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25 July 2021, Volume 42 Issue 7

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Papers

Video semantics-driven resource allocation algorithm in Internet of vehicles

Jiujiu CHEN, Chunyan FENG, Caili GUO, Yang YANG, Qizheng SUN, Meiyi ZHU

2021, 42(7):  1-11.  doi:10.11959/j.issn.1000-436x.2021080

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Aiming at the problem that traditional resource allocation methods will no longer be applicable, with the demand of intelligent computing services such as video semantic understanding in Internet of vehicles, the video semantic driven resource allocation algorithm was studied.First of all, taking the object detection task as an example, a semantic driven resource allocation guidance model for video was proposed and an algorithm for solving model parameters was given.Secondly, an optimization problem of resource allocation driven by video semantics in Internet of vehicles was constructed, which was transformed into a convex problem and solved by convex optimization algorithm.Furthermore, in order to reduce the complexity of the convex optimization algorithm, a resource allocation algorithm based on reinforcement Q learning was proposed.Finally, the performance advantages of the proposed algorithm are verified by simulations.

Design of self-adaptive spatio-temporal diversity joint scheduling strategy

Qing TONG, Yunfei GUO, Shumin HUO, Yawen WANG, Yujia MAN, Kai ZHANG

2021, 42(7):  12-24.  doi:10.11959/j.issn.1000-436x.2021119

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To solve the problem that a diversity system is difficult to take defense capability, defense cost and quality of service into account at the same time under a single diversity strategy, firstly, the scheduling object selecting sequences under different security levels were constructed based on the measurement of scheduling heterogeneity, executor security and spatial diversity.Then, according to the coarse-grained evaluation of threat environment, the scheduling time and scheduling object were determined comprehensively.Through the realization of the spatio-temporal diversity Web server system in a cloud environment, the proposed scheduling strategy was tested with attack and defense experiments and compared with the existing scheduling strategies.The results show that the proposed scheduling strategy improves the defense capability significantly and maintains a high quality of service within the acceptable defense cost increasing range.

Communication service priority in smart substation and its queue scheduling method

Jun’e LI, Qiuyu LU, Jian LIU, Kai YUAN, Wei TIAN, Bijun PENG

2021, 42(7):  25-40.  doi:10.11959/j.issn.1000-436x.2021107

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The future development of power grid puts forward the requirement of unified network for all the communication services in smart substation.In order to meet the different quality of service (QoS) requirements of various communication services, as well as ensure the real-time and reliability of key services when the network congestion occurs, a priority scheme and its queue scheduling method for the smart substation was proposed.The priorities of the communication services in the smart substation were assigned considering the delay requirements, the importance to power grid operation control, and the traffic of the communication services.The queue scheduling algorithm of hierarchical deficit weighted round robin (HDWRR), combining the advantages of strict priority queue (SPQ) algorithm and deficit weight round robin (DWRR) algorithm, was designed for the proposed priority scheme.The simulation results show that the proposed method has isolation ability for malicious traffic, and can ensure the real-time and reliable transmission of key services in smart substation once the network is congested.

Recognition method based on hesitant fuzzy set for unequal length sequences and its application

Shuangming LI, Xin GUAN, Guidong SUN

2021, 42(7):  41-51.  doi:10.11959/j.issn.1000-436x.2021118

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Aiming at the problem that unequal length sequences were difficult to recognize, a recognition method based on hesitant fuzzy distance measure was proposed.Firstly, the problem was described from the perspective of fuzzy value, and the hesitant fuzzy information recognition model of unequal length sequence was established by lattice closeness degree.Secondly, the mean value, variance, relative range and hesitancy degree of hesitant fuzzy values were defined.Combined with membership difference of the shorter part, the generalized integrated feature distance measure and the generalized weighted integrated feature distance measure were defined to meet relevant properties of metric space, and the strict mathematical proof process was given.Finally, entropy measure and support measure were proposed to determine the weight, and the VIKOR recognition method based on hesitant distance measure was given.The simulation results verify the effectiveness and feasibility of proposed method from numerical examples, energy strategy selection and target recognition respectively.

Identity-based provable data possession scheme for multi-source IoT terminal data in public cloud

Huaqun WANG, Zhe LIU, Debiao HE, Jiguo LI

2021, 42(7):  52-60.  doi:10.11959/j.issn.1000-436x.2021077

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To solve the problem of multi-source IoT data integrity verification, identity-based provable data possession for multi-source IoT terminal in public cloud (ID-MPDP) was proposed.Firstly, the formal definitions of system model and security model of ID-MPDP were given.Then, the specific ID-MPDP scheme was designed by using RSA.Finally, the performance analysis and security analysis of ID-MPDP were given.Through performance analysis and security analysis, ID-MPDP was provably secure, efficient and convertible.It has the following advantages, such as it can be used for the integrity checking for multi-source IoT terminal data, it has lower block expansion rate, it eliminates the certification management cost by using the identity-based public key cryptography and it is convertible.

Link prediction method based on the similarity of high path

Qiuyang GU, Bao WU, Renyong CHI

2021, 42(7):  61-69.  doi:10.11959/j.issn.1000-436x.2021055

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For the problem that the existing link prediction method has many problems, including low accuracy and low efficiency, a method of high-order path similarity link prediction was proposed.Firstly, the path was used as the judging feature to predict missing links in complex networks, which could make resource allocation more effective and restricts information leakage by punishing public neighbor pairs.Secondly, by using high order paths as judging features, the available long paths between seed nodes would be punished.Finally, several real complex network datasets were used for numerical examples calculation.Experimental results show that the proposed algorithm is more accurate and efficient than other baseline methods.

Port address overloading based packet forwarding verification in SDN

Ping WU, Chaowen CHANG, Yingying MA

2021, 42(7):  70-83.  doi:10.11959/j.issn.1000-436x.2021108

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Aiming at the problem that the existing forwarding verification mechanisms in software-defined networking (SDN) incur significant communication overhead caused by embedding additional packet fields, a packet forwarding verification mechanism based on port address overloading was proposed, which key idea was the ingress switch implemented port address overloading by reconstructing port and address of packet, downstream switches executed packet probabilistic verification based on overloading port address, and the controller acquired valid and invalid packet statistics of node verification in the path and localized anomaly.Anomaly detection threshold of malicious injecting and dropping packets was presented by theoretical analysis.Finally, the proposed scheme was implemented and evaluated.Experiments demonstrate the proposed scheme achieves efficient forwarding and effective anomaly localization with less than 10% of additional forwarding delays and less than 8% of throughput degradation.

Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal

Zeliang AN, Tianqi ZHANG, Baoze MA, Pan DENG, Yuqing XU

2021, 42(7):  84-94.  doi:10.11959/j.issn.1000-436x.2021142

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To recognize the modulation style adopted in multiple-input-multiple-output orthogonal space-time block code (MIMO-OSTBC) systems, a cooperative modulation recognition algorithm based on the one-dimensional convolutional neural network (1D-CNN) was proposed.With the lossless I/Q signal selected as shallow features, the zero-forcing blind equalization was first leveraged to improve the discrimination of different modulation signals.Then the 1D-CNN recognition model was devised and trained to extract deep features from shallow ones.Later, two decision fusion strategies of voting-based and confidence-based were leveraged in the multiple-antenna receiver to improve recognition accuracy.Experimental results show that the proposed algorithm can effectively recognize five modulation types {BPSK, 4PSK,8PSK,16QAM,4PAM}, with a 100% recognition accuracy when the signal-to-noise is equal or greater than-2 dB.

Botnet detection based on generative adversarial network

Futai ZOU, Yue TAN, Lin WANG, Yongkang JIANG

2021, 42(7):  95-106.  doi:10.11959/j.issn.1000-436x.2021082

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In order to solve the problems of botnets’ strong concealment and difficulty in identification, and improve the detection accuracy of botnets, a botnet detection method based on generative adversarial networks was proposed.By reorganizing the data packets in the botnet traffic into streams, the traffic statistics characteristics in the time dimension and the traffic image characteristics in the space dimension were extracted respectively.Then with the botnet traffic feature generation algorithm based on generative adversarial network, botnet feature samples were produced in the two dimensions.Finally combined with the application of deep learning in botnet detection scenarios, a botnet detection model based on DCGAN and a botnet detection model based on BiLSTM-GAN were proposed.Experiments show that the proposed model improves the botnet detection ability and generalization ability.

Channel modeling of molecular communication via free diffusion with multiple receiver

Zhuo SUN, Xu BAO, Jie LIN, Wence ZHANG

2021, 42(7):  107-116.  doi:10.11959/j.issn.1000-436x.2021151

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A coexistence scenario with a point source, a pair of absorbing and transparent receiver was considered, an interference factor was introduced in the proposed channel model based on the receiving molecular probability in the transparent receiver considering the influences of the absorbing receiver on the transparent one.Furthermore, the channel model of point source and transparent receiver had been proposed by using Levenberg-Marquardt algorithm combined with artificial neural network to study and predict channel model parameters.The simulation results not only verify the effectiveness of the proposed channel model, but also show that the peak time of any point in the environment is directly proportional to the square of the distance from the point source to the receiver, and inversely proportional to the molecular diffusion coefficient, and the peak time is not affected by the absorbing receiver in the environment.

Memory fragment file carving algorithm based on the reverse of the structure chain

Binglong LI, Zhenyu ZHOU, Yu ZHANG, Heyu ZHANG, Chaowen CHANG

2021, 42(7):  117-127.  doi:10.11959/j.issn.1000-436x.2021143

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To address the extraction of document evidence for doc, pdf, and other common file types in the memory image, the model of fragment file carving based on memory image was proposed.Then, on the basis of the model, the fragment file carving algorithm based on the reverse of file object structure chain was designed and implemented, the algorithm was able to get file data left behind in the memory image file.The experimental results show that the proposed algorithm can successfully carve out of memory file’s metadata, and the accuracy is 100%, and in a typical application case, the accuracy of the algorithm for memory file can achieve 87.5%, far higher than disk-based file caving algorithm.

Secure communication mechanism for VSN based on certificateless signcryption

Wenbo ZHANG, Wenhua HUANG, Jingyu FENG

2021, 42(7):  128-136.  doi:10.11959/j.issn.1000-436x.2021144

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To solve the communication security problems of vehicular social network (VSN), an efficient certificateless signcryption scheme was proposed.The proposed scheme was proven secure in the random oracle model based on the computational Diffie-Hellman problem and elliptic curve discrete logarithm problem, which provided confidentiality and unforgeability protection for VSN members.In addition, when the pseudonym mechanism was used to solve the privacy protection problem in VSN, without installing tamper-proof device, a self-generation mechanism for vehicle pseudonyms and their keys was proposed.The performance analysis shows that the proposed scheme can decrease communication cost, and significantly reduce the computation overhead of the key generation center.

UAV ad hoc network link prediction based on deep graph embedding

Jian SHU, Qining WANG, Linlan LIU

2021, 42(7):  137-149.  doi:10.11959/j.issn.1000-436x.2021083

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Aiming at the characteristics of the UAV ad hoc network (UAANET), such as topological temporal-varying, node mobility and intermittent connection, a temporal graph embedding model was proposed to present the preprocessed UAANET.To improve the sampling efficiency, the sampling interval was calculated based on linear probability.The network structure features were mapped to the relationship between nodes, and the contextual semantic features of nodes were extracted by adversarial training.With the help of long and short-term memory network, the temporal characteristics of the UAANET were extracted to predict the connection at the next moment.AUC, MAP, and Error Rate were employed as evaluation indexes.The simulation experiments based on NS-3 show that compared with Node2vec, DDNE and E-LSTM-D, the proposed method has a better accuracy.

Bus cache-based location privacy protection scheme in the Internet of vehicles

Jie CUI, Xuefeng CHEN, Jing ZHANG, Lu WEI, Hong ZHONG

2021, 42(7):  150-161.  doi:10.11959/j.issn.1000-436x.2021132

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To solve the problem of real location leakage when vehicles use location-based service (LBS) on the Internet of vehicles, a location privacy protection scheme based on bus cache was proposed.Firstly, a point of interest (POI) pool was obtained from the LBS provider based on its route information.Then the data in the POI pool was selected form a POI list while driving.Finally, the POI list was broadcast to surrounding private vehicles.After the private vehicle received the broadcast data, it verified the identity of the bus and then stored the POI list in the vehicle’s local cache.When a private vehicle needed to query POI information, it would first retrieve it in the local cache, and if the cache was missed, it would send a query request to the LBS provider using the k-anonymity method.The simulation experiment results show that the proposed scheme can reduce the possibility of leakage of the real location of the private vehicle by reducing the number of communications between the private vehicle and the LBSP, thereby effectively improving the privacy protection level of the private vehicle.

Spectral clustering-based energy-efficient resource allocation algorithm in heterogeneous cellular ultra-dense network

Xue WANG, Jing LIU, Jiani SUN, Jizhen ZHANG, Zhihong QIAN

2021, 42(7):  162-175.  doi:10.11959/j.issn.1000-436x.2021141

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In order to solve problems of high power consumption, spectrum shortage and low energy efficiency in the ultra-intensive 5G mobile communication scenario, a resource allocation algorithm based on the maximum energy efficiency for the two-layer heterogeneous cellular non-orthogonal multiple access network was proposed.The original NP-hard optimization problem on the downlink communication link of ultra-dense scene was divided into two subproblem, such as frequency resource allocation and power allocation, which became a deterministic constraint optimization problem.The frequency resource allocation scheme of different user groups was obtained by using base station clustering based on the improved k-means algorithm and users grouping based on spectral clustering algorithm.The fraction of energy efficiency optimization was transformed into a solvable continuous convex optimization problem and power distribution was realized by Dinkelbach method, and the Lagrange multiplier iterative algorithm, respectively.Jointly optimize system energy efficiency in terms of base station clustering, user grouping, resource block allocation and power allocation, which minimized the inter-cluster interference and intra-cluster interference of the base station efficiently.The simulation results show that the proposed algorithm is better on energy efficiency and computational efficiency compared with existing algorithms.

Optimal energy-efficiency beamforming design for SWIPT-enabled sink in sensor cloud based on deep learning

Zhe WANG, Taoshen LI, Lina GE, Guifen ZHANG, Min WU

2021, 42(7):  176-188.  doi:10.11959/j.issn.1000-436x.2021131

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To solve the problems of high complexity and poor real-time performance caused by traditional wireless resource management based on optimization methods, the energy efficiency maximization problem of sink node and its mathematical model were established for SWIPT-enabled sensor-cloud system, then the deep learning method was introduced to realize the solving and online decision-making with lower complexity and higher real-time performance.The mathematical model was transformed into a high-dimensional solvable form, and then a SWIFT-WMMSE algorithm with iterated forms was designed to solve optimal beamforming vector.The convergence of SWIPT-WMMSE algorithm was proved.Then, based on error propagation of DNN approximation, the scale design criteria for the DNN was deduced, and the approximation was realized through DNN training.Finally, the simulation results verify the effectiveness of SWIPT-WMMSE and DNN algorithm, as well as the approximation effect of DNN and its system performance gains.

Network slicing with spectrum sharing strategy in cognitive capacity harvesting network

Jie HUANG, Fan YANG, Yingzhao XIE, Xun ZUO, Tian QIU

2021, 42(7):  189-197.  doi:10.11959/j.issn.1000-436x.2021137

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To realizing the network slice with spectrum sharing in cognitive capacity harvesting network, a spectrum sharing strategy was proposed based on 4D conflict graph and opportunistic capacity.Firstly, a 4D conflict graph model for mesh network was built and a method to achieve the node sets with conflict free was proposed.Then, the opportunistic capacity model of spectrum sharing was established and the opportunistic capacity of the unlicensed channel was derived.Finally, the spectrum sharing strategy based on 4D conflict graph and opportunistic capacity was proposed for cognitive capacity harvesting network.The simulation results show that compared with existing algorithms, the proposed algorithm can effectively utilize opportunistic spectrum resources and further improve channel utilization.

Comprehensive Review

Survey of high-precision localization and the prospect of future evolution

Huiqiang WANG, Kaixuan GAO, Hongwu LYU

2021, 42(7):  198-210.  doi:10.11959/j.issn.1000-436x.2021136

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Nowadays, high-precision indoor positioning service has witnessed an increasing interest because it has become the key support of many 5G major application scenarios, and the implementation technologies and mechanisms are continuously updated with advantages, disadvantages and specificities.Therefore, a timely and systematic summary of indoor-positioning was made, and that of prospects for where this domain was heading.Firstly, based on the analysis of the existing papers, summarization of this domain was proposed from two aspects, such as positioning-techniques and positioning-methods.Secondly, a classification model was put forward based on the positioning scenarios and the “hidden requirements” included were pointed out.Then, the evaluation indices system of positioning-systems was proposed and some of the existing high-accuracy positioning system was evaluated by it.Finally, the direction of evolution about high-precision indoor positioning domain was summarized, the development trend of integrated network was pointed out, and some important concepts, such as “online is inplace”, were proposed.

Correspondences

Intelligent CSI feedback method for fast time-varying FDD massive MIMO system

Yong LIAO, Shuai WANG, Ning SUN

2021, 42(7):  211-219.  doi:10.11959/j.issn.1000-436x.2021129

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In the frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system, the channel state information (CSI) matrix existed noise caused by the wireless channel interference and the time correlation caused by Doppler shift.Because of these effects, the communication system couldn’t guarantee the requirements of reliability and low delay.An intelligent CSI feedback method was adopted.The convolutional neural network (CNN) and batch normalization (BN) network was used to extract the noise in the CSI matrix and learned the spatial structure of the channel.The time correlation between the CSI matrices through the attention mechanism was extracted to improve the accuracy of CSI reconstruction.The data was generated by the standard fast time-varying channel model simulation to train the network offline.System simulation and analysis show that the proposed method can effectively suppress the influence of noise and extract the time correlation caused by Doppler.Compared with the traditional CSI compression feedback algorithm and CsiNet algorithm, the proposed method has better NMSE and cosine similarity performance.

Lightweight privacy protection data auditing scheme for regenerating-coding-based distributed storage

Guangjun LIU, Wangmei GUO, Jinbo XIONG, Ximeng LIU, Changyu DONG

2021, 42(7):  220-230.  doi:10.11959/j.issn.1000-436x.2021116

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To reduce the security implementation cost of the outsourcing data audit mechanism for the regenerating-coding-based distributed storage systems, an orthogonal algebraic coding method was put forward to construct a lightweight privacy-preserving audit scheme based on linear homomorphic authentication.The homomorphic authentication tags were generated with the orthogonalization between the file encoded data and the private secret key vector, and the privacy protection of the auditing response message was achieved by using the random masking that was constructed by randomizing the orthogonal basis vectors of the specific sub-vector of the user’s secret key.The work realized the effective integration of algebraic coding, privacy protection, and security auditing.Theoretical analysis shows that the proposed scheme can realize the information-theoretic security in the regenerating-coding-based storage applications.Compared with the existing works, the proposed scheme is of low computational complexity and communication overhead, and better performance advantages.

Speaker verification method based on deep information divergence maximization

Chen CHEN, Yafeng RONG, Chaoqun JI, Deyun CHEN, Yongjun HE

2021, 42(7):  231-237.  doi:10.11959/j.issn.1000-436x.2021133

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To solve the problem that the nonlinear relationship between speaker representations cannot be accurately captured in speaker verification, an objective function based on depth information divergence maximization was proposed.It could implicitly represent the nonlinear relationship between speaker representations by calculating the similarity between their distributions.Under the supervision of the optimization goal of maximizing the statistical correlation, the deep neural network was optimized towards the direction that the within-class data was more compact and the between-class data were far away from each other, and finally the discrimination of deep speaker representation space could be effectively improved.Experimental results show that compared with other deep learning methods, the relative EER of the proposed method is reduced by 15.80% at most, which significantly improves the system performance.