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    25 October 2018, Volume 39 Issue 10
    Papers
    Performance analysis and testing of personal influence algorithm in online social networks
    Yong QUAN,Yan JIA,Liang ZHANG,Zheng ZHU,Bin ZHOU,Binxing FANG
    2018, 39(10):  1-10.  doi:10.11959/j.issn.1000-436x.2018217
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    Social influence is the key factor to drive information propagation in online social networks and can be modeled and analyzed with social networking data.As a kind of classical personal influence algorithm,two parallel implementation versions of a PageRank based method were introduced.Furthermore,extensive experiments were conducted on a large-scale real dataset to test the performance of these parallel methods in a distributed environment.The results demonstrate that the computational efficiency of the personal influence algorithm can be improved significantly in massive data sets by virtue of existing big data processing framework,and provide an empirical reference for the future research and optimization of the algorithm as well.

    Stochastic algorithm for HDFS data theft detection based on MapReduce
    Yuanzhao GAO,Binglong LI,Xingyuan CHEN
    2018, 39(10):  11-21.  doi:10.11959/j.issn.1000-436x.2018222
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    To address the problems of big data efficient analysis and insider theft detection in the data theft detection of distributed cloud computing storage,taking HDFS (hadoop distributed file system) as a case study,a stochastic algorithm for HDFS data theft detection based on MapReduce was proposed.By analyzing the MAC timestamp features of HDFS generated by folder replication,the replication behavior’s detection and measurement method was established to detect all data theft modes including insider theft.The data set which is suitable for MapReduce task partition and maintains the HDFS hierarchy was designed to achieve efficient analysis of large-volume timestamps.The experimental results show that the missed rate and the number of mislabeled folders could be kept at a low level by adopting segment detection strategy.The algorithm was proved to be efficient and had good scalability under the MapReduce framework.

    Path planning algorithm for WCE with joint energy replenishment and data collection based on multi-objective optimization
    Zhenchun WEI,Renhao SUN,Zengwei LYU,Jianghong HAN,Lei SHI,Junyi XU
    2018, 39(10):  22-33.  doi:10.11959/j.issn.1000-436x.2018216
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    Considering limited energy of the wireless charging equipment (WCE) in wireless rechargeable sensor network,an energy replenishment strategy and a data collection strategy are designed.On the basis of these,a path planning model for WCE with functions of joint energy replenishment and data collection based on multi-objective optimization is constructed with two optimization objectives,maximizing the total energy utility of WCE and minimizing the average delay of data transmission of all the sensor nodes in the network.To deal with it,a multi-objective ant colony optimization algorithm based on elitist strategy was proposed,where the state transition strategy and the pheromone updating strategy were improved.Then,the Pareto set was obtained in terms of this multi-objective optimization problem.The parameter setting of ant colony algorithm’s effects on the proposed algorithm were analyzed under 20 sensor nodes.50 groups of contrastive experiments show that the average number of energy utilization obtained by ES-MOAC algorithm is 4.53% higher than that of NSGA-II algorithm.The average number of average delay of all node data transmission obtained by ES-MOAC algorithm is 5.12% lower than that of NSGA-II algorithm.

    Cancellation performance of co-site antenna interference cancellation system for AM interference
    Yunhao JIANG,Nan ZHAO,Cong LIU,Wenfang DING,Ziqiang XI
    2018, 39(10):  34-42.  doi:10.11959/j.issn.1000-436x.2018214
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    The interference cancellation performance of actual co-site antenna interference cancellation system (CICS) under amplitude modulation jamming signal was analyzed.The expressions of the steady-state weights and theirs structure characteristics of the interference cancellation system under the AM signal were given.The formulae of the interference cancellation ratio (ICR) for carrier frequency and side frequency were derived.The influence factors and the influence rules of the ICR were analyzed.The steady state weights of the interference cancellation system (ICS) were the weighting of the carrier frequency optimal weights and the edge frequency optimal weights according to the power ratio.The increase of the signal bandwidth will increase the discreteness of the carrier frequency optimal weights and the edge frequency optimal weights and lead to the decrease of the interference cancellation ratio (ICR).The decrease degree of the edge frequency ICR was greater than that of the carrier frequency ICR.To improve ICR by increasing the gain was limited.The relationship between the interference cancellation ratio limit and the equivalent transmission delay was given.The design method of the interference cancellation system to improve the ICR under the AM signal was proposed.Finally,the correctness and validity of the theoretical analysis was verified by simulation.

    Real-time prediction and updating method for LEO satellite handover based on time evolving graph
    Xin HU,Hangyu SONG,Shuaijun LIU,Xiuhua LI,Weidong WANG,Chunting WANG
    2018, 39(10):  43-51.  doi:10.11959/j.issn.1000-436x.2018166
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    In order to solve the problem of low handover efficiency raised by the interwined impacts of time-varying topology of LEO satellites and terminal movement,a real-time satellite handover prediction framework based on time evolving graph and a shortest path dynamic updating method was proposed.This framework dynamically updated the handover prediction results from the temporal and spatial dimensions according to the terminal movement.The simulation results show that the framework can effectively improve the accuracy of the forecast and avoid the unnecessary handover.

    Blind estimation of spreading codes for multi-antenna LC-DS-CDMA signals based on tensor decomposition
    Zhijin ZHAO,Hui YIN,Fangfang QIANG,Junna SHANG
    2018, 39(10):  52-58.  doi:10.11959/j.issn.1000-436x.2018212
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    Aiming at the problem of the poor performance of the imputation method for the spreading codes blind estimation of the multi-antenna long-code direct sequence code division multiple access (CDMA) signals.Firstly,using the segmentation idea,the received signals were constructed into a three-dimensional tensor model,and the tensor was divided into sub-tensors according to the spreading gain.The sub-tensors were constructed into the Tucker decomposition model.Secondly,the spreading code fragments and the receiver gain matrixes were obtained from the sub-tensors by Tucker decomposition which was using the variable step gradient descending algorithm.Then,because of the same characteristic of the receiver gain matrixes in each sub-tensor,the permutation matrixes were estimated by using the cross correlation matrixes of the receiver gain matrixes and the order of the spreading code fragments was corrected.Finally,the amplitude of the spreading codes was determined by using the autocorrelation of the spreading codes,and each user’s spreading code was obtained.The simulation results verify the effectiveness of the proposed algorithm.

    Ant colony algorithm of partially optimal programming based on dynamic convex hull guidance for solving TSP problem
    Xuesen MA,Shuai GONG,Jian ZHU,Hao TANG
    2018, 39(10):  59-71.  doi:10.11959/j.issn.1000-436x.2018218
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    To solve basic ant colony algorithm’s drawbacks of large search space,low convergence rate and easiness of trapping in local optimal solution,an ant colony algorithm of partially optimal programming based on dynamic convex hull guidance was proposed.The improved algorithm dynamically controlled the urban selection range of the ants,which could reduce the search space of ants on basis of helping the algorithm to jump out of local optimal solution to global optimal solution.Meanwhile,the delayed drift factor and the convex hull constructed by the cities to be chosen were introduced to intervene the current ants’ urban choice,it could increase the diversity of the early solution of the algorithm and improve the ability of ants’ partially optimal programming.Then the pheromone updating was coordinated by using construction information of convex hull and the complete path information that combined local with whole,it could improve the accuracy of the algorithm by guiding the subsequent ants to partially optimal programming.The pheromone maximum and minimum limit strategy with convergence was designed to avoid the algorithm’s premature stagnation and accelerate the solving speed of the algorithm.Finally,the proposed algorithm was applied to four classic TSP models.Simulation results show that the algorithm has better optimal solution,higher convergence rate and better applicability.

    Quantum secret sharing with graph states based on Chinese remainder theorem
    Jianwu LIANG,Xiaoshu LIU,Zi CHENG
    2018, 39(10):  72-78.  doi:10.11959/j.issn.1000-436x.2018220
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    Based on the topological features of quantum graph states,a quantum secret sharing scheme based on Chinese remainder theorem with a vivid graphic description was proposed.The dealer extracts sub-secrets according to Chinese remainder theorem over finite field,which were imbedded with quantum graph states and transmitted to the legal participants with unitary operations.Group-recovery protocols were used in the secret recovering processing through rebuilding sub-secrets among legal cooperative participants.Analysis shows that it could provide better security and capability of the information.

    Blind recognition of RSC based on logarithmic conformity
    Zhaogen ZHONG,Zhaojun WU,Limin ZHANG,Zhiqing WANG
    2018, 39(10):  79-86.  doi:10.11959/j.issn.1000-436x.2018211
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    A new algorithm based on logarithmic conformity was proposed.Firstly,based on the probability of total RSC coding equation,the concept of logarithmic coincidence degree,which could measure the establishment of coding equation,was introduced.Then because of constraint length of RSC,the polynomial database could be generated,and then logarithmic conformity of every polynomial could be calculated when traversing the database.As the results,the RSC could be recognized,because the correct polynomial could make the conformity maximum.The algorithm has small amount of calculation because of finite traversal,which was only related to amount of intercepted data,besides,this algorithm has good error tolerance by soft decisions.The simulation results show that the correct ratio of recognition can reach 90% at SNR of 0 dB and its performances are obviously superior to those of existing algorithms.

    WSN key recovery attack based on symmetric matrix decomposition
    Xiangmin JI,Bo ZHAO,Jinhui LIU,Jianwei JIA,Huanguo ZHANG,Shuang XIANG
    2018, 39(10):  87-96.  doi:10.11959/j.issn.1000-436x.2018221
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    The key protocol is one of the crucial technologies to ensure the security for wireless sensor network(WSN).Parakh,et al.proposed a key agreement for WSN based on matrix decomposition.However,the study revealed that the protocol had security risks.A key recovery attack scheme against this protocol was proposed by using the properties of symmetric matrix and permutation matrix.Based on intercepting the row and column vector of the node,elementary transformation was performed to construct a linear algebraic attack algorithm and the equivalent key was obtained.The computational complexity is O(N6).Experimental results show that the method can recover the equivalent key of the above protocol within the polynomial computational complexity and the memory consumption is within an acceptable range.In addition,an improved scheme for key agreement was proposed to resist the linear algebraic attack by using a random disturbance matrix,and the correctness and security analysis were also carried out.

    Energy-efficient strategy for work node by DRAM voltage regulation in storm
    Yonglin PU,Jiong YU,Liang LU,Chen BIAN,Bin LIAO,Ziyang LI
    2018, 39(10):  97-117.  doi:10.11959/j.issn.1000-436x.2018213
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    Focused on the problem that traditional energy-efficient strategies never consider about the real time of data processing and transmission,models of directed acyclic graph,parallelism of instance,resource allocation for task and critical path were set up based on the features of data stream processing and the structure of storm cluster.Meanwhile,the WNDVR-storm (energy-efficient strategy for work node by dram voltage regulation in storm) was proposed according to the analysis of critical path and system performance,which included two energy-efficient algorithms aiming at whether there were any work nodes executing on the non-critical path of a topology.Finally,the appropriate threshold values fit for the CPU utilization of work node and the volume of transmitted data were determined based on the data processing and transmission constraints to dynamically regulate the DRAM voltage of the system.The experimental result shows that the strategy can reduce energy consumption effectively.Moreover,the fewer constraints are,the higher energy efficiency is.

    Comprehensive Reviews
    State-of-the-art survey of privacy-preserving data aggregation in wireless sensor networks
    Xiaoying ZHANG,Hui PENG,Hong CHEN
    2018, 39(10):  130-142.  doi:10.11959/j.issn.1000-436x.2018101
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    A state-of-the-art survey of privacy-preserving data aggregation techniques in wireless sensor networks was reviewed.Firstly,preliminaries were introduced,including network models,adversary models,and performance evaluation metrics.Secondly,existing related work was classified into several types according to privacy preservation techniques,such as homomorphic encryption,data perturbation,slicing-mixing technique,generalization,secure multiparty computation,and the key mechanisms of typical protocols were elaborated and analyzed.Finally,the promising future research directions were discussed.

    Correspondences
    Markov game modeling of mimic defense and defense strategy determination
    Xingming ZHANG,Zeyu GU,Shuai WEI,Jianliang SHEN
    2018, 39(10):  143-154.  doi:10.11959/j.issn.1000-436x.2018223
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    Network mimic defense technology enhances the robustness of active defense through the redundancy,dynamic and diversity as well as the decision feedback mechanism.However,little work has been done for its security assessment and existing classic game models are not suitable for its dynamic characteristics and lack of universality.A Markov game model was proposed to analyze the transfer relationship between offensive and defensive status and the measurement method of safety and reliability of mimic defense,and calculated the offensive and defensive game equilibrium through non-linear programming algorithm to determine the best defensive strategy considering performance.Experiments give a comparison with the multi-target hiding technique and shows that the mimic defense has a higher defensive effect.Combining with the specific network case,the specific attack and defense path for the exploit of the system vulnerability is given and the effectiveness of the defense strategy algorithm is verified.

    Multitier ensemble classifiers for malicious network traffic detection
    Jie WANG,Lili YANG,Min YANG
    2018, 39(10):  155-165.  doi:10.11959/j.issn.1000-436x.2018224
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    A malicious network traffic detection method based on multi-level distributed ensemble classifier was proposed for the problem that the attack model was not trained accurately due to the lack of some samples of attack steps for detecting attack in the current network big data environment,as well as the deficiency of the existing ensemble classifier in the construction of multilevel classifier.The dataset was first preprocessed and aggregated into different clusters,then noise processing on each cluster was performed,and then a multi-level distributed ensemble classifier,MLDE,was built to detect network malicious traffic.In the MLDE ensemble framework the base classifier was used at the bottom,while the non-bottom different ensemble classifiers were used.The framework was simple to be built.In the framework,big data sets were concurrently processed,and the size of ensemble classifier was adjusted according to the size of data sets.The experimental results show that the AUC value can reach 0.999 when MLDE base users random forest was used in the first layer,bagging was used in the second layer and AdaBoost classifier was used in the third layer.

    Markov prediction based handover in dynamic ultra dense network
    Qingmin MENG,Yuanyuan ZHAO,Wenjing YUE,Yulong ZOU,Xiaoming WANG
    2018, 39(10):  166-174.  doi:10.11959/j.issn.1000-436x.2018225
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    In order to solve the problem of the communication and computational problems of large-scale machine communication in an ultra-dense cellular network,a Markov prediction based handover scheme (MPHS) was proposed.Firstly,a kind of heterogeneous network design with semi-structure and central control was considered which contains the densely deployed virtual nodes and thus realized a low cost and efficient coverage.The network can dynamically adjust the access point according to the user's mobility and network traffic.Secondly,a Markov model was constructed,and the idea of load-aware was introduced.By weighing the signal quality and the cell load,the user's next optimal access point was effectively predicted.The simulation results show the feasibility and effectiveness of the proposed scheme for cell handover predicting.

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
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53859522、010-53878236
Email: xuebao@ptpress.com.cn
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ISSN 1000-436X
CN 11-2102/TN
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