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Computing Resource Allocation for Blockchain-Based Mobile Edge Computing

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 863-885页

作者： Wanbo Zhang Yuqi Fan Jun Zhang xu ding Jung Yoon Kim School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui ProvinceHefei University of TechnologyHefei230601China Anhui Province Key Lab of Aerospace Structural Parts Forming Technology and Equipment Hefei University of TechnologyHefei230009China College of Future Industry Gachon UniversitySeongnam13120South Korea

Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

关键词： Mobile edge computing blockchain resource allocation

Density of excess modes below the first phonon mode in four-dimensional glasses

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Chinese Physics B 2024年第7期33卷 483-489页

作者：王利近胥鼎张世允 School of Physics and Optoelectronic Engineering Anhui UniversityHefei 230601China Department of Physics University of Science and Technology of ChinaHefei 230026China

Glasses are known to possess low-frequency excess modes beyond the Debye prediction.For decades,it has been assumed that evolution of low-frequency density of excess modes D(ω) with frequency ω follows a power-law scaling:D(ω)~ω~γ.However,it remains debated on the value of γ at low frequencies below the first phonon-like mode in finitesize glasses.Early simulation studies reported γ=4 at low frequencies in two-(2D),three-(3D),and four-dimensional(4D)glasses,whereas recent observations in 2D and 3D glasses suggested γ=3.5 in a lower-frequency regime.It is uncertain whether the low-frequency scaling of D(ω)~ω~(3.5) could be generalized to 4D glasses.Here,we conduct numerical simulation studies of excess modes at frequencies below the first phonon-like mode in 4D model glasses.It is found that the system size dependence of D(ω) below the first phonon-like mode varies with spatial dimensions:D(ω) increases in2D glasses but decreases in 3D and 4D glasses as the system size increases.Furthermore,we demonstrate that the ω~(3.5)scaling,rather than the ω~4 scaling,works in the lowest-frequency regime accessed in 4D glasses,regardless of interaction potentials and system sizes examined.Therefore,our findings in 4D glasses,combined with previous results in 2D and 3D glasses,suggest a common low-frequency scaling of D(ω)~ ω~(3.5) below the first phonon-like mode across different spatial dimensions,which would inspire further theoretical studies.

关键词： vibrational density of states excess modes four-dimensional glasses scaling computer simulation

Different types of drug abusers prefrontal cortex activation patterns and based on machine-learning classification

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Journal of Innovative Optical Health Sciences 2022年第2期15卷 83-92页

作者： Banghua Yang xuelin Gu Shouwei Gao Lin Feng Yan ding xu Wen Wang School of Mechanical and Electrical Engineering and Automation Shanghai University Shanghai 200444P.R.China Shanghai Drug Rehabilitation Administration Bureau Shanghai 200080 P.R.China Department of Radiology&Functional and Molecular Imaging Key Lab of Shaanxi Province Tangdu Hospital Fourth Military Medical University Xi'anShaanxi 710038P.R.China Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of Education ShanghaiP.R.China Shanghai Intelligent Engineering Technology Research Center for Addiction and Rehabilitation Minhang District 200240 ShanghaiP.R.China

Drug addiction can cause abnormal brain activation changes,which are the root cause of drug craving and brain function errors.This study enrolled drug abusers to determine the effects of different drugs on brain activation.A functional near-infrared spectroscopy(fNIRS)device was used for the research.This study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings.We collected the fNIRS data of 30 drug users,including 10 who used heroin,10 who used Methamphetamine,and 10 who used mixed drugs.First,using Statistical Analysis,the study analyzed the activations of eight functional areas of the left and right hemispheres of the prefrontal cortex of drug addicts who respectively used heroin,Methamphetamine,and mixed drugs,including Left/Right-Dorsolateral prefrontal cortex(L/R-DLPFC),Left/Right-Ventrolateral prefrontal cortex(L/R-VLPFC),Left/Right-Fronto-polar prefrontal cortex(L/R-FPC),and Left/Right Orbitofrontal Cortex(L/R-OFC).Second,referencing the degrees of activation of oxyhaemoglobin concentration(HbO2),the study made an analysis and got the specific activation patterns of each group of the addicts.Finally,after taking out data which are related to the addicts who recorded high degrees of activation among the three groups of addicts,and which had the same channel numbers,the paper classified the different drug abusers using the data as the input data for Convolutional Neural Networks(CNNs).The average three-class accuracy is 67.13%.It is of great significance for the analysis of brain function errors and personalized rehabilitation.

关键词： Drug addiction fNIRS machine-learning di®erent drug users brain regions activation

BCI+VR Rehabilitation Design of Closed-Loop Motor Imagery Based on the Degree of Drug Addiction

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China Communications 2022年第2期19卷 62-72页

作者： xuelin Gu Banghua Yang Shouwei Gao Honghao Gao Linfeng Yan ding xu Wen Wang School of Mechanical and Electrical Engineering and Automation Shanghai UniversityShanghai 200444China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Department of Radiology&Functional and Molecular Imaging Key Lab of Shaanxi Province Tangdu HospitalFourth Military Medical UniversityXi’anShaanxi 710038China Shanghai Drug Rehabilitation Administration Bureau Shanghai 200080China

After abusing drugs for long,drug users will experience deteriorated self-control cognitive ability,and poor emotional regulation.This paper designs a closed-loop virtual-reality(VR),motorimagery(MI)rehabilitation training system based on brain-computer interface(BCI)(MI-BCI+VR),aiming to enhance the self-control,cognition,and emotional regulation of drug addicts via personalized rehabilitation schemes.This paper is composed of two parts.In the first part,data of 45 drug addicts(mild:15;moderate:15;and severe:15)is tested with electroencephalogram(EEG)and near-infrared spectroscopy(NIRS)equipment(EEG-NIRS)under the dual-mode,synchronous signal collection paradigm.Using these data sets,a dual-modal signal convolutional neural network(CNN)algorithm is then designed based on decision fusion to detect and classify the addiction degree.In the second part,the MIBCI+VR rehabilitation system is designed,optimizing the Filter Bank Common Spatial Pattern(FBCSP)algorithm used in MI,and realizing MI-EEG intention recognition.Eight VR rehabilitation scenes are devised,achieving the communication between MI-BCI and VR scene models.Ten subjects are selected to test the rehabilitation system offline and online,and the test accuracy verifies the feasibility of the system.In future,it is suggested to develop personalized rehabilitation programs and treatment cycles based on the addiction degree.

关键词： drug addiction degree brain-computer interface,motor imagery virtual reality drug addiction rehabilitation

Secure communication in wireless powered communication networks with energy accumulation

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Science China(Information Sciences) 2021年第10期64卷 263-265页

作者： ding xu Wireless Communication Key Lab of Jiangsu Province Nanjing University of Posts and Telecommunications

Dear editor,Wireless powered communication network(WPCN) is a wireless network where user devices are powered wirelessly by harvesting radio frequency(RF) signals transmitted by dedicated power stations or nearby power sources [1].WPCN is attractive for wireless networks with low-power user devices, such as wireless sensor networks [2] and backscatter networks [3]. Meanwhile,

关键词： Wireless powered communication network secure communication eavesdropping energy accumulation energy harvesting

Cooperative resource allocation in cognitive wireless powered communication networks with energy accumulation and deadline requirements

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Science China(Information Sciences) 2019年第8期62卷 145-158页

作者： ding xu Qun LI Wireless Communication Key Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Nanjing University of Posts and Telecommunications

This study investigates a multi-carrier cognitive wireless powered communication network(CWPCN) with a wirelessly powered primary user(PU). A two-stage cooperative protocol between the PU and the secondary user(SU) is adopted so that the PU can harvest energy from the SU while the SU gains transmission opportunities. It is assumed that the energy harvested by the PU can be accumulated for future usage, and the quality of service of the PU is guaranteed by satisfying the required minimum number of data bits for a given deadline. Herein, we maximize the SU rate by considering the time allocation,subcarrier allocation, and power allocation in both an offline setting(in which the future channel gains are known a priori) and an online setting(in which only the current channel gains are known). In the offline and online schemes, the maximization problem is solved using the block-coordinate descent method and the Lagrange duality method. The effectiveness of the proposed schemes is evaluated and verified via simulation experiments against benchmark schemes.

关键词： wireless powered communication networks energy harvesting cognitive radio deadline constraint energy accumulation

Price-based time and energy allocation in cognitive radio multiple access networks with energy harvesting

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Science China(Information Sciences) 2017年第10期60卷 258-260页

作者： ding xu Qun LI Wireless Communication Key Lab of Jiangsu Province Nanjing University of Posts and Telecommunications

Cognitive radio(CR),a concept that can solve the conflict between spectrum scarcity and low spectrum utilization,has been researched widely recently[1].There are mainly three CR models:interweave,overlay and underlay[... 详细信息

关键词： CBS Price-based time and energy allocation in cognitive radio multiple access networks with energy harvesting

基于Gabor时频滤波的电磁超声螺栓轴力测量

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应用声学 2024年第3期43卷 584-590页

作者：苏杰庆丁旭武汉科技大学机械传动与制造工程湖北省重点实验室武汉430081 武汉科技大学精密制造研究院武汉430081

高强度螺栓轴向预紧力(轴力)的测量在工程应用中具有重要意义。使用电磁超声波对螺栓轴力检测时对超声回波声时测量精度要求较高,传统互相关估计法对超声回波声时估计易因噪声干扰发生估计错误,无法满足轴力测量精度要求。针对互相关法... 详细信息

高强度螺栓轴向预紧力(轴力)的测量在工程应用中具有重要意义。使用电磁超声波对螺栓轴力检测时对超声回波声时测量精度要求较高,传统互相关估计法对超声回波声时估计易因噪声干扰发生估计错误,无法满足轴力测量精度要求。针对互相关法对电磁超声测量信号声时估计存在不准确的问题,提出了Gabor时频滤波法。通过螺栓轴力测量实验采集测量信号,对测量信号进行Gabor变换,在时频域中进行滤波,再对去噪后的信号进行互相关估计测得信号的声时,进而计算出螺栓轴力。实验表明:Gabor时频滤波法能有效地滤除电磁超声信号中的噪声,改善互相关估计的稳定性,提高螺栓轴力测量的准确率。

关键词：螺栓轴力互相关声时电磁超声信号 Gabor变换

Resource allocation in cognitive wireless powered communication networks with wirelessly powered secondary users and primary users

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Science China(Information Sciences) 2019年第2期62卷 224-229页

作者： ding xu Qun LI Wireless Communication Key Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Jiangsu Key Lab of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications

Dear editor,Wireless powered communication networks(WPCNs) are popular especially for wireless sensor networks where sensors can be wirelessly powered. For coordinating wireless power and information transfer, Ju and Zhang in their pioneer work [1] proposed a "harvest-then-transmit" protocol for WPCN, where the time for wireless power transfer (WPT) and wireless information transfer(WIT) is divided into two phases:the WPT phase and WIT phase. Meanwhile, cognitive radio (CR)

关键词： WIT Resource allocation in cognitive wireless powered communication networks with wirelessly powered secondary users and primary users