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    25 October 2020, Volume 41 Issue 10
    Topics: Convergence of Communications and Computing for the IoE
    Resource management in blockchain-enabled heterogeneous edge computing system
    Ping ZHANG,Shilin LI,Yiming LIU,Xiaoqi QIN,Xiaodong XU
    2020, 41(10):  1-14.  doi:10.11959/j.issn.1000-436x.2020206
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    In blockchain-enabled mobile edge computing (BMEC) systems,a new class of blockchain application related computation tasks was introduced to the system.Due to the differences of parallelism among computation tasks,heterogenous computing framework was introduced to suitably split various computation tasks on processors with vastly different processing power to achieve efficient task execution.Under the limited computation and communication resources,a system-wide utility maximization problem by jointly considering heterogeneous processor scheduling,computation and bandwidth resource allocation was formulated as a mixed-integer nonlinear programming problem.To solve the problem efficiently,the formulated problem was transformed into two sub-problems,namely application-driven heterogeneous processor scheduling and joint resource allocation,and a Lagrange-dual based algorithm was proposed.Simulation results show that the proposed scheme can effectively improve the system-wide utility of the BMEC system.

    Algorithm design on energy efficiency maximization for UAV-assisted edge computing
    Qihui WU,Wei WU
    2020, 41(10):  15-24.  doi:10.11959/j.issn.1000-436x.2020204
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    For the unmanned aerial vehicle (UAV)-assisted edge computing system,a two-stage alternative algorithm was proposed to solve the formulated complex non-convex problem.Firstly,the formulated non-linear fractional programming problem was reformulated to the equivalent parametric problem by using Dinkelbach method.Secondly,two sub-problems were further considered based on it.By employing the Lagrange duality method,the closed-form solutions for the central processing unit frequencies and the number of data bits were derived.Finally,based on the solutions obtained,the conditions that the source node prefers to offload/share its data and the relay chooses to forward the computation results,as well as the approaches to achieve high energy efficiency were revealed.Numerical results demonstrate that the proposed design can achieve a performance improvement of up to 20 times over the conventional schemes.

    Multiuser computation offloading for edge-cloud collaboration using submodular optimization
    Bing LIANG,Wen JI
    2020, 41(10):  25-36.  doi:10.11959/j.issn.1000-436x.2020205
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    A computation offloading scheme based on edge-cloud computing was proposed to improve the system utility of multiuser computation offloading.This scheme improved the system utility while considering the optimization of edge-cloud resources.In order to tackle the problems of computation offloading mode selection and edge-cloud resource allocation,a greedy algorithm based on submodular theory was developed by fully exploiting the computing and communication resources of cloud and edge.The simulation results demonstrate that the proposed scheme effectively reduces the delay and energy consumption of computing tasks.Additionally,when computing tasks are offloaded to edge and cloud from devices,the proposed scheme still maintains stable system utilities under ultra-limited resources.

    Service function chain embedding algorithm with wireless multicast in mobile edge computing network
    Kan WANG,Nan ZHAO,Junhuai LI,Huaijun WANG
    2020, 41(10):  37-47.  doi:10.11959/j.issn.1000-436x.2020210
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    To resolve the excessive system overhead and serious traffic congestion in user-oriented service function chain (SFC) embedding in mobile edge computing (MEC) networks,a content-oriented joint wireless multicast and SFC embedding algorithm was proposed for the multi-base station and multi-user edge networks with MEC servers.By involving four kinds of system overhead,including service flow,server function sustaining power,server function service power and wireless transmission power,an optimization model was proposed to jointly design SFC embedding with multicast beamforming.Firstly,with Lagrangian dual decomposition,the problem was decoupled into two independent subproblems,namely,SFC embedding and multicast beamforming.Secondly,with the L<sub>p</sub> norm penalty term-based successive convex approximation algorithm,the integer programming-based SFC embedding problem was relaxed to an equivalent linear programming one.Finally,the non-convex beamforming optimization problem was transformed into a series of convex ones via the path following technique.Simulation results revealed that the proposed algorithm has good convergence,and is superior to both the optimal SFC embedding with unicasting and random SFC embedding with multicasting in terms of system overhead.

    6G oriented blockchain based Internet of things data sharing and storage mechanism
    Yu’na JIANG,Xiaohu GE,Yang YANG,Chengxiang WANG,Jie LI
    2020, 41(10):  48-58.  doi:10.11959/j.issn.1000-436x.2020211
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    Considering the heterogeneity of various IoT system and the single point failure of centralized data-processing platform,a decentralized IoT data sharing and storage method based on blockchain technology was proposed.The block consensus and decentralized storage of shared data were realized through the PoS consensus mechanism.A block layered propagation mechanism between consensus node and verified node was proposed based on the Gossip protocol.The block propagation delay model and decentralization evaluation model of blockchain networks were derived.The trade-off between the block propagation delay and the decentralization degree of networks was analyzed.The simulation results demonstrate that the block propagation delay and degree of network decentralization decrease with the increase of minimal capabilities of consensus nodes.As an application example,in the trajectory data sharing scenario of confirmed patients,the data sharing smart contract is implemented and tested based on the Ethereum development platform.

    Computation energy efficiency maximization based resource allocation scheme in wireless powered mobile edge computing network
    Liqin SHI,Yinghui YE,Guangyue LU
    2020, 41(10):  59-69.  doi:10.11959/j.issn.1000-436x.2020182
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    For wireless powered mobile edge computing (MEC) network,a system computation energy efficiency (CEE) maximization scheme by considering the limited computation capacity at the MEC server side was proposed.Specifically,a CEE maximization optimization problem was formulated by jointly optimizing the computing frequencies and execution time of the MEC server and the edge user(EU),the transmit power and offloading time of each EU,the energy harvesting time and the transmit power of the power beacon.Since the formulated optimization problem was a non-convex fractional optimization problem and hard to solve,the formulated problem was firstly transformed into a non-convex subtraction problem by means of the generalized fractional programming theory and then transform the subtraction problem into an equivalent convex problem by introducing a series of auxiliary variables.On this basis,an iterative algorithm to obtain the optimal solutions was proposed.Simulation results verify the fast convergence of the proposed algorithm and show that the proposed resource allocation scheme can achieve a higher CEE by comparing with other schemes.

    Papers
    Cross-dataset person re-identification method based on multi-pool fusion and background elimination network
    Yanfeng LI,Bin ZHANG,Jia SUN,Houjin CHEN,Jinlei ZHU
    2020, 41(10):  70-79.  doi:10.11959/j.issn.1000-436x.2020181
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    The existing cross-dataset person re-identification methods were generally aimed at reducing the difference of data distribution between two datasets,which ignored the influence of background information on recognition performance.In order to solve this problem,a cross-dataset person re-ID method based on multi-pool fusion and background elimination network was proposed.To describe both global and local features and implement multiple fine-grained representations,a multi-pool fusion network was constructed.To supervise the network to extract useful foreground features,a feature-level supervised background elimination network was constructed.The final network loss function was defined as a multi-task loss,which combined both person classification loss and feature activation loss.Three person re-ID benchmarks were employed to evaluate the proposed method.Using MSMT17 as the training set,the cross-dataset mAP for Market-1501 was 35.53%,which was 9.24% higher than ResNet50.Using MSMT17 as the training set,the cross-dataset mAP for DukeMTMC-reID was 41.45%,which was 10.72% higher than ResNet50.Compared with existing methods,the proposed method shows better cross-dataset person re-ID performance.

    Knowledge triple extraction in cybersecurity with adversarial active learning
    Tao LI,Yuanbo GUO,Ankang JU
    2020, 41(10):  80-91.  doi:10.11959/j.issn.1000-436x.2020174
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    Aiming at the problem that using pipeline methods for extracting cybersecurity knowledge triples may cause the errors propagation of entity recognition and did not consider the correlation between entity recognition and relation extraction,and training triple extraction model lacked labeled corpora,an end-to-end cybersecurity knowledge triple extraction method with adversarial active learning was proposed.For knowledge triple extraction,the conventional entity recognition and relation extraction were modelled as sequence labeling task through joint labeling strategy firstly.And then,a BiLSTM-LSTM-based model with dynamic attention mechanism was designed to jointly extract entities and relations,forming triples.Finally,with adversarial learning framework,a discriminator was trained to incrementally select high-quality samples for labeling,and the performance of the joint extraction model was continuously enhanced by iterative retraining.Experiments show that the proposed joint extraction model outperforms the existing cybersecurity knowledge triple extraction methods,and demonstrate the effectiveness of proposed adversarial active learning scheme.

    Load prediction based elastic resource scheduling strategy in Flink
    Ziyang LI,Jiong YU,Yuefei WANG,Chen BIAN,Yonglin PU,Yitian ZHANG,Yu LIU
    2020, 41(10):  92-108.  doi:10.11959/j.issn.1000-436x.2020195
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    In order to solve the problem that the load of big data stream computing platform fluctuates drastically while the cluster was suffering from the performance bottleneck due to the shortage of computing resources,the load prediction based elastic resource scheduling strategy in Flink (LPERS-Flink) was proposed.Firstly,the load prediction model was set up as the foundation to propose the load prediction algorithm and predict the variation tendency of the processing load.Secondly,the resource judgment model was set up to identify the performance bottleneck and resource redundancy of the cluster while the resource scheduling algorithm was proposed to draw up the resource rescheduling plan.Finally,the online load migration algorithm was proposed to execute the resource rescheduling plan and migrate processing load among nodes efficiently.The experimental results show that the strategy provides better performance promotion in the application with drastically fluctuating processing load.The scale and resource configuration of the cluster responded to the variation of processing load in time and the communication overhead of the load migration was reduced effectively.

    Federated learning based intelligent edge computing technique for video surveillance
    Yu ZHAO,Jie YANG,Miao LIU,Jinlong SUN,Guan GUI
    2020, 41(10):  109-115.  doi:10.11959/j.issn.1000-436x.2020192
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    With the explosion of global data,centralized cloud computing cannot provide low-latency,high-efficiency video surveillance services.A distributed edge computing model was proposed,which directly processed video data at the edge node to reduce the transmission pressure of the network,eased the computational burden of the central cloud server,and reduced the processing delay of the video surveillance system.Combined with the federated learning algorithm,a lightweight neural network was used,which trained in different scenarios and deployed on edge devices with limited computing power.Experimental results show that,compared with the general neural network model,the detection accuracy of the proposed method is improved by 18%,and the model training time is reduced.

    Time deterministic routing algorithm and protocol based on time-varying graph over the space-ground integrated network
    Hongyan LI,Tao ZHANG,Jingqian ZHANG,Keyi SHI,Pengcheng ZENG
    2020, 41(10):  116-129.  doi:10.11959/j.issn.1000-436x.2020188
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    In terms of the difficulties to construct the end-to-end path and improve the utilization of network resources,caused by the time-varying multi-dimensional resources and diverse services over the space-ground integrated networks,the time-varying graph-based time deterministic routing algorithm and protocol for the space-ground integrated network was proposed.Firstly,a time-varying continuous graph model was constructed to describe the spatial and temporal attributes of multi-dimensional resources,such as the topology,link connectivity opportunities,node caching and so on.Then,a service-oriented time deterministic routing algorithm was proposed.According to the calculation rules of link cumulative traffic,and the constraints of node flow conservation and cache,the time-varying path with the shortest transmission delay was constructed.After that,a delay-guaranteed time deterministic routing protocol was designed by combining the proposed routing algorithm with both the segment routing technology and the time sensitive network technology,which supported the dynamic topology discovery,the efficient calculation of deterministic routing and the timing forwarding of data packets on the time-varying network.The simulation results show that,compared with the routing algorithm based on snapshot graph and contact graph,the proposed routing algorithm has higher link resource utilization rate and the packet successful delivery rate by jointly using link and node storage resources by association,and ensures the end-to-end transmission delay of services.

    Chaotic sequence based polar code encrypted scheme in negative secrecy capacity case
    Xiaohui ZHANG,Shunliang ZHANG,Bowen LI
    2020, 41(10):  130-138.  doi:10.11959/j.issn.1000-436x.2020187
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    A chaos based encrypted polar coding scheme,which could be applied to the negative secrecy capacity case,was proposed.Chaotic sequences were employed to encrypt the information bits and fill the frozen bits.And multi-block polar coding structure was also employed in the proposed scheme.The proposed scheme was featured as lower complexity and higher secrecy transmission rate.Corresponding mathematical analysis had been performed in terms of the error probability,security and transmission rate.The result reveals that the proposed scheme can achieve reliability,security in negative secrecy capacity case.What’s more,it has relatively low complexity and high secrecy transmission rate compared with the existing schemes.

    Secrecy performance analysis on cooperative CR-NOMA network
    Zhen YANG,Mengyao ZHU,Youhong FENG
    2020, 41(10):  139-147.  doi:10.11959/j.issn.1000-436x.2020180
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    In order to improve the secrecy performance of communication system and make efficient use of limited spectrum,overlay cognitive radio (OCR) technology was combined with non-orthogonal multiple access (NOMA) technology and the communication model was proposed,in which secondary network realized dynamic switching between assisting primary network communication and secondary network communication by sensing whether the primary user occupied the spectrum or not.Artificial noise (AN) aided technology was used in primary and secondary networks respectively to further improve the secrecy performance of the system.The secrecy performance of the system was studied by deducing the expressions of the primary and secondary network secrecy outage probability and secrecy throughput respectively.The simulation results show that the proposed cognitive cooperative NOMA communication scheme is beneficial in reducing secrecy outage probability and increasing secrecy throughput.Furthermore,the influence of AN power allocation factor on system performance is given.

    Research on improving deep space communication using hybrid RF-FSO system
    Hongzhan LIU,Ting JIANG,Yuan HAO
    2020, 41(10):  148-155.  doi:10.11959/j.issn.1000-436x.2020185
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    In order to improve the quality of deep space communication,a mixed RF-FSO system and hybrid LPPM-BPSK-SIM scheme were introduced in deep space.The bit error rate of the hybrid RF-FSO system and the FSO system were compared and analyzed under the impact of solar wind fluctuation.The simulation results indicate that the deep space communication system achieves better bit error rate performance by using hybrid RF-FSO system,and the system performance can be further enhanced by adopting hybrid LPPM-BPSK-SIM.

    Design of precoding matrices in MIMO full duplex two-way security communication system
    Weijia LEI,Yang ZHOU,Xianzhong XIE,Hongjiang LEI
    2020, 41(10):  156-171.  doi:10.11959/j.issn.1000-436x.2020155
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    The design of precoding matrices was studied in the multiple input multiple output (MIMO) full-duplex two-way security communication system where legitimate nodes transmit the confidential information accompanied by the artificial noise while receiving information.For the scenario where the perfect channel state information (CSI) of legitimate channels and eavesdropping channels was available,the difference of concave/convex (DC) programming was used to optimize the precoding matrices of the information signal and the artificial noise for the maximization of the secrecy sum rate.For the scenario where the CSI was imperfect,the channels were modeled by using worst-case criterion and the weighted minimum mean square error algorithm was used to get the robust precoding matrices of the information signal and the artificial noise.The simulation results prove that the proposed algorithm can effectively promote the secrecy sum rate of the system.

    Task distribution offloading algorithm of vehicle edge network based on DQN
    Haitao ZHAO,Tangwei ZHANG,Yue CHEN,Houlin ZHAO,Hongbo ZHU
    2020, 41(10):  172-178.  doi:10.11959/j.issn.1000-436x.2020160
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    In order to achieve the best balance between latency,computational rate and energy consumption,for a edge access network of IoV,a distribution offloading algorithm based on deep Q network (DQN) was considered.Firstly,these tasks of different vehicles were prioritized according to the analytic hierarchy process (AHP),so as to give different weights to the task processing rate to establish a relationship model.Secondly,by introducing edge computing based on DQN,the task offloading model was established by making weighted sum of task processing rate as optimization goal,which realized the long-term utility of strategies for offloading decisions.The performance evaluation results show that,compared with the Q-learning algorithm,the average task processing delay of the proposed method can effectively improve the task offload efficiency.

    Research on the system error performance of coherent orthogonal frequency division multiplexing system with M-distribution in satellite-to-ground laser communication
    Yi WANG,Yaping WANG
    2020, 41(10):  179-187.  doi:10.11959/j.issn.1000-436x.2020197
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    In order to alleviate the influence of atmospheric turbulence on the performance of satellite-to-ground laser communication system,based on the M-distribution atmospheric channel model,a multi-carrier coherent orthogonal frequency division multiplexing (OFDM) modulation system was proposed for uplink and downlink in the satellite-to-ground laser communication.The closed-form expression of bit error rate (BER) of coherent OFDM modulation system was derived.The relationship between the zenith angle,receiving aperture,signal-to-noise ratio (SNR),optimal beam divergence angle,and optimal transmission radius and the BER were studied under weak,and strong atmosphere turbulence,and compared with binary coherent differential phase shift keying (DPSK) modulation.Both the theory and the simulation results show that compared with coherent DPSK modulation,the bit error performance of the coherent OFDM modulation system in the satellite-to-ground laser communication system is better.

    Towards edge-collaborative,lightweight and secure region proposal network
    Jinbo XIONG,Renwan BI,Qianxin CHEN,Ximeng LIU
    2020, 41(10):  188-201.  doi:10.11959/j.issn.1000-436x.2020186
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    Aiming at the problem of image privacy leakage and computing efficiency in edge environment,a lightweight and secure region proposal network (SecRPN) was proposed.A series of secure computing protocols were designed based on the additive secret sharing scheme.Two non-collusive edge servers cooperate to perform calculation modules such as secure feature processing,secure anchor transformation,secure bounding-box correction,and secure non-maximum suppression.Theoretical analysis guarantees the correctness and security of SecRPN.The actual performance evaluation shows that SecRPN is outstanding in the computational cost and communication overhead compared with the existing works.

    Correspondences
    Energy efficiency maximization resource allocation algorithm in wireless-powered backscatter communication network
    Yongjun XU,Bowen GU,Qianbin CHEN,Jinzhao LIN
    2020, 41(10):  202-210.  doi:10.11959/j.issn.1000-436x.2020132
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    In order to alleviate the energy consumption problem caused by the increasing number of Internet of things (IoT) nodes,an energy-efficient (EE) maximization based resource allocation algorithm was proposed for multi-carrier wireless-powered backscatter communication network.Firstly,a multivariable and nonlinear resource allocation model was formulated to jointly optimize transmit power,transmission time,reflection coefficient,and energy-harvesting allocation coefficient,where the maximum transmit power constraint of the power station and the minimum harvested energy constraint at the backscatter device were considered.Then,the original non-convex optimization problem was transformed into a convex one which was solved by using Dinkelbach’s method and the variable substitution approach.Furthermore,the analytical solution of the resource allocation problem was obtained based on Lagrange dual theory.Simulation results verify that the proposed algorithm has better EE by comparing it with the existing algorithm under pure backscatter mode and algorithm under the harvested-then-transmit mode.

    Research on social network influence maximization algorithm based on time sequential relationship
    Jing CHEN,Ziyi QI
    2020, 41(10):  211-221.  doi:10.11959/j.issn.1000-436x.2020191
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    For the time sequential relationship between nodes in a dynamic social network,social network influence maximization based on time sequential relationship was proved.The problem was to find k nodes on a time sequential social network to maximize the spread of information.Firstly,the propagation probability between nodes was calculated by the improved degree estimation algorithm.Secondly,in order to solve the problem that WCM models based on static social networks could not be applied to time sequential social networks,an IWCM propagation model was proposed and based on this,a two-stage time sequential social network influence maximization algorithm was proposed.The algorithm used the time sequential heuristic phase and the time sequential greedy phase to select the candidate node with the largest influence estimated value inf (u) and the most influential seeds.At last,the efficiency and accuracy of the TIM algorithm were proved by experiments.In addition,the algorithm combines the advantages of the heuristic algorithm and the greedy algorithm,reducing the calculation range of the marginal revenue from all nodes in the network to the candidate nodes,and greatly shortens the running time of the program while ensuring accuracy.

    Gamma norm minimization based image denoising algorithm
    Hongyan WANG,Tuo WANG,Mian PAN,Zumin WANG
    2020, 41(10):  222-234.  doi:10.11959/j.issn.1000-436x.2020190
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    Focusing on the issue of rather poor denoising performance of the traditional kernel norm minimization based method caused by the biased approximation of kernel norm to rank function,based on the low-rank theory,a gamma norm minimization based image denoising algorithm was developed.The noisy image was firstly divided into some overlapping patches via the proposed algorithm,and then several non-local image patches most similar to the current image patch were sought adaptively based on the structural similarity index to form the similar image patch matrix.Subsequently,the non-convex gamma norm could be exploited to obtain unbiased approximation of the matrix rank function such that the low-rank denoising model could be constructed.Finally,the obtained low-rank denoising optimization issue could be tackled on the basis of singular value decomposition,and therefore the denoised image patches could be re-constructed as a denoised image.Simulation results demonstrate that,compared to the existing state-of-the-art PID,NLM,BM3D,NNM,WNNM,DnCNN and FFDNet algorithms,the developed method can eliminate Gaussian noise more considerably and retrieve the original image details rather precisely.

Copyright Information
Authorized by: China Association for Science and Technology
Sponsored by: China Institute of Communications
Editor-in-Chief: Zhang Ping
Associate Editor-in-Chief:
Zhang Yanchuan, Ma Jianfeng, Yang Zhen, Shen Lianfeng, Tao Xiaofeng, Liu Hualu
Editorial Director: Wu Nada, Zhao Li
Address: F2, Beiyang Chenguang Building, Shunbatiao No.1 Courtyard, Fengtai District, Beijing, China
Post: 100079
Tel: 010-53933889、53878169、
53859522、010-53878236
Email: xuebao@ptpress.com.cn
Email: txxb@bjxintong.com.cn
ISSN 1000-436X
CN 11-2102/TN
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