang LI Yanning GUO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期578-597,共20页
This paper addresses the optimization of Launch and Deployment(L&D)strategies for Low Earth Orbit(LEO)satellite constellations using Orbital Transfer Vehicles(OTVs).The problem is formulated as a bi-objective comb... This paper addresses the optimization of Launch and Deployment(L&D)strategies for Low Earth Orbit(LEO)satellite constellations using Orbital Transfer Vehicles(OTVs).The problem is formulated as a bi-objective combinatorial optimization model that simultaneously minimizes the number of launches and the average fuel consumption,thereby reducing launch costs and improving mission reliability.To enhance solution diversity,the∊-constraint method is employed to decompose the original problem into a series of constrained single-objective subproblems.A Cluster and Nearest Sorting initialized Genetic Algorithm with Local Search(CNS-LSGA)is developed to efficiently solve these subproblems.The CNS initialization mechanism is specifically designed to avoid infeasible solutions caused by limited OTV fuel and the high fuel requirements of LEO orbital maneuvers,while two local search operators are incorporated to improve local exploitation.Comparative evaluations across three LEO constellation architectures demonstrate that the proposed framework is both effective and robust,and can consistently obtain greater solution diversity and higher-quality results than the established population-based multi-objective algorithms. 展开更多
关键词 Launch and deployment Bi-objective optimization Orbital transfer vehicle Low Earth Orbit(LEO)constellation e-constraint method Genetic algorithm
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Effects of ground-based camera deployment parameters on SfM-Based 3D reconstruction of deformation features on stepped slopes 认领 引用
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作者 SHU Yunlong NIE Wen +4 位作者 ZHU Tianqiang MEHMOOD Mudassir YU Wen LIU Aixing KONG Qiuping 《Journal of Mountain Science》 SCIE CSCD 2026年第5期1941-1955,共15页
Stepped slopes are common in mountainous regions and are characterized by pronounced benches and risers,which generate strong occlusions during ground-based image acquisition and limit the accuracy of Structure-from-M... Stepped slopes are common in mountainous regions and are characterized by pronounced benches and risers,which generate strong occlusions during ground-based image acquisition and limit the accuracy of Structure-from-Motion(SfM)-based 3D reconstruction of surface deformation features.Despite the increasing application of SfM in slope monitoring,practical guidelines for optimizing ground-based camera deployment on stepped terrains remain limited.To address this gap,this study investigates the effects of ground-based camera deployment parameters on SfM reconstruction accuracy through geometrically scaled physical modeling and field validation.A 1:10 physical model of a rainfall-induced stepped slope was constructed to reproduce representative surface cracking patterns,with rainfall applied solely to induce deformation rather than treated as an experimental variable.Camera height,layout type,inter-camera angular interval,and camera number were systematically varied under controlled single-elevation configurations.Reconstruction accuracy was evaluated using the mean relative error between manually measured and SfM-derived crack widths.The results show that deployment geometry strongly governs reconstruction performance.A camera height of approximately one-third of slope height provides a favorable balance between spatial resolution and coverage.Compared with conventional horizontal layouts,fan-shaped configurations significantly reduce occlusion effects and improve crack-width measurement accuracy.Increasing the number of cameras generally reduces reconstruction error,but the improvement becomes marginal beyond four to five cameras under single-elevation deployment conditions.Under the optimal configuration,the physical model achieved a minimum mean relative error of 4.3%,while the field application at a prototype stepped slope yielded a mean relative error of 12.2%,which is acceptable for routine engineering monitoring.These findings provide practical and cost-effective guidance for ground-based SfM monitoring of deformation on stepped slopes in mountainous terrain. 展开更多
关键词 SfM Ground-based photogrammetry Stepped slopes Camera deployment parameters Crack-width measurement
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Quantum-Inspired Optimization Algorithm for 3D Multi-Objective Base-Station Deployment in Next-Generation 5G/6G Wireless Network 认领 引用
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作者 Yao-Hsin Chou Cheng-Yen Hua +1 位作者 Ru-Wei Tseng Shu-Yu Kuo 《Computers, Materials & Continua》 SCIE EI 2026年第5期981-996,共16页
The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)w... The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)while maintaining cost-efficiency and sustainable deployment.Traditional strategies struggle with complex 3D propagation,building penetration loss,and the balance between coverage and infrastructure cost.To address this challenge,this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate(GQTS-QNG)framework for 3D base-station deployment optimization.The problem is formulated as a multi-objective model that simultaneously maximizes coverage and minimizes deployment cost.A binary-to-decimal encodingmechanism is designed to represent discrete placement coordinates and base station types,leveraging a quantum-inspired method to efficiently search and refine solutions within challenging combinatorial environments.Global-best guidance and tabu memory are integrated to strengthen convergence stability and avoid revisiting previously explored solutions.Simulation results across user densities ranging from 1000 to 10,000 show that GQTS-QNG consistently finds deployment configurations achieving full coverage while reducing deployment cost compared with the state-of-the-art algorithms under equal iteration times.Additionally,our method generates welldistributed and structured Pareto fronts,offering diverse planning options that allow operators to flexibly balance cost and performance requirements.These findings demonstrate that GQTS-QNG is a scalable and efficient algorithm for sustainable 3D cellular network deployment in B5G/6G urban scenarios. 展开更多
关键词 3D network deployment quantum-inspired optimization B5G/6G multi-objective optimization coverage deployment cost urban wireless planning
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Energy-Efficient Network Deployment and Resource Allocation in IAB Aerial-Terrestrial Network 认领 引用
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作者 Wang Wei Sheng Min +2 位作者 Chen Xuhui Liu Junyu Li Jiandong 《China Communications》 SCIE EI CSCD 2026年第3期330-347,共18页
This paper aims to improve energy efficiency(EE)of the integrated access and backhaul(IAB)aerial-terrestrial network,facilitating rapid and adjustable network infrastructure deployment.This is challenging,as interfere... This paper aims to improve energy efficiency(EE)of the integrated access and backhaul(IAB)aerial-terrestrial network,facilitating rapid and adjustable network infrastructure deployment.This is challenging,as interference generated by backhaul and access links degrades network throughput,and power imbalance between these links increases overall energy consumption.To this end,we jointly optimize aerial base station(ABS)deployment,user association,and downlink power allocation for both terrestrial base station and ABSs to maximize network EE.Specifically,using fractional programming,the EE maximization problem is transformed into a subtractive-form parametric problem,and then decomposed into ABS deployment and resource allocation subproblems.A hybrid algorithm combining particle swarm optimization and simulated annealing is proposed to solve the ABS deployment subproblem,determining ABS spatial configurations and updating power allocation given fixed user association.Meanwhile,a dynamic power allocation in response to network load is designed to solve the resource allocation subproblem.Furthermore,considering the quality of service requirements of ground users and the transmit power constraints of base stations,a joint EE optimization algorithm is proposed to enhance the network EE.Simulation results validate the effectiveness of the proposed methods in improving network EE,especially in scenarios involving more deployed ABSs. 展开更多
关键词 aerial-terrestrial network energy efficiency integrated access and backhaul(IAB) network deployment resource allocation
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Optimization strategy for batch launch deployment of large-scale low earth orbit constellations based on multimodal transportation network model 认领 引用
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作者 Junru LIN Tiantian ZHANG Min HU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期518-538,共21页
Large-scale Low Earth Orbit(LEO)constellations have become a focal point due to their capability to provide round-the-clock high-fidelity information services.However,their efficient and economical batch deployment fa... Large-scale Low Earth Orbit(LEO)constellations have become a focal point due to their capability to provide round-the-clock high-fidelity information services.However,their efficient and economical batch deployment faces severe challenges stemming from growing demands and multiple constraints,with existing methods struggling to effectively address the computational complexity in large-scale scenarios.Addressing this pressing need,this study proposes an innovative deployment optimization framework.Its core lies in constructing a novel partial time-expanded network that significantly reduces(over 90%)redundant links through feasibility pruning and hierarchical aggregation strategies,effectively tackling the exponential growth of constraints inherent in traditional models,and proposing an efficient hybrid algorithm integrating column generation and A*search,which,combined with a subproblem filter,significantly enhances the solution efficiency and scalability for large-scale problems.The framework supports dual-channel,multi-configuration rocket strategies and achieves flexible deployment under multiple mission triggers through weighted optimization.The research demonstrates that the proposed method can effectively reduce deployment costs,improve optimization efficiency,and provide reliable decision support for large-scale constellation deployment. 展开更多
关键词 Column generation Constellation deployment Mixed integer programming:Satellite constellation Space logistics network model
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A Collaborative Deployment Method of UAV Hangar Siting for Forest Inspection 认领 引用
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作者 QIAN Long LIU Jixin +2 位作者 JIANG Hao ZENG Weili YANG Zhao 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2026年第3期371-385,共15页
The deployment of unmanned aerial vehicle(UAV)hangars is critical to the efficiency of forest inspections,significantly influencing both infrastructure construction costs and operational expenses.Existing research on ... The deployment of unmanned aerial vehicle(UAV)hangars is critical to the efficiency of forest inspections,significantly influencing both infrastructure construction costs and operational expenses.Existing research on hangar selection often overlooks the complex constraints posed by forest environments,such as topographical variability,power limitations,and coverage demands.To tackle these challenges,this paper presents a multiobjective optimization approach for UAV hangar selection in forest environments,aiming to reduce construction costs while maximizing coverage under complex topographical constraints.The process begins with the preliminary selection of candidate hangars,utilizing geographic data such as the digital elevation model(DEM),meteorological data,and power/signal coverage.A multi-criteria decision analysis(MCDA)method evaluates and scores candidates based on rigid and flexible criteria,including topographical suitability,wind speed,and power supply availability.A multi-objective optimization model is then developed to optimize the layout of hangars,incorporating critical constraints such as topographical characteristics,UAV power limits,and coverage redundancy.To solve this optimization problem,the non-dominated sorting genetic algorithmⅡ(NSGA-Ⅱ)is applied.Experimental results demonstrate that the proposed method outperforms traditional approaches,such as the greedy algorithm and the single-objective genetic algorithm.Specifically,the NSGA-Ⅱmethod reduces the number of hangars by 8.3%,and increases the coverage by 1.6%.It also significantly accelerates the convergence,demonstrating superior performance and efficiency.This methodology provides a comprehensive solution for UAV deployment in forest inspections and can be adapted to other complex topography. 展开更多
关键词 forest inspection unmanned aerial vehicle(UAV)hangar deployment multi-criteria decision analysis(MCDA) topographical constraints multi-objective optimization
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Amplitude-Ensemble Quantum-Inspired Tabu Search Algorithm forWireless Sensor Network Deployment 认领 引用
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作者 Kuo-Chun Tseng I-Chia Chen Yu-Chieh Cho 《Computers, Materials & Continua》 SCIE EI 2026年第9期2413-2448,共36页
Wireless Sensor Networks(WSNs)are important infrastructure for smart-city applications,such as environmental monitoring,public safety,and smart transportation.However,finding effective sensor locations is an NP-hard p... Wireless Sensor Networks(WSNs)are important infrastructure for smart-city applications,such as environmental monitoring,public safety,and smart transportation.However,finding effective sensor locations is an NP-hard problem because a deployment must satisfy sensing coverage and communication connectivity while minimizing the number of sensors.Following the basic framework of a previous study,this study replaces the original optimization algorithmwith the Amplitude-Ensemble Quantum-inspired Tabu Search(AEQTS)algorithmand retains the same entanglement-like initialization strategy,resulting in the proposed AEQTSwE(AEQTS with Entanglement)framework for theWSNdeployment problem.AEQTSwE uses a quantum-inspired search mechanism and an ensemble update strategy to explore the solution space more efficiently,while the retained initialization strategy provides highquality initial deployments.Experimental results show that AEQTSwE reduces the number of deployed sensors while satisfying the required coverage and connectivity constraints.It also converges faster and producesmore stable solutions than existing approaches under different conditions.Sensitivity,ablation,statistical,and complexity analyses further show that AEQTSwE has low parameter sensitivity,stable performance,and potential for larger and more complex deployment scenarios. 展开更多
关键词 Wireless sensor network deployment sensor deployment quantum-inspired optimization smart-city applications
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A novel hybrid cybersecurity assessment methodology for HTTPS deployment 认领 引用
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作者 Abdelhadi Zineddine Yousra Belfaik +1 位作者 Yassine Sadqi Said Safi 《High-Confidence Computing》 EI CSCD 2026年第2期1-19,共19页
Implementing HTTPS is a complex process encompassing technical and human factors,with a significant reliance on the webmaster’s expertise.Although various evaluation methods have been proposed in the scientific liter... Implementing HTTPS is a complex process encompassing technical and human factors,with a significant reliance on the webmaster’s expertise.Although various evaluation methods have been proposed in the scientific literature to address HTTPS deployment challenges,including vulnerabilities related to X.509 certificate fields,cipher suites,mixed content,encryption libraries,and the behaviors of users and webmasters,there remains a lack of a comprehensive methodology that integrates these metrics into a unified framework.To address this gap,this paper introduces a novel hybrid assessment methodology that combines three main cybersecurity assessment techniques:examination,testing,and interviewing.The methodology is further enhanced by integrating K-Means clustering with Large Language Model(LLM)reasoning to interpret interview-based insights and uncover behavioral patterns.It evaluates 12 critical security measures and mechanisms essential for robust HTTPS deployment.The effectiveness of the proposed methodology is demonstrated through a case study analyzing the security posture of HTTPS-secured websites across five domains:e-commerce,e-finance,education,government,and e-newspapers.The obtained results prove the applicability of the methodology in real-world scenarios and offer actionable insights for practitioners and researchers.In addition,the generated dataset of findings provides a comprehensive overview of the analyzed HTTPS website’s security levels and establishes a valuable foundation for future research to improve HTTPS implementation. 展开更多
关键词 HTTPS deployment Cybersecurity assessment GPT-4o Human factors Hybrid framework HTTPS-secured websites
Highway roadside unit deployment under uneven distribution evolution of intelligent connected vehicles 认领 引用
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作者 Jiyuan Zhou Xiaolin Yu +3 位作者 Jian Geng Ying Zhang Luyu Zhang Zhenhua Mou 《Journal of Highway and Transportation Research and Development(English Edition)》 2026年第1期67-77,共11页
As the penetration rate of connected and autonomous vehicles(CAVs)increases on highways,their role as network nodes for communication tasks raises significant challenges.Spatial heterogeneity in node distribution crea... As the penetration rate of connected and autonomous vehicles(CAVs)increases on highways,their role as network nodes for communication tasks raises significant challenges.Spatial heterogeneity in node distribution creates density discrepancies that substantially impact network lifetime and stability,thereby constraining optimal deployment strategies for road side units(RSUs).This study introduces an enhanced low-energy adaptive clustering hierarchy(LEACH)clustering algorithm tailored for vehicular networks.By identifying dense and sparse regions through dynamic clustering,the algorithm categorizes node functions according to regional characteristics to balance energy consumption and improve network connectivity.MATLAB simulations validate the algorithm’s performance under non-uniform vehicle distributions.The research further analyzes how vehicle node distribution patterns and CAV penetration rates affect optimal RSU deployment intervals.Key findings reveal that with consistent RSU-vehicle communication ranges:At a CAV traffic density of 0.01 and relative spatial density of 0.5(uniform distribution),RSUs should be deployed at 641 m intervals.At a relative density of 0.9(concentrated distribution),deployment intervals can expand to 1,887 m while maintaining high network connectivity.This adaptive strategy reduces communication blind spots by 32%,lowers deployment costs by 18%,and enhances vehicle-road coordination efficiency and traffic safety.The results provide critical technical support for intelligent vehicle-road collaboration systems. 展开更多
关键词 intelligent transport heterogeneous traffic flow vehicle grouping RSU deployment LEACH algorithm uneven distribution
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Challenges in the Large-Scale Deployment of CCUS 认领 引用 被引量：14
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作者 Zhenhua Rui Lianbo Zeng Birol Dindoruk 《Engineering》 SCIE EI CSCD 2025年第1期17-20,共4页
1.Introduction Climate change mitigation pathways aimed at limiting global anthropogenic carbon dioxide(CO2)emissions while striving to constrain the global temperature increase to below 2℃—as outlined by the Int... 1.Introduction Climate change mitigation pathways aimed at limiting global anthropogenic carbon dioxide(CO2)emissions while striving to constrain the global temperature increase to below 2℃—as outlined by the Intergovernmental Panel on Climate Change(IPCC)—consistently predict the widespread implementation of CO2geological storage on a global scale. 展开更多
关键词 Large-Scale Deployment CCUS Challenges Climate Change Mitigation
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Asynchronous deployment scheme and multibody modeling of a ring-truss mesh reflector antenna 认领 引用 被引量：1
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作者 Baiyan He Kangkang Li +3 位作者 Lijun Jia Rui Nie Yesen Fan Guobiao Wang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2025年第5期190-206,共17页
Mesh reflector antennas are the mainstream of large space-borne antennas,and the stretching of the truss achieves their deployment.Currently,the truss is commonly designed to be a single degree of freedom(DOF)deployab... Mesh reflector antennas are the mainstream of large space-borne antennas,and the stretching of the truss achieves their deployment.Currently,the truss is commonly designed to be a single degree of freedom(DOF)deployable mechanism with synchronization constraints.However,each deployable unit’s drive distribution and resistance load are uneven,and the forced synchronization constraints lead to the flexible deformation of rods and difficulties in the deployment scheme design.This paper introduces an asynchronous deployment scheme with a multi-DOF closed-chain deployable truss.The DOF of the truss is calculated,and the kinematic and dynamic models are established,considering the truss’s and cable net’s real-time coupling.An integrated solving algorithm for implicit differential-algebraic equations is proposed to solve the dynamic models.A prototype of a six-unit antenna was fabricated,and the experiment was carried out.The dynamic performances in synchronous and asynchronous deployment schemes are analyzed,and the results show that the cable resistance and truss kinetic energy impact under the asynchronous deployment scheme are minor,and the antenna is more straightforward to deploy.The work provides a new asynchronous deployment scheme and a universal antenna modeling method for dynamic design and performance improvement. 展开更多
关键词 Mesh antenna Deployment dynamic Driving scheme Performance evaluation Numerical analysis
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GBiDC-PEST:A novel lightweight model for real-time multiclass tiny pest detection and mobile platform deployment 认领 引用 被引量：1
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作者 Weiyue Xu Ruxue Yang +2 位作者 Raghupathy Karthikeyan Yinhao Shi Qiong Su 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2025年第7期2749-2769,共21页
Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has b... Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings. 展开更多
关键词 mobile counting real-time processing pest detection tiny object identification algorithm deployment
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Optimized Deployment Method for Finite Access Points Based on Virtual Force Fusion Bat Algorithm 认领 引用
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作者 Jian Li Qing Zhang +2 位作者 Tong Yang Yu’an Chen Yongzhong Zhan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第9期3029-3051,共23页
In the deployment of wireless networks in two-dimensional outdoor campus spaces,aiming at the problem of efficient coverage of the monitoring area by limited number of access points(APs),this paper proposes a deployme... In the deployment of wireless networks in two-dimensional outdoor campus spaces,aiming at the problem of efficient coverage of the monitoring area by limited number of access points(APs),this paper proposes a deployment method of multi-objective optimization with virtual force fusion bat algorithm(VFBA)using the classical four-node regular distribution as an entry point.The introduction of Lévy flight strategy for bat position updating helps to maintain the population diversity,reduce the premature maturity problem caused by population convergence,avoid the over aggregation of individuals in the local optimal region,and enhance the superiority in global search;the virtual force algorithm simulates the attraction and repulsion between individuals,which enables individual bats to precisely locate the optimal solution within the search space.At the same time,the fusion effect of virtual force prompts the bat individuals to move faster to the potential optimal solution.To validate the effectiveness of the fusion algorithm,the benchmark test function is selected for simulation testing.Finally,the simulation result verifies that the VFBA achieves superior coverage and effectively reduces node redundancy compared to the other three regular layout methods.The VFBA also shows better coverage results when compared to other optimization algorithms. 展开更多
关键词 Multi-objective optimization deployment virtual force algorithm bat algorithm fusion algorithm
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Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations 认领 引用
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作者 Jiaying Shen Donglin Zhu +5 位作者 Yujia Liu Leyi Wang Jialing Hu Zhaolong Ouyang Changjun Zhou Taiyong Li 《Computers, Materials & Continua》 SCIE EI 2025年第1期345-369,共25页
The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(I... The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO. 展开更多
关键词 Particle swarm optimization effective coverage area global optimization base station deployment
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Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment:Advancing Big Data Intelligence in Control Systems 认领 引用
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作者 Peiying Zhang Yihong Yu +3 位作者 Jing Liu ChongLv Lizhuang Tan Yulin Zhang 《Computers, Materials & Continua》 SCIE EI 2025年第6期4393-4409,共17页
As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitati... As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitating data-driven decision making,fostering innovation ecosystems,and maintaining operational stability.In this study,we propose an advanced deployment algorithm for Service Function Chaining(SFC)that leverages an enhanced Practical Byzantine Fault Tolerance(PBFT)mechanism.The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings.By integrating blockchain technology and Deep Reinforcement Learning(DRL),our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment.Specifically,the enhanced PBFT consensus mechanism(VRPBFT)significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function(VRF)and a node reputation grading model.Experimental results demonstrate that compared to traditional PBFT,the proposed VRPBFT algorithm reduces consensus latency by approximately 30%and decreases the proportion of Byzantine nodes by 40%after 100 rounds of consensus.Furthermore,the DRL-based SFC deployment algorithm(SDRL)exhibits rapid convergence during training,with improvements in long-term average revenue,request acceptance rate,and revenue/cost ratio of 17%,14.49%,and 20.35%,respectively,over existing algorithms.Additionally,the CPU resource utilization of the SDRL algorithmreaches up to 42%,which is 27.96%higher than other algorithms.These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency,service quality,and security in SFC deployment. 展开更多
关键词 Big data intelligent transformation heterogeneous networks service function chain blockchain deep reinforcement learning trusted deployment
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A Base Station Deployment Algorithm for Wireless Positioning Considering Dynamic Obstacles 认领 引用
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作者 Aiguo Li Yunfei Jia 《Computers, Materials & Continua》 SCIE EI 2025年第3期4573-4591,共19页
In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel.Most studies focus on optimizing base station deploym... In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel.Most studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind spots.However,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic environments.Nevertheless,research in this area still needs to be conducted.This paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this problem.This algorithm considers the dynamic alterations in obstacle locations within the designated area.It determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage rates.The experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle positions.Experimental results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles change.With 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage. 展开更多
关键词 Wireless positioning base station deployment dynamic obstacles dynamic obstacle wireless positioning
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Modelling and simulating the dynamics of resource deployment system 认领 引用
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作者 WU Weiwei SHI Jian LIU Yexin 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第3期701-713,共13页
Resource management must attach importance to effective resource deployment.Aiming at the research of resource deployment system,firstly,as an important factor of resource deployment system,corporate technological inn... Resource management must attach importance to effective resource deployment.Aiming at the research of resource deployment system,firstly,as an important factor of resource deployment system,corporate technological innovation social responsibility(CISR)is analyzed.Based on this,this paper constructs a system dynamics model to analyze the changes in resource deployment system affected by CISR.The simulation model is developed using Venism personal learning edition(PLE).The results show that CISR,acted as a new factor affecting the resource deployment system,has a positive effect on resource deployment system performance.Moreover,when CISR exceeds the threshold value,the resource deployment system performance increases significantly faster,reflecting that the resource deployment system becomes more efficient.The results show that the method proposed in this paper is feasible and efficient.This research provides theoretical and practical implications for resource deployment system research. 展开更多
关键词 corporate technological innovation social responsibility resource deployment system performance strategic uniqueness strategic flexibility system dynamics
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Strategic Global Deployment of Photovoltaic Technology:Balancing Economic Capacity and Decarbonization Potential 认领 引用
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作者 Ian Marius PETERS 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第2期261-268,共8页
This study investigates the disparities in the deployment of photovoltaic(PV)technology for carbon emissions reduction across different nations,highlighting the mismatch between countries with high economic capacity a... This study investigates the disparities in the deployment of photovoltaic(PV)technology for carbon emissions reduction across different nations,highlighting the mismatch between countries with high economic capacity and those where PV installation would maximize global decarbonization benefits.This mismatch is discussed based on three key factors influencing decarbonization via PV technology:per capita gross domestic product;carbon intensity of the energy system;and solar resource availability.Current PV deployment is predominantly concentrated in economically advanced countries,and does not coincide with regions where the environmental and economic impact of such installations would be most significant.Through a series of thought experiments,it is demonstrated how alternative prioritization strategies could significantly reduce global carbon emissions.Argument is put forward for a globally coordinated approach to PV deployment,particularly targeting high-impact sunbelt regions,to enhance the efficacy of decarbonization efforts and promote equitable energy access.The study underscores the need for international policies that support sustainable energy transitions in economically less developed regions through workforce development and assistance with the activation of capital. 展开更多
关键词 photovoltaic deployment decarbonization strategies solar resource availability global energy equity carbon emission reductions
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Multi-objective topology optimization for cutout design in deployable composite thin-walled structures 认领 引用
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作者 Hao JIN Ning AN +3 位作者 Qilong JIA Chun SHAO Xiaofei MA Jinxiong ZHOU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期674-694,共21页
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu... Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:http://gffzz188fe103f8f1460asxkfq6kq0xppo6bbp.ffgz.tsg.suse.edu.cn/jinhao-ok1/Topo-for-DCTWS.git. 展开更多
关键词 Composite laminates Deployable structures Multi-objective optimization Thin-walled structures Topology optimization
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