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

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Computers, Materials & Continua 2024年第1期78卷 1135-1156页

作者： Shasha Zhao Huanwen Yan Qifeng Lin Xiangnan Feng He Chen Dengyin Zhang college of Internet of things Nanjing University of Posts and TelecommunicationsNanjing210003China Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of things Nanjing University of Posts and TelecommunicationsNanjing210003China Tongding Interconnection Information Co. Ltd.Suzhou215000China

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

关键词： Cloud computing distributed processing evolutionary artificial bee colony algorithm hierarchical particle swarm optimization load balancing

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A Least-squares-based Iterative Method with Better Convergence for PF/OPF in Integrated Transmission and Distribution Networks

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 953-962页

作者： Kunjie Tang Shufeng Dong Yonghua Song college of Electrical Engineering Zhejiang UniversityHangzhou 310027China State Key Laboratory of Internet of things for Smart City and Department of Electrical and Computer Engineering University of MacaoMacao SARChina IEEE

The limitations of the conventional master-slavesplitting(MSS)method,which is commonly applied to power flow and optimal power flow in integrated transmission and distribution(I-T&D)networks,are first *** that the MSS method suffers from a slow convergence rate or even divergence under some circumstances,a least-squares-based iterative(LSI)method is *** with the MSS method,the LSI method modifies the iterative variables in each iteration by solving a least-squares problem with the information in previous iterations.A practical implementation and a parameter tuning strategy for the LSI method are ***,a LSI-PF method is proposed to solve I-T&D power flow and a LSIheterogeneous decomposition(LSI-HGD)method is proposed to solve optimal power *** experiments demonstrate that the proposed LSI-PF and LSI-HGD methods can achieve the same accuracy as the benchmark ***,these LSI methods,with appropriate settings,significantly enhance the convergence and efficiency of conventional ***,in some cases,where conventional methods diverge,these LSI methods can still converge.

关键词： Fixed-point theory integrated transmission and distribution networks least-squares-based iterative method master-slave-splitting method optimal power flow power flow

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

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Digital Communications and Networks 2024年第1期10卷 109-116页

作者： Xiaoming He Yingchi Mao Yinqiu Liu Ping Ping Yan Hong Han Hu The college of Computer and Information Hohai UniversityNanjingChina The Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjing China The School of Computer Science and Engineering Nanyang Technological UniversitySingapore The college of Textile and Clothing Engineering Soochow UniversitySuzhouChina The college of Internet of things Nanjing University of Posts and TelecomurunicationsNanjingChina

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

关键词： B5G Heterogeneous edge networks PPO Channel assignment Power allocation Throughput

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A convolutional neural network to detect possible hidden data in spatial domain images

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Cybersecurity 2024年第1期7卷 37-52页

作者： Jean De La Croix Ntivuguruzwa Tohari Ahmad Department of Informatics Institut Teknologi Sepuluh Nopember(ITS)Kampus ITS Keputih SukoliloSurabaya60111Indonesia African Center of Excellence in the Internet of things College of Science and TechnologyUniversity of Rwanda3900KigaliRwanda

Hiding secret data in digital multimedia has been essential to protect the ***,attackers with a steganalysis technique may break *** steganalysis methods have good results with conventional Machine Learning(ML)techniques;however,the introduction of Convolutional Neural Network(CNN),a deep learning paradigm,achieved better performance over the previously proposed ML-based *** the existing CNN-based approaches yield good results,they present performance issues in classification accuracy and stability in the network training *** research proposes a new method with a CNN architecture to improve the hidden data detection accuracy and the training phase stability in spatial domain *** proposed method comprises three phases:pre-processing,feature extraction,and ***,in the pre-processing phase,we use spatial rich model filters to enhance the noise within images altered by data hiding;secondly,in the feature extraction phase,we use two-dimensional depthwise separable convolutions to improve the signal-to-noise and regular convolutions to model local features;and finally,in the classification,we use multi-scale average pooling for local features aggregation and representability enhancement regardless of the input size variation,followed by three fully connected layers to form the final feature maps that we transform into class probabilities using the softmax *** results identify an improvement in the accuracy of the considered recent scheme ranging between 4.6 and 10.2%with reduced training time up to 30.81%.

关键词： Information security Spatial domain steganalysis Deep learning Convolutional neural network Infrastructure

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Integrated Clustering and Routing Design and Triangle Path Optimization for UAV-Assisted Wireless Sensor Networks

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China Communications 2024年第4期21卷 178-192页

作者： Shao Liwei Qian Liping Wu Mengru Wu Yuan college of Information Engineering Zhejiang University of TechnologyHangzhou 310014China State Key Laboratory of Internet of things for Smart City University of MacaoMacaoChina Department of Computer and Information Science University of MacaoMacaoChina

With the development of the Internet of things(IoT),it requires better performance from wireless sensor networks(WSNs),such as larger coverage,longer lifetime,and lower ***,a large amount of data generated from monitoring and long-distance transmission places a heavy burden on sensor nodes with the limited battery *** this,we investigate an unmanned aerial vehicles assisted mobile wireless sensor network(UAV-assisted WSN)to prolong the network lifetime in this ***,we use UAVs to assist the WSN in collecting *** the current UAV-assisted WSN,the clustering and routing schemes are determined ***,such a separate consideration might not maximize the lifetime of the whole WSN due to the mutual coupling of clustering and *** efficiently prolong the lifetime of the WSN,we propose an integrated clustering and routing scheme that jointly optimizes the clustering and routing *** the whole network space,it is intractable to efficiently obtain the optimal integrated clustering and routing ***,we propose the Monte-Las search strategy based on Monte Carlo and Las Vegas ideas,which can generate the chain matrix to guide the algorithm to find the solution *** point-to-point collection leads to long collection paths,so a triangle optimization strategy is then proposed that finds a compromise path to shorten the collection path based on the geometric distribution and energy of sensor *** avoid the coverage hole caused by the death of sensor nodes,the deployment of mobile sensor nodes and the preventive mechanism design are *** emergency data transmission mechanism is further proposed to reduce the latency of collecting the latency-sensitive data due to the absence of *** with the existing schemes,the proposed scheme can prolong the lifetime of the UAVassisted WSN at least by 360%,and shorten the collection path of UAVs by 56.24%.

关键词： Monte-Las search strategy triangle path optimization unmanned aerial vehicles wireless sensor networks

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Exploiting demand-side heterogeneous flexible resources in risk management of power system frequency

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Science China(Technological Sciences) 2024年第5期67卷 1612-1627页

作者： YAO Yu SONG YongHua YE ChengJin DING Yi ZHAO YuMing college of Electrical Engineering Zhejiang UniversityHangzhou310027China State Key Laboratory of Internet of things for Smart City University of MacaoMacao SAR519000China Shenzhen Power Supply Bureau Co. Ltd.Shenzhen518051China

More demand-side flexible resources(DFRs)are participating in the frequency regulation of renewable power systems,whose heterogeneous characteristics have a significant impact on the system frequency ***,selecting suitable DFRs poses a formidable challenge for independent system operators(ISO).In this paper,a reserve allocation methodology for heterogeneous DFRs is proposed to manage the risk of power system ***,a performance curve is developed to describe the cost,capacity,and response speed of ***,a clustering method for multiple distributed DFRs is conducted to calculate the aggregated performance curves and uncertainty ***,the frequency security criterion considering DFRs’performance is constructed,whose linearity makes it can be easily coupled into the system scheduling model and ***,a risk management model for DFRs considering frequency-chance-constraint is proposed to make a trade-off between cost and frequency ***,the model is transformed into mixed integer second-order cone programming(MISOCP)and solved by the commercial *** proposed model is validated by the IEEE 30 and IEEE 118 bus systems.

关键词： demand-side heterogenous flexible resources risk management of power system frequency performance curve aggregation method mixed integer second-order cone programming

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A Survey on Model Predictive Control of DFIGs in Wind Energy Conversion Systems

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1085-1104页

作者： Shuo Yan Junyu Chen Minghao Wang Yongheng Yang Jose M.Rodriguez STEM college RMIT UniversityMelbourneAustralia Hong Kong Polytechnic University Hong KongChina State Key Laboratory of Internet of things for Smart City(UM) Department of Electrical and Computer EngineeringUniversity of MacaoMacaoChina college of Electrical Engineering Zhejiang UniversityHangzhou 310027China Universidad San Sebastin Campus BellavistaSantiago de ChileCLSantiago de Chile 000000Chile IEEE

This paper presents a comprehensive study on the model predictive control(MPC)of doubly fed induction generators(DFIG)in wind energy conversion systems(WECS);in particular the MPC of the rotor side *** general principle of prevalent MPC strategies is discussed to introduce the theoretical framework in the first ***,mainstream and high-performance MPC methods of DFIGs have been identified as model predictive current control(MPCC),model predictive torque control(MPTC),and model predictive power control(MPPC).Starting from analyzing dynamic models,these MPC strategies are investigated in terms of operating principles and technical *** paper further investigates implementation of MPC strategies in unbalanced *** on the quantitative and qualitative analysis of several case studies,performance of various MPC schemes is compared and their special features are *** for designing MPC strategies of DFIGs in WECS are provided.

关键词： Doubly fed induction generator(DFIG) model predictive control(MPC) wind energy

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

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China Communications 2024年第3期21卷 137-148页

作者： Lu Yin Xue Yongtao Li Qingyuan Wu Luocheng Li Taosen Yang Peipei Zhu Hongbo college of Internet of things Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications) Ministry of EducationNanjing 210003China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China Research Institute of Internet of things Nanjing University of Posts and TelecommunicationsNanjing 210003China Bell Honors School Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Automation Nanjing University of Posts and TelecommunicationsNanjing 210023China

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

关键词： blockchain data sharing Internet of things Oracle

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Simulation and forecasting of fishery weather based on statistical machine learning

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Information Processing in Agriculture 2024年第1期11卷 127-142页

作者： Xueqian Fu Chunyu Zhang Fuhao Chang Lingling Han Xiaolong Zhao Zhengjie Wang Qiaoyu Ma National Innovation Center for Digital Fishery China Agricultural UniversityBeijingChina Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural AffairsBeijingChina Beijing Engineering and Technology Research Center for Internet of things in Agriculture BeijingChina college of Information and Electrical Engineering China Agricultural UniversityBeijingChina

As the new generation of artificial intelligence(AI)continues to evolve,weather big data and statistical machine learning(SML)technologies complement each other and are deeply integrated to significantly improve the processing and forecasting accuracy of fishery *** fishery weather services play a crucial role in fishery production,serving as a great safeguard for economic benefits and personal safety,enabling fishermen to carry out fishery production better,and contributing to the sustainable development of the fishery *** objective of this paper is to offer an understanding of the present state of research and development in SML technology for simulating and forecasting fishery ***,we analyze the current state of research and technical features of SML in weather and summarize the applications of SML in simulation and forecasting of fishery weather,which mainly include three aspects:fishery weather scenario generation,fishery weather forecasting,and fishery extreme weather *** also illustrate the main technical means and principles of SML ***,we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.

关键词： Fishery weather Statistical machine learning Weather generation Weather forecasting Extreme weather warning

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Crop pest image recognition based on the improved ViT method Author links open overlay panel

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Information Processing in Agriculture 2024年第2期11卷 249-259页

作者： Xueqian Fu Qiaoyu Ma Feifei Yang Chunyu Zhang Xiaolong Zhao Fuhao Chang Lingling Han college of Information and Electrical Engineering China Agricultural UniversityBeijingChina National Innovation Center for Digital Fishery China Agricultural UniversityBeijingChina Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural AffairsBeijingChina Beijing Engineering and Technology Research Center for Internet of things in Agriculture BeijingChina

The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability and sustainable ***,the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low *** recognition method based on pattern recognition and deep learning can automatically fit image features,and use features to classify and predict *** study introduced the improved Vision Transformer(ViT)method for crop pest image *** them,the region with the most obvious features can be effectively selected by block *** self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion *** the experiment,data with 7 classes of examples are used for *** can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology,accurately judge the crop diseases and pests category,provide method reference for agricultural diseases and pests identification research,and further optimize the crop diseases and pests control work for agricultural workers in need.

关键词： Crop diseases recognition Fruit tree leaf disease Vision Transformer Deep learning Computer Vision

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