China Communications

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  • July 2016 Vol. 13 No. 7
      
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  • Iterative Noncoherent Block Detection of Coded MPSK for Cooperative Relay Systems
    Yucheng He*, Jingjing Zhang, Rui Zhao, Lin Zhou,
    2016, 13(7): 1-6.
    Abstract ( )   Knowledge map   Save
    Maximum likelihood (ML) noncoherent block detection techniques are investigated for block-coded MPSK modulation in cooperative decode-and-forward relay systems over slow fading channels. A decision-directed iterative Viterbi algorithm (IVA) is derived for a suboptimal ML noncoherent detection. Simulation results show that the IVA can approach the error performances of the exhaustive detection method but at a lower complexity.
  • Traffic Matrix Estimation for IP-over-WDM Networks via Optical Bypass Techniques
    Laisen Nie, Dingde Jiang *, Lei Guo
    2016, 13(7): 7-15.
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    A traffic matrix is a necessary parameter fornetwork management functions, and itsupplies a flow-level view of a large-scale IP-over-WDM backbone network. This paper studies the problem of traffic matrix estimationand proposes an exact traffic matrix estimation approach based on network tomography techniques. The traditional network tomography model is extended to make it compatible with compressive sensing constraints. First, a stochastic perturbation is introduced in the traditional network tomography inference model.Then, an algorithm is proposed to achieve additional optical link observations via optical bypass techniques. The obtained optical link observations are used as extensions for the perturbed network tomography model to ensure that the synthetic model can meetcompressive sensing constraints. Finally, the traffic matrix is estimated from the synthetic model by means of a compressive sensing recovery algorithm.
  • Efficient Algorithm for K-Barrier Coverage Based on Integer Linear Programming
    Yanhua Zhang, Xingming Sun*, Baowei Wang
    2016, 13(7): 16-23.
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    Barrier coverage of wireless sensor networks is an important issue in the detection of intruders who are attempting to cross a region of interest. However, in certain applications, barrier coverage cannot be satisfied after random deployment. In this paper, we study how mobile sensors can be efficiently relocated to achieve k-barrier coverage. In particular, two problems are studied: relocation of sensors with minimum number of mobile sensors and formation of k-barrier coverage with minimum energy cost. These two problems were formulated as 0–1 integer linear programming (ILP). The formulation is computationally intractable because of integrality and complicated constraints. Therefore, we relax the integrality and complicated constraints of the formulation and construct a special model known as RELAX-RSMN with a totally unimodular constraint coefficient matrix to solve the relaxed 0–1 ILP rapidly through linear programming. Theoretical analysis and simulation were performed to verify the effectiveness of our approach.
  • Intrusion Detection Algorithm Based on Density, Cluster Centers, and Nearest Neighbors
    Xiujuan Wang, Chenxi Zhang, Kangfeng Zheng
    2016, 13(7): 24-31.
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    Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls. It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls. Many intrusion detection methods are processed through machine learning. Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology. However, almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data. In this paper, a new hybrid learning method is proposed on the basis of features such as density, cluster centers, and nearest neighbors (DCNN). In this algorithm, data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor. k-NN classifier is adopted to classify the new feature vectors. Our experiment shows that DCNN, which combines K-means, clustering-based density, and k-NN classifier, is effective in intrusion detection.
  • A Survivability Routing Mechanism in SDN Enabled Wireless Mesh Networks: Design and Evaluation
    Yuhuai Peng, Xiaoxue Gong *, Lei Guo, Dezhi Kong
    2016, 13(7): 32-38.
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    In software-defined networking, the separation of control plane from forwarding plane introduces new challenges to network reliability. This paper proposes a fault-tolerant routing mechanism to improve survivability by converting the survivability problem into two sub-problems: constructing an elastic-aware routing tree and controller selection. Based on the shortest path tree, this scheme continuously attempts to prune the routing tree to enhance network survivability. After a certain number of iterations, elastic-aware routing continues to improve network resiliency by increasing the number of edges in this tree. Simulation results demonstrate this fault-tolerant mechanism performs better than the traditional method in terms of the number of protected nodes and network fragility indicator.
  • Distributed Energy Saving Mechanism Based on CoMP in LTE-A System
    Fei Zheng, Wenjing Li*, Luoming Meng, Peng Yu, Lei Peng
    2016, 13(7): 39-47.
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    To reduce the energy consumption of the LTE-A system, a distributed energy-saving mechanism based on CoMP (CoMP-DESM) is proposed to solve the inadequate coverage problem under the dormant cells. First, the network is divided into clusters based on the equivalent cell principle. Then, we transfer global optimization into a group of subproblems. Second, a joint processing-based cooperative cell selection model is constructed to determine cooperative cells and dormant cells. Third, the compensative cells with a determined threshold are selected to control users’ access. Finally, a simulation is implemented in Matlab. Results show that the energy-saving rate can reach 36.4% and that the mechanism meets the network coverage requirement. Thus, joint processing can be effectively applied in an energy saving mechanism and used to improve the network performance of edge users without increasing transmission power.
  • QoE Assessment and Prediction Method for High-Definition Video Stream Using Image Damage Accumulation
    Yang Geng, Luoming Meng, Yao Wang , Yu Yang , Zhiguo Qu
    2016, 13(7): 48-59.
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    The accuracy of the traditional assessment method of the quality of experience (QoE) has been facing challenges with the growth of high-definition (HD) video streaming services. Image display-quality damage is the main factor that affects the QoE in HD video services through UDP network transmission. In this paper, we introduce a novel objective factor known as image damage accumulation (IDA) to assess user’s QoE in HD video services. First, this paper quantitatively analyzed the effect on user quality of experience by IDA and established a mapping relationship between mean opinion scores and IDA. Furthermore, the probability of image damage caused by compression and transmission were analyzed. Based on this analysis, an objective QoE assessment and prediction method for HD video stream service that evaluated the user experience according to IDA are proposed. The proposed method can achieve assessment and prediction accuracy on three distinct subjective tests.
  • Fingerprint Liveness Detection Based on Multi-Scale LPQ and PCA
    Chengsheng Yuan, Xingming Sun*, Rui Lv
    2016, 13(7): 60-65.
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    Fingerprint authentication system is used to verify users’ identification according to the characteristics of their fingerprints. However, this system has some security and privacy problems. For example, some artificial fingerprints can trick the fingerprint authentication system and access information using real users’ identification. Therefore, a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information. In this paper, a new software-based liveness detection approach using multi-scale local phase quantity (LPQ) and principal component analysis (PCA) is proposed. The feature vectors of a fingerprint are constructed through multi-scale LPQ. PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features. Finally, a training model is gained using support vector machine classifier, and the liveness of a fingerprint is detected on the basis of the training model. Experimental results demonstrate that our proposed method can detect the liveness of users’ fingerprints and achieve high recognition accuracy. This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.
  • Steganalysis of LSB Matching Using Characteristic Function Moment of Pixel Differences
    Xianyi Chen, Guangyong Gao *, Dandan Liu, Zhihua Xia
    2016, 13(7): 66-73.
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    Nowadays, many steganographic tools have been developed, and secret messages can be imperceptibly transmitted through public networks. This paper concentrates on steganalysis against spatial least significant bit (LSB) matching, which is the prototype of many advanced information hiding methods. Many existing algorithms deal with steganalysis problems by using the dependencies between adjacent pixels. From another aspect, this paper calculates the differences among pixel pairs and proves that the histogram of difference values will be smoothed by stego noises. We calculate the difference histogram characteristic function (DHCF) and deduce that the moment of DHCFs (DHCFM) will be diminished after stego bits are hidden in the image. Accordingly, we compute the DHCFMs as the discriminative features. We calibrate the features by decreasing the influence of image content on them and train support vector machine classifiers based on the calibrated features. Experimental results demonstrate that the DHCFMs calculated with nonadjacent pixels are helpful to detect stego messages hidden by LSB matching.
  • A Novel Hidden Danger Prediction Method in Cloud-Based Intelligent Industrial Production Management Using Timeliness Managing Extreme Learning Machine
    Xiong Luo, Xiaona Yang,Weiping Wang*, Xiaohui Chang, Xinyan Wang , Zhigang Zhao
    2016, 13(7): 74-82.
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    To prevent possible accidents, the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently. A machine learning algorithm that uses timeliness managing extreme learning machine is utilized in this article to achieve the above prediction. Compared with traditional learning algorithms, extreme learning machine (ELM) exhibits high performance because of its unique feature of a high generalization capability at a fast learning speed. Timeliness managing ELM is proposed by incorporating timeliness management scheme into ELM. When using the timeliness managing ELM scheme to predict hidden dangers, newly incremental data could be added prior to the historical data to maximize the contribution of the newly incremental training data, because the incremental data may be able to contribute reasonable weights to represent the current production situation according to practical analysis of accidents in some industrial productions. Experimental results from a coal mine show that the use of timeliness managing ELM can improve the prediction accuracy of hidden dangers with better stability compared with other similar machine learning methods.
  • Multiway Dynamic Trust Chain Model on Virtual Machine for Cloud Computing
    Jie Zhu, Guoyuan Lin *, Fucheng You , Huaqun Liu , Chunru Zhou
    2016, 13(7): 83-91.
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    This paper sums up four security factors after analyzing co-residency threats caused by the special multitenant environment in the cloud. To secure the factors, a multiway dynamic trust chain transfer model was proposed on the basis of a measurement interactive virtual machine and current behavior to protect the integrity of the system. A trust chain construction module is designed in a virtual machine monitor. Through dynamic monitoring, it achieves the purpose of transferring integrity between virtual machine. A cloud system with a trust authentication function is implemented on the basis of the model, and its practicability is shown.
  • Attribute-Based Access Control Scheme with Efficient Revocation in Cloud Computing
    Zhihua Xia,Liangao Zhang,Dandan Liu
    2016, 13(7): 92-99.
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    Attribute-based encryption (ABE) supports the fine-grained sharing of encrypted data. In some common designs, attributes are managed by an attribute authority that is supposed to be fully trustworthy. This concept implies that the attribute authority can access all encrypted data, which is known as the key escrow problem. In addition, because all access privileges are defined over a single attribute universe and attributes are shared among multiple data users, the revocation of users is inefficient for the existing ABE scheme. In this paper, we propose a novel scheme that solves the key escrow problem and supports efficient user revocation. First, an access controller is introduced into the existing scheme, and then, secret keys are generated corporately by the attribute authority and access controller. Second, an efficient user revocation mechanism is achieved using a version key that supports forward and backward security. The analysis proves that our scheme is secure and efficient in user authorization and revocation.
  • Decision Model of Knowledge Transfer in Big Data Environment
    Chuanrong Wu *, Yingwu Chen , Feng Li
    2016, 13(7): 100-107.
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    A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment. This model can determine the weight of knowledge transferred from another enterprise or from a big data provider. Numerous simulation experiments are implemented to test the efficiency of the optimization model. Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider, the total discount expectation of profits will increase, and the transfer cost will be reduced. The calculated results are in accordance with the actual economic situation. The optimization model can provide useful decision support for enterprises in a big data environment.
  • Multilevel Pattern Mining Architecture for Automatic Network Monitoring in Heterogeneous Wireless Communication Networks
    Zhiguo Qu,, John Keeney, Sebastian Robitzsch, Faisal Zaman, Xiaojun Wang
    2016, 13(7): 108-116.
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    The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks. This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data. This architecture leverages and combines existing frequent itemset discovery over data streams, association rule deduction, frequent sequential pattern mining, and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.
  • Audio Zero-Watermark Scheme Based on Discrete Cosine Transform-Discrete Wavelet Transform-Singular Value Decomposition
    Min Lei, Yu Yang, XiaoMing Liu, MingZhi Cheng, Rui Wang
    2016, 13(7): 117-121.
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    Traditional watermark embedding schemes inevitably modify the data in a host audio signal and lead to the degradation of the host signal.In this paper, a novel audio zero-watermarking algorithm based on discrete wavelet transform(DWT),discrete cosine transform(DCT),and singular value decomposition(SVD) is presented. The watermark is registered by performing SVD on the coefficients generated through DWT and DCT to avoid data modification and host signal degradation. Simulation results show that the proposed zero-watermarking algorithm is strongly robust to common signal processing methods such as requantization, MP3 compression, resampling, addition of white Gaussian noise, and low-pass filtering.
  • Novel Zero-Watermarking Scheme Based on DWT-DCT
    Yu Yang, Min Lei, Xiaoming Liu , Zhiguo Qu *, Cheng Wang
    2016, 13(7): 122-126.
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    Essential characteristics for watermark registration and detection without data modification can be used in anaudio zero-watermarking scheme. In this paper, a novel audio zero-watermarking scheme based on discrete wavelet transform (DWT) and discrete cosine transform (DCT) is presented. Thewatermark is registered by comparing the mean absolute values of the adjacent frame coefficients after DWT and DCT. Simulation results show that the proposed scheme is stronglyrobusttocommon attacks such as AWGN, downsampling, low-pass filtering,requantization, and MP3 compression. A performance analysis of the proposed scheme shows that all bit error rates after attacks are zero.
  • Adaptive Energy-Efficient Power Allocation for DAS with Imperfect Channel State Information and Antenna Selection
    Weiye Xu*, Min Lin, Yu Yang , Xiangbin Yu
    2016, 13(7): 127-134.
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    Considering that perfect channel state information (CSI) is difficult to obtain in practice, energy efficiency (EE) for distributed antenna systems (DAS) based on imperfect CSI and antennas selection is investigated in Rayleigh fading channel. A novel EE that is defined as the average transmission rate divided by the total consumed power is introduced. In accordance with this definition, an adaptive power allocation (PA) scheme for DAS is proposed to maximize the EE under the maximum transmit power constraint. The solution of PA in the constrained EE optimization does exist and is unique. A practical iterative algorithm with Newton method is presented to obtain the solution of PA. The proposed scheme includes the one under perfect CSI as a special case, and it only needs large scale and statistical information. As a result, the scheme has low overhead and good robustness. The theoretical EE is also derived for performance evaluation, and simulation result shows the validity of the theoretical analysis. Moreover, EE can be enhanced by decreasing the estimation error and/or path loss exponents.