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    25 April 2018, Volume 39 Issue 4
    Delay-aware cross-layer optimization method for FANET
    Shaojie WEN, Chuanhe HUANG
    2018, 39(4):  1-12.  doi:10.11959/j.issn.1000-436x.2018070
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    An asynchronous distributed cross-layer optimization (ADCO) method was proposed to solve the problem of jointly considering real-time routing,rate allocation and power control in FANET (flying ad hoc network).And a delay-constrained cross-layer optimization framework was designed to formally represent proposed problem.Then Lagrangian relaxation and dual decomposition methods was used to divide joint optimization problem into several sub-problems.ADCO allowed each relay node to perform the optimization operation for different sub-problems with local information,and the relay nodes could update the dual variables based on asynchronous update mechanism.The simulation results show that the proposed algorithm can improve the network performance effectively in terms of energy efficiency,packet timeout ratio and network throughput.

    Papers
    Research on the scalability of parallel community detection algorithms
    Qiang LIU,Yan JIA,Binxing FANG,Bin ZHOU,Yue HU,Jiuming HUANG
    2018, 39(4):  13-20.  doi:10.11959/j.issn.1000-436x.2018052
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    The social network often contains a large amount of information about users and groups,such as topic evolution mode,group aggregation effect,the law of information dissemination and so on.The mining of these information has become an important task for social network analysis.As one characteristic of the social network,the group aggregation effect is characterized by the community structure of the social network.The discovery of community structure has become the basis and key point of other social network analysis tasks.With the rapid growth of the number of online social network users,the traditional community detection methods have been difficult to be used,which contributes to the development of parallel community detection technology.The current mainstream parallel community detection methods,including Louvain algorithm and label propagation algorithm,were tested in the large-scale data sets,and corresponding advantages and disadvantages were pointed out so as to provide useful information for later applications.

    Mimic storage scheme based on regenerated code
    Yue CHEN,Longjiang WANG,Xincheng YAN,Xinyue ZHANG
    2018, 39(4):  21-34.  doi:10.11959/j.issn.1000-436x.2018066
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    Aiming to solve security threats in the cloud storage system due to static storage architecture and storage mode,a mimic storage scheme based on regenerated code was proposed.The scheme used network coding scheme to store the data in the cloud data node,and used mimicry transformation mechanism based on regeneration code to change data storage state dynamically according to the random time-varying factors,which could guarantee data integrity and data availability continuously.The mimicry transformation mechanism is a random,time-varying and dynamic scheme,which increases the uncertainty of storage system.It blocks and interferes with the attack chain,increases the difficulty and cost of the attack operation,and improves the security and reliability of the system.

    Transmission scheduling scheme based on deep Q learning in wireless network
    Jiang ZHU,Tingting WANG,Yonghui SONG,Yali LIU
    2018, 39(4):  35-44.  doi:10.11959/j.issn.1000-436x.2018058
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    To cope with the problem of data transmission in wireless networks,a deep Q learning based transmission scheduling scheme was proposed.The Markov decision process system model was formulated to describe the state transition of the system.The Q learning algorithm was adopted to learn and explore the system states transition information in the case of unknown system states transition probability to obtain the approximate optimal strategy of the schedule node.In addition,when the system state scale was big,the deep learning method was employed to map the relation between state and behavior to solve the problem of the large amount of computation and storage space in Q learning process.The simulation results show that the proposed scheme can approach the optimal strategy based on strategy iteration in terms of power consumption,throughput,packets loss rate.And the proposed scheme has a lower complexity,which can solve the problem of the curse of dimensionality.

    Energy saving proactive neighbor discovery algorithm in mobile low-duty-cycle wireless sensor network
    Junbin LIANG,Xiang ZHOU,Taoshen LI
    2018, 39(4):  45-55.  doi:10.11959/j.issn.1000-436x.2018054
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    Mobile low-duty-cycle wireless sensor network is a new kind of wireless multi-hop network,which is self-organized by a large number of nodes that have mobile ability and are able to get into sleep for a long time.Such networks have wide application prospects in national defense,industry,agriculture and other fields that need long term monitoring in severe environments.However,the movement and the sleeping features of nodes lead to constantly change of network topology,which makes the nodes difficult to discover their neighbors quickly.Therefore,the nodes cannot achieve optimal distribution decisions.In order to solve this problem,a new proactive neighbor discovery algorithm was proposed.This algorithm made the nodes in the network take the initiative to find their neighbors when they woke up,and avoided the delay caused by long time waiting in the traditional passive neighbor discovery.In addition,by predicting the movement speed and distance of neighbors,the neighbor set at the next moment can be quickly determined,which can further reduce the delay and obtain more accurate neighbor discovery results.Theoretical analysis and experimental results show that compared with the existing algorithms,the algorithm can find all the neighbors in MLDC-WSN with less energy consumption and lower delay.

    Research on satellite link emulation for space-ground integration information network
    Yuan LIU,Hao ZHANG,Haiyang YE,Jianfeng LI,Xiaofeng WANG,Guizhu ZHANG
    2018, 39(4):  56-67.  doi:10.11959/j.issn.1000-436x.2018071
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    Aiming at the large scale and diverse technologies of the space-ground integration information network,it was necessary to rely on realistic satellite network scene to verify the various schemes.A method of satellite link emulation in cloud computing environment was proposed.The critical factors that affect the characteristics of satellite link were analyzed,OpenStack and STK were integrated to build an emulation platform for space-ground integration information network.Through the link modeling and calculation,the intermittence,delay and bit error rate of satellite link based on flow table rules and queue management rules can be toggled dynamically in real time.In order to improve the throughput of satellite link,satellite nodes have dynamic routing learning ability.Satellite links can be flexibly constructed and reconstructed.The experimental results show that the method can accurately emulate the satellite link,effectively guarantee the fidelity and periodicity of satellite link emulation,and facilitate the scientific research of satellite network scene.

    Resource allocation optimization algorithm based on double populations differential evolution in WSN
    Xiaochen HAO,Liyuan WANG,Jinshuo LIU,Lixia XIE,Wenhuan ZHANG
    2018, 39(4):  68-75.  doi:10.11959/j.issn.1000-436x.2018061
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    A resource allocation algorithm was proposed for improving the network performance through jointing channel allocation,power control and timeslot allocation in multi-radio multi-channel wireless sensor network.More specifically,the network was modeled as a multi-objective optimization problem where the energy efficient,resource allocation balanced,networks capacity maximized were considered under the link interference and link conflict constraints.Due to the problem was NP-Hardness,a simple centralized algorithm——differential evolution based on double populations was used to solve the constrained multi-objective optimization problem.The simulation results show that the proposed algorithm significantly improves the network capacity and energy efficiency and guarantees the resource allocation balancing while reducing link interference and avoiding link conflict.

    Degree distribution optimization for LT codes and its application in link maintenance of cognitive radio
    Benshun YI,Weiqing YAO
    2018, 39(4):  76-83.  doi:10.11959/j.issn.1000-436x.2018062
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    The degree distribution has a great influence on the performance of Luby transform (LT) codes.Based on the link maintain model of cognitive radio system,the novel degree distribution of LT codes was obtained by using the THOA (two-layer hierarchical optimization algorithm) to combine the IPD (improved poisson distribution) which achieved high decoding success rate with low overhead and the robust soliton distribution (RSD) which achieved high decoding success rate with high overhead.Simulation results show that the proposed method with application to link maintenance in cognitive radio system can improve the reliability and efficiency of secondary communication.

    Method of fast construction of M-sequence feedback functions with polynomial representation
    Jie GUAN,Congwei ZHOU
    2018, 39(4):  84-90.  doi:10.11959/j.issn.1000-436x.2018060
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    The construction of M-sequence feedback functions has always been a hotspot in the theory of stream cipher.Based on the structural properties of the M-sequence feedback function constructed by the m-sequence,the method of fast construction of a class M-sequence feedback functions was proposed by combining the function transformation and function derivative.Meanwhile,polynomial representation,amount and weight property of the class M-sequence feedback functions were considered.

    Combination strategy of active learning for hyperspectral images classification
    Ying CUI,Kai XU,Zhongjun LU,Shubin LIU,Liguo WANG
    2018, 39(4):  91-99.  doi:10.11959/j.issn.1000-436x.2018067
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    In order to improve the phenomena of jitter and instability of the traditional active learning single strategy algorithm in selecting the most valuable unlabeled samples.The idea of weighted combination of ensemble learning classifier and proposes a joint selection based on the combination strategy method (ESAL,ensemble strategy active learning) was introduced,the combination of the model was extended to the combination of the strategy so as to achieve the fusion of multiple strategies in a single model and achieve higher stability.By analyzing the classification results of hyperspectral remote sensing images,the ESAL algorithm can save 25.4% of the cost compared with the single strategy algorithm and reduce the jitter frequency to 16.67% when the same accuracy threshold is obtained,and the jitter is obviously improved.ESAL algorithm is out of good stability.

    Efficient revocable certificateless remote anonymous authentication protocol for wireless body area network
    Shun ZHANG,Hongli FAN,Hong ZHONG,Miaomiao TIAN
    2018, 39(4):  100-111.  doi:10.11959/j.issn.1000-436x.2018064
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    To ensure the security and privacy of patients’ health data in wireless body area network (WBAN),communication parties must be mutual authenticated.Now some bilinear pairings led to a larger computation cost for users and tree structure revocation would lead to larger user storage cost.In order to achieve revocation and reduce the cost of the user side,a novel revocable certificate less remote anonymous authentication protocol for WBAN was proposed by using elliptic curve cryptography and revoke algorithm that could revoke users by updating their time-private-keys.Security requirements including anonymity,mutual authentication and session key establishment were satisfied in proposed scheme.Compared with the existing schemes,the experimental analysis shows that the computation cost and storage cost of the authentication protocol are greatly reduced,which is more suitable for resource-constrained WBAN.Security analysis also shows that the protocol is secure in the random oracle model.

    k nearest neighbor pattern match queries over spatio-temporal label trajectories
    Jianqiu XU,Junxiu LIANG,Xiaolin QIN
    2018, 39(4):  112-122.  doi:10.11959/j.issn.1000-436x.2018063
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    Spatio-temporal label trajectories extended traditional spatio-temporal trajectories with semantic labels.k nearest neighbor pattern match was proposed to return the k nearest trajectories that fulfilled the temporal pattern condition.The Label R-Tree (LR-Tree for short) was proposed,which appending a label table and adding label bitmap in each entry,and k nearest neighbor pattern match query algorithm based on LR-Tree was designed.Using both real and synthetic datasets,the LR-Tree was extensively evaluated in comparison with 3DR-Tree,SETI and TB-Tree.The experimental results demonstrate that LR-Tree showing better pruning ability,and verify the effectiveness of proposed algorithm and index.

    Study on traffic scene semantic segmentation method based on convolutional neural network
    Linhui LI,Bo QIAN,Jing LIAN,Weina ZHENG,Yafu ZHOU
    2018, 39(4):  123-130.  doi:10.11959/j.issn.1000-436x.2018053
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    In order to improve the semantic segmentation accuracy of traffic scene,a segmentation method was proposed based on RGB-D image and convolutional neural network.Firstly,on the basis of semi-global stereo matching algorithm,the disparity map was obtained,and the sample library was established by fusing the disparity map D and RGB image into the four-channel RGB-D image.Then,with two different structures,the networks were trained by using two different learning rate adjustment strategy respectively.Finally,the traffic scene semantic segmentation test was carried out with RGB-D image as the input,and the results were compared with the segmentation method based on RGB image.The experimental results show that the proposed traffic scene segmentation algorithm based on RGB-D image can achieve higher semantic segmentation accuracy than that based on RGB image.

    Authentication scheme for multi-cloud environment based on smart card
    Sen ZHAO,Qingqing GAN,Xiaoming WANG,Fang YU
    2018, 39(4):  131-138.  doi:10.11959/j.issn.1000-436x.2018055
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    To solve the problem of the access keys stored in a smart card increasing linearly with the number of registered clouds without third party participated in authentication,an authentication scheme was proposed for multi-cloud environment based on smart card.In the proposed scheme,the authentication was realized between user and multiple clouds without third party participation when the smart card only stored two access key.Thus the storage cost of smart card was reduced effectively.Because there was no public key cryptography,the authentication messages was generated by using XOR homomorphic function and Hash function,thus the computational cost of the smart card and the cloud servers was reduced effectively.Moreover,the proposed scheme also didn’t need to store any user’s information on the cloud servers,thereby reducing the storage and management costs of the cloud servers.The security analysis and the performance analysis show that the proposed scheme is able to resist multiple attacks,which is secure and efficient.

    Efficient DDoS attack detection and prevention scheme based on SDN in cloud environment
    Heng HE,Yan HU,Lianghan ZHENG,Zhengyuan XUE
    2018, 39(4):  139-151.  doi:10.11959/j.issn.1000-436x.2018068
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    For addressing the problem of two typical types of distributed denial of service (DDoS) attacks in cloud environment,a DDoS attack detection and prevention scheme called SDCC based on software defined network (SDN) architecture was proposed.SDCC used a combination of bandwidth detection and data flow detection,utilized confidence-based filtering (CBF) method to calculate the CBF score of packets,judged the packet of CBF score below the threshold as an attacking packet,added its attribute information to the attack flow feature library,and sent the flow table to intercept it through SDN controller.Simulation results show that SDCC can detect and prevent different types of DDoS attacks effectively,and it has high detection efficiency,reduces the controller’s computation overhead,and achieves a low false positive rate.

    Improved SLM algorithm for PAPR reduction in OFDM system
    Ce JI,Wenjing ZHU,Ying WEI,Dianxia JIA
    2018, 39(4):  152-158.  doi:10.11959/j.issn.1000-436x.2018059
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    In order to reduce the peak to average power ratio (PAPR) of the conventional selective mapping (SLM) algorithm,decrease the transmission of side-band information,and improve the spectral efficiency in the orthogonal frequency division multiplexing (OFDM) system,the TL-SLM algorithm based on the conversion matrix and the chaotic sequence was proposed firstly.Although the TL-SLM algorithm could effectively decrease the transmission of side-band information,the reduction of PA P R was limited.To solve this problem,an improved TL-SLM algorithm based on the rotation vector was proposed,which was the TR-SLM algorithm.TR-SLM algorithm introduced the rotation vector to generate more time-domain alternative signals to further reducing the PA P R.The performance analysis shows that although the TL-SLM algorithm and the TR-SLM algorithm increase the complexity a little,the PA P R performance is effectively improved and the transmission of side-band information is greatly reduced.

    Network function outsourcing system based on prefix-preserving encryption
    Lingbo WEI,Xiaobing FENG,Chi ZHANG,Hualong SHENG,Nenghai YU
    2018, 39(4):  159-166.  doi:10.11959/j.issn.1000-436x.2018057
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    Due to the problem of high cost and limited scalability of dedicated hardware middleboxes,it is popular for enterprises to outsource middleboxes as software processes to the cloud service provider.In the current network function outsourcing schemes,the cloud service provider requires the enterprise’s communication traffic and network strategy which poses a serious threat to the enterprise’s piracy.Based on prefix-preserving encryption,a privacy preserving network function outsourcing system was proposed.Compared with other similar schemes,the system not only realizes the privacy protection of communication traffic,but also has higher throughput and lower delay.

    Data comparable encryption scheme based on cloud computing in Internet of things
    Qian MENG,Jianfeng MA,Kefei CHEN,Yinbin MIAO,Tengfei YANG
    2018, 39(4):  167-175.  doi:10.11959/j.issn.1000-436x.2018065
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    The previously proposed short comparable encryption (SCE) scheme can infer the plaintext relationship by comparing the ciphertexts relationship as well as ensuring data security in Internet of things.Unfortunately,it will incur high storage and computational burden during the process of comparing ciphertexts and generating tokens.To this end,an efficient short comparable encryption scheme called SCESW was proposed,which was utilizing the sliding window method with the same size window.Formal security analysis shows that the scheme can guarantee weak indistinguishability in standard model as well as data security and integrity.The experimental results demonstrate that the storage of the SCESW scheme is 1 t (t1) times shorter than that of the SCE scheme and the efficiency of the SCESW scheme is superior to that of the SCE scheme.

    Correspondences
    APM:agent protection mechanism applied for IaaS platform
    Peiru FAN,Bo ZHAO,Mingtao NI,Zhihong CHEN
    2018, 39(4):  176-188.  doi:10.11959/j.issn.1000-436x.2018069
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    The interference of false or fake test data on IaaS platform will contaminate the evaluation results,confusing users’ choices for IaaS services.To solve this problem,an agent protection mechanism (APM) for IaaS platform test environment was proposed.It ensured the integrity and commanded validity of the agent without additional hardware or software.Also an effectiveness verification approach based on requests was presented to detect APM failure problems timely.An experiment environment according to APM was implemented to evaluate the effectiveness and the performance overhead.Experimental results show that the APM is effective in protecting agent integrity and command validation,and its performance overhead is minor.

    Social network bursty topic discovery based on RNN and topic model
    Lei SHI,Junping DU,Meiyu LIANG
    2018, 39(4):  189-198.  doi:10.11959/j.issn.1000-436x.2018056
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    The data is noisy and diverse,with a large number of meaningless topics in social network.The traditional method of bursty topic discovery cannot solve the sparseness problem in social network,and require complicated post-processing.In order to tackle this problem,a bursty topic discovery method based on recurrent neural network and topic model was proposed.Firstly,the weight prior based on RNN and IDF were constructed to learn the relationship between words.At the same time,the word pairs were constructed to solve the sparseness problem.Secondly,the “spike and slab” prior was introduced to decouple the sparsity and smoothness of the bursty topic distribution.Finally,the burstiness of words were leveraged to model the bursty topic and the common topic,and automatically discover the bursty topics.To evaluate the effectiveness of proposed method,the various experiments were conducted.Both qualitative and quantitative evaluations demonstrate that the proposed RTM-SBTD method outperforms favorably against several state-of-the-art methods.

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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