Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus t...Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.展开更多
Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus t...Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.展开更多
Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can e...Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can enhance operational efficiency through better and informed decision making. This review article examines the application of geospatial technologies, including GPS, GIS, and remote sensing, for optimizing resource utilization in livestock management. It compares these technologies to traditional livestock management practices and highlights their potential to improve animal tracking, feed intake monitoring, disease monitoring, pasture selection, and rangeland management. Previously, animal management practices were labor-intensive, time-consuming, and required more precision for optimal animal health and productivity. Digital technologies, including Artificial Intelligence (AI) and Machine Learning (ML) have transformed the livestock sector through precision livestock management. However, major challenges such as high cost, availability and accessibility to these technologies have deterred their implementation. To fully realize the benefits and tremendous contribution of these digital technologies and to address the challenges associated with their widespread adoption, the review proposes a collaborative approach between different stakeholders in the livestock sector including livestock farmers, researchers, veterinarians, industry professionals, technology developers, the private sector, financial institutions and government to share knowledge and expertise. The collaboration would facilitate the integration of various strategies to ensure the effective and wide adoption of digital technologies in livestock management by supporting the development of user-friendly and accessible tools tailored to specific livestock management and production systems.展开更多
With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integ...With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture.Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced.Satellite mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of networks.Furthermore,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference constraints.The simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.展开更多
In the next generation of computing environment e-health care services depend on cloud services.The Cloud computing environment provides a real-time computing environment for e-health care applications.But these servi...In the next generation of computing environment e-health care services depend on cloud services.The Cloud computing environment provides a real-time computing environment for e-health care applications.But these services generate a huge number of computational tasks,real-time computing and comes with a deadline,so conventional cloud optimizationmodels cannot fulfil the task in the least time and within the deadline.To overcome this issue many resource optimization meta-heuristic models are been proposed but these models cannot find a global best solution to complete the task in the least time and manage utilization with the least simulation time.In order to overcome existing issues,an artificial neural-inspired whale optimization is proposed to provide a reliable solution for healthcare applications.In this work,two models are proposed one for reliability estimation and the other is based on whale optimization technique and neural network-based binary classifier.The predictive model enhances the quality of service using performance metrics,makespan,least average task completion time,resource usages cost and utilization of the system.Fromresults as compared to existing algorithms the proposedANN-WHOalgorithms prove to improve the average start time by 29.3%,average finish time by 29.5%and utilization by 11%.展开更多
The emergence of coordinated and consistent macro behavior among self-interested individuals competing for limited resources represents a central inquiry in comprehending market mechanisms and collective behavior.Trad...The emergence of coordinated and consistent macro behavior among self-interested individuals competing for limited resources represents a central inquiry in comprehending market mechanisms and collective behavior.Traditional economics tackles this challenge through a mathematical and theoretical lens,assuming individuals are entirely rational and markets tend to stabilize through the price mechanism.Our paper addresses this issue from an econophysics standpoint,employing reinforcement learning to construct a multi-agent system modeled on minority games.Our study has undertaken a comparative analysis from both collective and individual perspectives,affirming the pivotal roles of reward feedback and individual memory in addressing the aforementioned challenge.Reward feedback serves as the guiding force for the evolution of collective behavior,propelling it towards an overall increase in rewards.Individuals,drawing insights from their own rewards through accumulated learning,gain information about the collective state and adjust their behavior accordingly.Furthermore,we apply information theory to present a formalized equation for the evolution of collective behavior.Our research supplements existing conclusions regarding the mechanisms of a free market and,at a micro level,unveils the dynamic evolution of individual behavior in synchronization with the collective.展开更多
Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities.This study introduces a neural network-ba...Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities.This study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing conditions.Designed to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real time.The training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent performance.The simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods.展开更多
As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational ...As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.展开更多
The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tas...The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times.展开更多
Fifth generation(5G)cellular networks intend to overcome the challenging demands posed by dynamic service quality requirements,which are not achieved by single network technology.The future cellular networks require e...Fifth generation(5G)cellular networks intend to overcome the challenging demands posed by dynamic service quality requirements,which are not achieved by single network technology.The future cellular networks require efficient resource allocation and power control schemes that meet throughput and energy efficiency requirements when multiple technologies coexist and share network resources.In this paper,we optimize the throughput and energy efficiency(EE)performance for the coexistence of two technologies that have been identified for the future cellular networks,namely,massive multiple-input multiple-output(MIMO)and network-assisted device-to-device(D2D)communications.In such a hybrid network,the co/cross-tier interferences between cellular and D2D communications caused by spectrum sharing is a significant challenge.To this end,we formulate the average sum rate and EE optimization problem as mixed-integer non-linear programming(MINLP).We develop distributed resource allocation algorithms based on matching theory to alleviate interferences and optimize network performance.It is shown in this paper that the proposed algorithms converge to a stable matching and terminate after finite iterations.Matlab simulation results show that the proposed algorithms achieved more than 88%of the average transmission rate and 86%of the energy efficiency performance of the optimal matching with lower complexity.展开更多
In order to optimize resource integration and optimal scheduling problems in the cloud manufacturing environment,this paper proposes to use load balancing,service cost and service quality as optimization goals for res...In order to optimize resource integration and optimal scheduling problems in the cloud manufacturing environment,this paper proposes to use load balancing,service cost and service quality as optimization goals for resource scheduling,however,resource providers have resource utilization requirements for cloud manufacturing platforms.In the process of resource optimization scheduling,the interests of all parties have conflicts of interest,which makes it impossible to obtain better optimization results for resource scheduling.Therefore,amultithreaded auto-negotiation method based on the Stackelberg game is proposed to resolve conflicts of interest in the process of resource scheduling.The cloud manufacturing platform first calculates the expected value reduction plan for each round of global optimization,using the negotiation algorithm based on the Stackelberg game,the cloud manufacturing platformnegotiates andmediateswith the participants’agents,to maximize self-interest by constantly changing one’s own plan,iteratively find multiple sets of locally optimized negotiation plans and return to the cloud manufacturing platform.Through multiple rounds of negotiation and calculation,we finally get a target expected value reduction plan that takes into account the benefits of the resource provider and the overall benefits of the completion of the manufacturing task.Finally,through experimental simulation and comparative analysis,the validity and rationality of the model are verified.展开更多
In order to realize sustainable development of the arid area of Northwest China, rational water resources exploitation and optimization are primary prerequisites. Based on the essential principle of sustainable develo...In order to realize sustainable development of the arid area of Northwest China, rational water resources exploitation and optimization are primary prerequisites. Based on the essential principle of sustainable development, this paper puts forward a general idea on water resources optimization and eco-environmental protection in Qaidam Basin, and identifies the competitive multiple targets of water resources optimization. By some qualitative methods such as Input-output Model & AHP Model and some quantitative methods such as System Dynamics Model & Produce Function Model, some standard plans of water resources optimization come into being. According to the Multiple Targets Decision by the Closest Value Model, the best plan of water resources optimization, eco-environmental protection and sustainable development in Qaidam Basin is finally decided.展开更多
Extensive land use will cause many environmental problems.It is an urgent task to improve land use efficiency and optimize land use patterns.In recent years,due to the flow decrease,the Guanzhong Basin in Shaanxi Prov...Extensive land use will cause many environmental problems.It is an urgent task to improve land use efficiency and optimize land use patterns.In recent years,due to the flow decrease,the Guanzhong Basin in Shaanxi Province is confronted with the problem of insufficient water resources reserve.Based on the Coupled Ground-Water and Surface-Water Flow Model(GSFLOW),this paper evaluates the response of water resources in the basin to changes in land use patterns,optimizes the land use pattern,improves the ecological and economic benefits,and the efficiency of various spatial development,providing a reference for ecological protection and high-quality development of the Yellow River Basin.The research shows that the land use pattern in the Guanzhong Basin should be further optimized.Under the condition of considering ecological and economic development,the percentage change of the optimum area of farmland,forest,grassland,water area,and urban area compared with the current land use area ratio is+2.3,+2.4,-6.1,+0.2,and+1.6,respectively.The economic and ecological value of land increases by14.1%and 3.1%,respectively,and the number of water resources can increase by 2.5%.展开更多
According to the "Special Water Supply Plan for Southern Cities and Towns in Huangshan City (2017-2030)", Fengle Reservoir and Yuetan Reservoir are the two major water supply sources for southern cities and ...According to the "Special Water Supply Plan for Southern Cities and Towns in Huangshan City (2017-2030)", Fengle Reservoir and Yuetan Reservoir are the two major water supply sources for southern cities and towns (Tunxi District, Huangshan Hi-tech Zone, Xiuning County, Huizhou District and Shexian County). This topic focuses on giving full play to the basic role of the two reservoirs in ensuring regional water supply safety and ecological safety. Therefore, our idea of optimal integration of water resources is also carried out within the regional scope of the entire southern cities and towns. It is elaborated and analyzed from the perspectives of water supply status, water resources status and allocation, water supply demand and planning, water resources integration, and other issues and suggestions are put forward展开更多
Applying non-orthogonal multiple access(NOMA)to the mobile edge computing(MEC)network supported by unmanned aerial vehicles(UAVs)can improve spectral efficiency and achieve massive user access on the basis of solving ...Applying non-orthogonal multiple access(NOMA)to the mobile edge computing(MEC)network supported by unmanned aerial vehicles(UAVs)can improve spectral efficiency and achieve massive user access on the basis of solving computing resource constraints and coverage problems.However,the UAV-enabled network has a serious risk of information leakage on account of the openness of wireless channel.This paper considers a UAV-MEC secure network based on NOMA technology,which aims to minimize the UAV energy consumption.To achieve the purpose while meeting the security and users’latency requirements,we formulate an optimization problem that jointly optimizes the UAV trajectory and the allocation of network resources.Given that the original problem is non-convex and multivariate coupled,we proposed an effective algorithm to decouple the nonconvex problem into independent user relation coefficients and subproblems based on successive convex approximation(SCA)and block coordinate descent(BCD).The simulation results showcase the performance of our optimization scheme across various parameter settings and confirm its superiority over other benchmarks with respect to energy consumption.展开更多
This study explores the influence of Green Logistics Management(GLM)on Sustainable Logistics Performance(SLP),emphasizing the pivotal role of Green Innovation(GI)in promoting sustainability and enhancing logistics eff...This study explores the influence of Green Logistics Management(GLM)on Sustainable Logistics Performance(SLP),emphasizing the pivotal role of Green Innovation(GI)in promoting sustainability and enhancing logistics efficiency(LE).As organizations increasingly seek to align operational efficiency with environmental goals,GLM has emerged as a strategic approach to achieving this balance.The research evaluates the impact of GLM on SLP,examines GI’s contribution to improving LE,and validates the relationship between green logistics practices and SLP.Survey-based data analysis employing reliable scales(AVE and Cronbach’s alpha>0.70)reveals that GI significantly advances LE.Firms demonstrate a strong commitment to sustainability,with high scores for eco-friendly packaging(5.35)and clean technologies(5.14).Despite this,variability in adoption rates highlights differences in implementation across organizations.The findings confirm that GLM positively influences SLP,underscoring the importance of integrating green practices into logistics operations.This study provides actionable insights for organizations and policymakers by addressing inconsistencies in green logistics practices and proposing strategies to enhance sustainability and operational efficiency.It presents a practical framework for improving environmental and business performance,offering valuable guidance for firms striving to achieve sustainable growth while meeting environmental objectives.The research contributes to advancing the logistics sector’s sustainability and innovation-driven performance.展开更多
Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive information.However,in low-light scenarios,the illuminati...Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive information.However,in low-light scenarios,the illumination degradation of visible light images makes it difficult for existing fusion methods to extract texture detail information from the scene.At this time,relying solely on the target saliency information provided by infrared images is far from sufficient.To address this challenge,this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement,named LLE-Fuse.The method is based on the improvement of the MobileOne Block,using the Edge-MobileOne Block embedded with the Sobel operator to perform feature extraction and downsampling on the source images.The intermediate features at different scales obtained are then fused by a cross-modal attention fusion module.In addition,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is used for image enhancement of both infrared and visible light images,guiding the network model to learn low-light enhancement capabilities through enhancement loss.Upon completion of network training,the Edge-MobileOne Block is optimized into a direct connection structure similar to MobileNetV1 through structural reparameterization,effectively reducing computational resource consumption.Finally,after extensive experimental comparisons,our method achieved improvements of 4.6%,40.5%,156.9%,9.2%,and 98.6%in the evaluation metrics Standard Deviation(SD),Visual Information Fidelity(VIF),Entropy(EN),and Spatial Frequency(SF),respectively,compared to the best results of the compared algorithms,while only being 1.5 ms/it slower in computation speed than the fastest method.展开更多
Effective water distribution and transparency are threatened with being outrightly undermined unless the good name of urban infrastructure is maintained.With improved control systems in place to check leakage,variabil...Effective water distribution and transparency are threatened with being outrightly undermined unless the good name of urban infrastructure is maintained.With improved control systems in place to check leakage,variability of pressure,and conscientiousness of energy,issues that previously went unnoticed are now becoming recognized.This paper presents a grandiose hybrid framework that combines Multi-Agent Deep Reinforcement Learning(MADRL)with Shapley Additive Explanations(SHAP)-based Explainable AI(XAI)for adaptive and interpretable water resource management.In the methodology,the agents perform decentralized learning of the control policies for the pumps and valves based on the real-time network states,while also providing human-understandable explanations of the agents’decisions,using SHAP.This framework has been validated on five very diverse datasets,three of which are real-world scenarios involving actual water consumption from NYC and Alicante,with the other two being simulationbased standards such as LeakDB and the Water Distribution System Anomaly(WDSA)network.Empirical results demonstrate that the MADRL SHAP hybrid system reduces water loss by up to 32%,improves energy efficiency by+up to 25%,and maintains pressure stability between 91%and 93%,thereby outperforming the traditional rule-based control,single-agent DRL(Deep Reinforcement Learning),and XGBoost SHAP baselines.Furthermore,SHAP-based+interpretation brings transparency to the proposed model,with the average explanation consistency for all prediction models reaching 88%,thus further reinforcing the trustworthiness of the system on which the decision-making is based and empowering the utility operators to derive actionable insights from the model.The proposed framework addresses the critical challenges of smart water distribution.展开更多
One of the most effective technology for the 5G mobile communications is Device-to-device(D2D)communication which is also called terminal pass-through technology.It can directly communicate between devices under the c...One of the most effective technology for the 5G mobile communications is Device-to-device(D2D)communication which is also called terminal pass-through technology.It can directly communicate between devices under the control of a base station and does not require a base station to forward it.The advantages of applying D2D communication technology to cellular networks are:It can increase the communication system capacity,improve the system spectrum efficiency,increase the data transmission rate,and reduce the base station load.Aiming at the problem of co-channel interference between the D2D and cellular users,this paper proposes an efficient algorithm for resource allocation based on the idea of Q-learning,which creates multi-agent learners from multiple D2D users,and the system throughput is determined from the corresponding state-learning of the Q value list and the maximum Q action is obtained through dynamic power for control for D2D users.The mutual interference between the D2D users and base stations and exact channel state information is not required during the Q-learning process and symmetric data transmission mechanism is adopted.The proposed algorithm maximizes the system throughput by controlling the power of D2D users while guaranteeing the quality-of-service of the cellular users.Simulation results show that the proposed algorithm effectively improves system performance as compared with existing algorithms.展开更多
Recently,Internet of Things(IoT)have been applied widely and improved the quality of the daily life.However,the lightweight IoT devices can hardly implement complicated applications since they usually have limited com...Recently,Internet of Things(IoT)have been applied widely and improved the quality of the daily life.However,the lightweight IoT devices can hardly implement complicated applications since they usually have limited computing resource and just can execute some simple computation tasks.Moreover,data transmission and interaction in IoT is another crucial issue when the IoT devices are deployed at remote areas without manual operation.Mobile edge computing(MEC)and unmanned aerial vehicle(UAV)provide significant solutions to these problems.In addition,in order to ensure the security and privacy of data,blockchain has been attracted great attention from both academia and industry.Therefore,an UAV-assisted IoT system integrated with MEC and blockchain is pro-posed.The optimization problem in the proposed architecture is formulated to achieve the optimal trade-off between energy consumption and computation latency through jointly considering computa-tion offloading decision,spectrum resource allocation and computing resource allocation.Consider-ing this complicated optimization problem,the non-convex mixed integer problem can be transformed into a convex problem,and a distributed algorithm based on alternating direction multiplier method(ADMM)is proposed.Simulation results demonstrate the validity of this scheme.展开更多
基金supported in part by the National Key R&D Program of China under Grant 2020YFB1005900the National Natural Science Foundation of China under Grant 62001220+3 种基金the Jiangsu Provincial Key Research and Development Program under Grants BE2022068the Natural Science Foundation of Jiangsu Province under Grants BK20200440the Future Network Scientific Research Fund Project FNSRFP-2021-YB-03the Young Elite Scientist Sponsorship Program,China Association for Science and Technology.
文摘Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.
基金This work was supported in part by the National Key R&D Program of China under Grant 2020YFB1005900the National Natural Science Foundation of China under Grant 62001220+3 种基金the Jiangsu Provincial Key Research and Development Program under Grants BE2022068the Natural Science Foundation of Jiangsu Province under Grants BK20200440the Future Network Scientific Research Fund Project FNSRFP-2021-YB-03the Young Elite Scientist Sponsorship Program,China Association for Science and Technology.
文摘Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.
文摘Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can enhance operational efficiency through better and informed decision making. This review article examines the application of geospatial technologies, including GPS, GIS, and remote sensing, for optimizing resource utilization in livestock management. It compares these technologies to traditional livestock management practices and highlights their potential to improve animal tracking, feed intake monitoring, disease monitoring, pasture selection, and rangeland management. Previously, animal management practices were labor-intensive, time-consuming, and required more precision for optimal animal health and productivity. Digital technologies, including Artificial Intelligence (AI) and Machine Learning (ML) have transformed the livestock sector through precision livestock management. However, major challenges such as high cost, availability and accessibility to these technologies have deterred their implementation. To fully realize the benefits and tremendous contribution of these digital technologies and to address the challenges associated with their widespread adoption, the review proposes a collaborative approach between different stakeholders in the livestock sector including livestock farmers, researchers, veterinarians, industry professionals, technology developers, the private sector, financial institutions and government to share knowledge and expertise. The collaboration would facilitate the integration of various strategies to ensure the effective and wide adoption of digital technologies in livestock management by supporting the development of user-friendly and accessible tools tailored to specific livestock management and production systems.
基金supported in part by the National Key R&D Program of China(2020YFB1806103)the National Natural Science Foundation of China under Grant 62225103 and U22B2003+1 种基金Beijing Natural Science Foundation(L212004)China University Industry-University-Research Collaborative Innovation Fund(2021FNA05001).
文摘With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture.Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced.Satellite mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of networks.Furthermore,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference constraints.The simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.
文摘In the next generation of computing environment e-health care services depend on cloud services.The Cloud computing environment provides a real-time computing environment for e-health care applications.But these services generate a huge number of computational tasks,real-time computing and comes with a deadline,so conventional cloud optimizationmodels cannot fulfil the task in the least time and within the deadline.To overcome this issue many resource optimization meta-heuristic models are been proposed but these models cannot find a global best solution to complete the task in the least time and manage utilization with the least simulation time.In order to overcome existing issues,an artificial neural-inspired whale optimization is proposed to provide a reliable solution for healthcare applications.In this work,two models are proposed one for reliability estimation and the other is based on whale optimization technique and neural network-based binary classifier.The predictive model enhances the quality of service using performance metrics,makespan,least average task completion time,resource usages cost and utilization of the system.Fromresults as compared to existing algorithms the proposedANN-WHOalgorithms prove to improve the average start time by 29.3%,average finish time by 29.5%and utilization by 11%.
基金partially supported by the National Natural Science Foundation of China(Nos.U22A20261 and 61402210)the National Key R&D Program of China(No.2020YFC0832500)+4 种基金the Gansu Province Science and Technology Major Project-Industrial Project(No.22ZD6GA048)the Gansu Province Key Research and Development Plan-Industrial Project(No.22YF7GA004)the Fundamental Research Funds for the Central Universities(Nos.lzujbky-2022-kb12,lzujbky-2021-sp43,lzujbky-2020-sp02,lzujbky-2019-kb51,and lzujbky-2018-k12)the Science and Technology Plan of Qinghai Province(No.2020-GX-164)the Supercomputing Center of Lanzhou University,and the Gansu Provincial Science and Technology Major Special Innovation Consortium Project(No.21ZD3GA002)。
文摘The emergence of coordinated and consistent macro behavior among self-interested individuals competing for limited resources represents a central inquiry in comprehending market mechanisms and collective behavior.Traditional economics tackles this challenge through a mathematical and theoretical lens,assuming individuals are entirely rational and markets tend to stabilize through the price mechanism.Our paper addresses this issue from an econophysics standpoint,employing reinforcement learning to construct a multi-agent system modeled on minority games.Our study has undertaken a comparative analysis from both collective and individual perspectives,affirming the pivotal roles of reward feedback and individual memory in addressing the aforementioned challenge.Reward feedback serves as the guiding force for the evolution of collective behavior,propelling it towards an overall increase in rewards.Individuals,drawing insights from their own rewards through accumulated learning,gain information about the collective state and adjust their behavior accordingly.Furthermore,we apply information theory to present a formalized equation for the evolution of collective behavior.Our research supplements existing conclusions regarding the mechanisms of a free market and,at a micro level,unveils the dynamic evolution of individual behavior in synchronization with the collective.
基金funding of the Deanship of Graduate Studies and Scientific Research,Jazan University,Saudi Arabia,through Project Number:ISP-2024.
文摘Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities.This study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing conditions.Designed to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real time.The training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent performance.The simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods.
基金in part by the National Natural Science Foundation of China(NSFC)under Grant 62371012in part by the Beijing Natural Science Foundation under Grant 4252001.
文摘As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.
文摘The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times.
文摘Fifth generation(5G)cellular networks intend to overcome the challenging demands posed by dynamic service quality requirements,which are not achieved by single network technology.The future cellular networks require efficient resource allocation and power control schemes that meet throughput and energy efficiency requirements when multiple technologies coexist and share network resources.In this paper,we optimize the throughput and energy efficiency(EE)performance for the coexistence of two technologies that have been identified for the future cellular networks,namely,massive multiple-input multiple-output(MIMO)and network-assisted device-to-device(D2D)communications.In such a hybrid network,the co/cross-tier interferences between cellular and D2D communications caused by spectrum sharing is a significant challenge.To this end,we formulate the average sum rate and EE optimization problem as mixed-integer non-linear programming(MINLP).We develop distributed resource allocation algorithms based on matching theory to alleviate interferences and optimize network performance.It is shown in this paper that the proposed algorithms converge to a stable matching and terminate after finite iterations.Matlab simulation results show that the proposed algorithms achieved more than 88%of the average transmission rate and 86%of the energy efficiency performance of the optimal matching with lower complexity.
基金Project was supported by the special projects for the central government to guide the development of local science and technology(ZY20B11).
文摘In order to optimize resource integration and optimal scheduling problems in the cloud manufacturing environment,this paper proposes to use load balancing,service cost and service quality as optimization goals for resource scheduling,however,resource providers have resource utilization requirements for cloud manufacturing platforms.In the process of resource optimization scheduling,the interests of all parties have conflicts of interest,which makes it impossible to obtain better optimization results for resource scheduling.Therefore,amultithreaded auto-negotiation method based on the Stackelberg game is proposed to resolve conflicts of interest in the process of resource scheduling.The cloud manufacturing platform first calculates the expected value reduction plan for each round of global optimization,using the negotiation algorithm based on the Stackelberg game,the cloud manufacturing platformnegotiates andmediateswith the participants’agents,to maximize self-interest by constantly changing one’s own plan,iteratively find multiple sets of locally optimized negotiation plans and return to the cloud manufacturing platform.Through multiple rounds of negotiation and calculation,we finally get a target expected value reduction plan that takes into account the benefits of the resource provider and the overall benefits of the completion of the manufacturing task.Finally,through experimental simulation and comparative analysis,the validity and rationality of the model are verified.
基金National Natural Science Foundation of China, No.49871035.
文摘In order to realize sustainable development of the arid area of Northwest China, rational water resources exploitation and optimization are primary prerequisites. Based on the essential principle of sustainable development, this paper puts forward a general idea on water resources optimization and eco-environmental protection in Qaidam Basin, and identifies the competitive multiple targets of water resources optimization. By some qualitative methods such as Input-output Model & AHP Model and some quantitative methods such as System Dynamics Model & Produce Function Model, some standard plans of water resources optimization come into being. According to the Multiple Targets Decision by the Closest Value Model, the best plan of water resources optimization, eco-environmental protection and sustainable development in Qaidam Basin is finally decided.
基金jointly supported by the National Natural Science Foundation of China(41702280)the projects of the China Geology Survey(DD20221754 and DD20190333)。
文摘Extensive land use will cause many environmental problems.It is an urgent task to improve land use efficiency and optimize land use patterns.In recent years,due to the flow decrease,the Guanzhong Basin in Shaanxi Province is confronted with the problem of insufficient water resources reserve.Based on the Coupled Ground-Water and Surface-Water Flow Model(GSFLOW),this paper evaluates the response of water resources in the basin to changes in land use patterns,optimizes the land use pattern,improves the ecological and economic benefits,and the efficiency of various spatial development,providing a reference for ecological protection and high-quality development of the Yellow River Basin.The research shows that the land use pattern in the Guanzhong Basin should be further optimized.Under the condition of considering ecological and economic development,the percentage change of the optimum area of farmland,forest,grassland,water area,and urban area compared with the current land use area ratio is+2.3,+2.4,-6.1,+0.2,and+1.6,respectively.The economic and ecological value of land increases by14.1%and 3.1%,respectively,and the number of water resources can increase by 2.5%.
文摘According to the "Special Water Supply Plan for Southern Cities and Towns in Huangshan City (2017-2030)", Fengle Reservoir and Yuetan Reservoir are the two major water supply sources for southern cities and towns (Tunxi District, Huangshan Hi-tech Zone, Xiuning County, Huizhou District and Shexian County). This topic focuses on giving full play to the basic role of the two reservoirs in ensuring regional water supply safety and ecological safety. Therefore, our idea of optimal integration of water resources is also carried out within the regional scope of the entire southern cities and towns. It is elaborated and analyzed from the perspectives of water supply status, water resources status and allocation, water supply demand and planning, water resources integration, and other issues and suggestions are put forward
基金supported in part by the National Natural Science Foundation of China under Grant 61971474in part by the National Natural Science Foundation of China under Grant 62301594+2 种基金in part by the Special Funds of the National Natural Science Foundation of China under Grant 62341112in part by the Beijing Nova Program under Grant Z201100006820121in part by the Beijing Municipal Science and Technology Project under Grant Z181100003218015.
文摘Applying non-orthogonal multiple access(NOMA)to the mobile edge computing(MEC)network supported by unmanned aerial vehicles(UAVs)can improve spectral efficiency and achieve massive user access on the basis of solving computing resource constraints and coverage problems.However,the UAV-enabled network has a serious risk of information leakage on account of the openness of wireless channel.This paper considers a UAV-MEC secure network based on NOMA technology,which aims to minimize the UAV energy consumption.To achieve the purpose while meeting the security and users’latency requirements,we formulate an optimization problem that jointly optimizes the UAV trajectory and the allocation of network resources.Given that the original problem is non-convex and multivariate coupled,we proposed an effective algorithm to decouple the nonconvex problem into independent user relation coefficients and subproblems based on successive convex approximation(SCA)and block coordinate descent(BCD).The simulation results showcase the performance of our optimization scheme across various parameter settings and confirm its superiority over other benchmarks with respect to energy consumption.
文摘This study explores the influence of Green Logistics Management(GLM)on Sustainable Logistics Performance(SLP),emphasizing the pivotal role of Green Innovation(GI)in promoting sustainability and enhancing logistics efficiency(LE).As organizations increasingly seek to align operational efficiency with environmental goals,GLM has emerged as a strategic approach to achieving this balance.The research evaluates the impact of GLM on SLP,examines GI’s contribution to improving LE,and validates the relationship between green logistics practices and SLP.Survey-based data analysis employing reliable scales(AVE and Cronbach’s alpha>0.70)reveals that GI significantly advances LE.Firms demonstrate a strong commitment to sustainability,with high scores for eco-friendly packaging(5.35)and clean technologies(5.14).Despite this,variability in adoption rates highlights differences in implementation across organizations.The findings confirm that GLM positively influences SLP,underscoring the importance of integrating green practices into logistics operations.This study provides actionable insights for organizations and policymakers by addressing inconsistencies in green logistics practices and proposing strategies to enhance sustainability and operational efficiency.It presents a practical framework for improving environmental and business performance,offering valuable guidance for firms striving to achieve sustainable growth while meeting environmental objectives.The research contributes to advancing the logistics sector’s sustainability and innovation-driven performance.
基金This researchwas Sponsored by Xinjiang Uygur Autonomous Region Tianshan Talent Programme Project(2023TCLJ02)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01C349).
文摘Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive information.However,in low-light scenarios,the illumination degradation of visible light images makes it difficult for existing fusion methods to extract texture detail information from the scene.At this time,relying solely on the target saliency information provided by infrared images is far from sufficient.To address this challenge,this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement,named LLE-Fuse.The method is based on the improvement of the MobileOne Block,using the Edge-MobileOne Block embedded with the Sobel operator to perform feature extraction and downsampling on the source images.The intermediate features at different scales obtained are then fused by a cross-modal attention fusion module.In addition,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is used for image enhancement of both infrared and visible light images,guiding the network model to learn low-light enhancement capabilities through enhancement loss.Upon completion of network training,the Edge-MobileOne Block is optimized into a direct connection structure similar to MobileNetV1 through structural reparameterization,effectively reducing computational resource consumption.Finally,after extensive experimental comparisons,our method achieved improvements of 4.6%,40.5%,156.9%,9.2%,and 98.6%in the evaluation metrics Standard Deviation(SD),Visual Information Fidelity(VIF),Entropy(EN),and Spatial Frequency(SF),respectively,compared to the best results of the compared algorithms,while only being 1.5 ms/it slower in computation speed than the fastest method.
基金supported via funding from Prince sattam bin Abdulaziz University project number(PSAU/2025/R/1446).
文摘Effective water distribution and transparency are threatened with being outrightly undermined unless the good name of urban infrastructure is maintained.With improved control systems in place to check leakage,variability of pressure,and conscientiousness of energy,issues that previously went unnoticed are now becoming recognized.This paper presents a grandiose hybrid framework that combines Multi-Agent Deep Reinforcement Learning(MADRL)with Shapley Additive Explanations(SHAP)-based Explainable AI(XAI)for adaptive and interpretable water resource management.In the methodology,the agents perform decentralized learning of the control policies for the pumps and valves based on the real-time network states,while also providing human-understandable explanations of the agents’decisions,using SHAP.This framework has been validated on five very diverse datasets,three of which are real-world scenarios involving actual water consumption from NYC and Alicante,with the other two being simulationbased standards such as LeakDB and the Water Distribution System Anomaly(WDSA)network.Empirical results demonstrate that the MADRL SHAP hybrid system reduces water loss by up to 32%,improves energy efficiency by+up to 25%,and maintains pressure stability between 91%and 93%,thereby outperforming the traditional rule-based control,single-agent DRL(Deep Reinforcement Learning),and XGBoost SHAP baselines.Furthermore,SHAP-based+interpretation brings transparency to the proposed model,with the average explanation consistency for all prediction models reaching 88%,thus further reinforcing the trustworthiness of the system on which the decision-making is based and empowering the utility operators to derive actionable insights from the model.The proposed framework addresses the critical challenges of smart water distribution.
文摘One of the most effective technology for the 5G mobile communications is Device-to-device(D2D)communication which is also called terminal pass-through technology.It can directly communicate between devices under the control of a base station and does not require a base station to forward it.The advantages of applying D2D communication technology to cellular networks are:It can increase the communication system capacity,improve the system spectrum efficiency,increase the data transmission rate,and reduce the base station load.Aiming at the problem of co-channel interference between the D2D and cellular users,this paper proposes an efficient algorithm for resource allocation based on the idea of Q-learning,which creates multi-agent learners from multiple D2D users,and the system throughput is determined from the corresponding state-learning of the Q value list and the maximum Q action is obtained through dynamic power for control for D2D users.The mutual interference between the D2D users and base stations and exact channel state information is not required during the Q-learning process and symmetric data transmission mechanism is adopted.The proposed algorithm maximizes the system throughput by controlling the power of D2D users while guaranteeing the quality-of-service of the cellular users.Simulation results show that the proposed algorithm effectively improves system performance as compared with existing algorithms.
基金Supported by the National Natural Science Foundation of China(No.61901011,61901067)the Foundation of Beijing Municipal Commission of Education(No.KM202110005021,KM202010005017)the Beijing Natural Science Foundation(No.L211002).
文摘Recently,Internet of Things(IoT)have been applied widely and improved the quality of the daily life.However,the lightweight IoT devices can hardly implement complicated applications since they usually have limited computing resource and just can execute some simple computation tasks.Moreover,data transmission and interaction in IoT is another crucial issue when the IoT devices are deployed at remote areas without manual operation.Mobile edge computing(MEC)and unmanned aerial vehicle(UAV)provide significant solutions to these problems.In addition,in order to ensure the security and privacy of data,blockchain has been attracted great attention from both academia and industry.Therefore,an UAV-assisted IoT system integrated with MEC and blockchain is pro-posed.The optimization problem in the proposed architecture is formulated to achieve the optimal trade-off between energy consumption and computation latency through jointly considering computa-tion offloading decision,spectrum resource allocation and computing resource allocation.Consider-ing this complicated optimization problem,the non-convex mixed integer problem can be transformed into a convex problem,and a distributed algorithm based on alternating direction multiplier method(ADMM)is proposed.Simulation results demonstrate the validity of this scheme.
