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25 July 2022, Volume 43 Issue 7

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Research on low-energy-consumption deployment of emergency UAV network for integrated communication-navigating-sensing

Li WANG, Qing WEI, Lianming XU, Yuan SHEN, Ping ZHANG, Aiguo FEI

2022, 43(7):  1-20.  doi:10.11959/j.issn.1000-436x.2022138

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In public emergencies such as accident relief, rescue workers are faced with challenges, such as poor communication, unstable navigating, and inaccurate disaster sensing.It is necessary to deploy an emergency unmanned aerial vehicle (UAV) network to guarantee the services of communication-navigating-sensing.Aiming at alleviating the problem of limited energy of UAV, a low-energy-consumption deployment of an emergency UAV network was first proposed for integrated communication-navigating-sensing (ICNS).The proposed scheme was able to realize network topology reconstruction and role cognition on demand.Then, a particle swarm algorithm based hierarchical matching decision-making algorithm was presented to jointly optimize three sub-problems, including the associations between UAVs and users, the resource allocation for multi-role UAV communications, and the UAV position.Simulation results show that the proposed ICNS scheme can achieve flexible adaptation of the multi-objective requirements and limited network resources, and dramatically reduce the demand for the number of UAVs and the deployment energy consumption.

Papers

Double-RIS assisted anti-jamming communication method based on joint active and passive beamforming optimization

Haiyan GUO, Zhen YANG, Yulong ZOU, Bin LYU, Yuntian FENG, Yujuan ZHAO

2022, 43(7):  21-30.  doi:10.11959/j.issn.1000-436x.2022144

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For double-reconfigurable intelligent surface (RIS) assisted wireless communication system, an anti-jamming communication method based on joint active and passive beamforming optimization was proposed.In order to maximize the received signal-to-interference-and-noise ratio (SINR), a joint active and passive beamforming optimization problem was formulated.By using the alternating optimization (AO) algorithm, the formulated joint optimization problem was divided into three subproblems, which were solved iteratively in an alternating manner.Specifically, the quasi-optimal passive beamforming vector of each RIS was obtained by applying the semidefinite relaxation (SDR) algorithm, and the optimal active beamforming vector of the BS was obtained based on the generalized Rayleigh quotient.Simulation results demonstrate that the proposed method outperforms the traditional single-RIS ant-jamming communication methods.

All-optical pattern matching system of 42 Gbit/s 4-bit BPSK signals and its demonstration for optoelectronic firewall

Qihan ZHANG, Xiaoxue GONG, Rui LI, Xin LI, Shanguo HUANG, Lei GUO

2022, 43(7):  31-40.  doi:10.11959/j.issn.1000-436x.2022141

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To achieve the all-optical pattern matching for optoelectronic firewall which could process high-speed optical signals efficiently, a BPSK all-optical pattern recognition system with the data rate up to 42 Gbit/s was proposed and demonstrated by employing the commercial optical components.First, the input BPSK sequence was converted to a positive and a negative IM signals by adding in-phase and inverse-phase coherent carriers, respectively.Then the converted IM signals were coupled into time delay lines and optical AND logic gates implemented by HNLF to achieve the optical correlation operation.In the end, a high-level optical pulse indicating the pattern matched result was output.The numerical simulation and the experiment results demonstrate that the proposed system can recognize a 4-bit target BPSK sequence from an 8-bit 42 Gbit/s BPSK input sequence, which proves the feasibility of the all-optical pattern matching of BPSK signals and can be applied to high-speed optoelectronic firewall.

Improved meet-in-the-middle attack on reduced-round Kiasu-BC algorithm

Manman LI, Shaozhen CHEN

2022, 43(7):  41-48.  doi:10.11959/j.issn.1000-436x.2022112

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Kiasu-BC algorithm is an internal tweakable block cipher of authenticated encryption algorithm Kiasu as one of first-round candidates in the CAESAR competition.The precomputation complexity is reduced by utilizing the freedom of the tweak and the internal key restriction through the research on structural characteristics of Kiasu-BC algorithm based on AES-128 round function.Combined with the differential enumeration technique, a new 5-round meet-in-the-middle distinguisher was constructed to improve the meet-in-the-middle attack on 8-round Kiasu-BC algorithm.The improved attack requires the time complexity of 2¹¹⁴, the memory complexity of 2⁶³ and the data complexity of 2¹⁰⁸.

Design of efficient anonymous identity authentication protocol for lightweight IoT devices

Zhenyu WANG, Yang GUO, Shaoqing LI, Shen HOU, Ding DENG

2022, 43(7):  49-61.  doi:10.11959/j.issn.1000-436x.2022125

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Aiming at the problem that complex security primitives in existing schemes were not suitable for resource-constrained IoT devices, a lightweight efficient anonymous identity authentication protocol for IoT devices was designed based on physical unclonable function (PUF).Through the formal security model and ProVerif tool, it was proved that the protocol satisfies 13 security properties such as information confidentiality, integrity, un-traceability, and forward/backward secrecy.Compared with existing relevant protocols, the computing overhead of the protocol on the device side and the server side is 0.468 ms and 0.072 ms respectively, and the device storage and communication overheads are 256 bit and 896 bit respectively, which is highly suitable for lightweight IoT devices with limited resources.

Certificateless public key cryptography based provable data possession scheme in edge environment

Ziyuan WANG, Ruizhong DU

2022, 43(7):  62-72.  doi:10.11959/j.issn.1000-436x.2022130

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In the edge environment, data transmission to the cloud needs to pass through a new entity, the edge node, which makes the data security problem more complicated, the confidentiality and integrity of data are difficult to be guaranteed, and the traditional provable data possession scheme is not suitable for the edge environment with a large number of devices.Based on this, a certificateless public key cryptography based provable data possession scheme was proposed for the edge environment, combining the online/offline signature idea, where the user device only needed to perform light computation when uploading data in the case of semi-trusted edge nodes, leaving the rest of the computation to be performed in the offline phase.The scheme used edge nodes for auditing work while supporting auditing in different storage states, as well as privacy protection and other features.The security analysis shows that the proposed scheme is proven to be secure by being able to effectively combat three types of adversary attacks under a stochastic prediction model.Experimental comparisons with other schemes show that the proposed scheme has lowest time overhead.

GNN-based optimization algorithm for joint user scheduling and beamforming

Shiwen HE, Jun YUAN, Zhenyu AN, Min ZHANG, Yongming HUANG, Yaoxue ZHANG

2022, 43(7):  73-84.  doi:10.11959/j.issn.1000-436x.2022133

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The coordinated multi-point (CoMP) transmission technology has the characteristics of reducing co-channel interference and improving spectral efficiency.For the CoMP technology, user scheduling (US) and beamforming (BF) design are two fundamental research problems located in the media access control layer and the physical layer, respectively.Under the consideration of user service quality requirements, the joint user US-BF optimization problem was investigated with the goal of maximizing network throughput.To overcome the problem that the traditional optimization algorithm had high computational cost and couldn’t effectively utilize the network historical data information, a joint US and power allocation (M-JEEPON) model based on graph neural network was proposed to realize joint US-BF optimization by combining the beam vector analytical solution.The simulation results show that the proposed algorithm can achieve the performance matching or even better than traditional optimization algorithms with lower computational overhead.

Fusion of Focal Loss’s cyber threat intelligence entity extraction

Yuanbo GUO, Yongfei LI, Qingli CHEN, Chen FANG, Yangyang HU

2022, 43(7):  85-92.  doi:10.11959/j.issn.1000-436x.2022132

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Cyber threat intelligence contains a wealth of knowledge of threat behavior.Timely analysis and process of threat intelligence can promote the transformation of defense from passive to active.Nowadays, most threat intelligence that exists in the form of natural language texts contains a large amount of unstructured data, which needs to be converted into structured data for subsequent processing using entity extraction methods.However, since threat intelligence contains numerous terminology such as vulnerability names, malware and APT organizations, and the distribution of entities are extremely unbalanced, the performance of extraction methods in general field are severely limited when applied to threat intelligence.Therefore, an entity extraction model integrated with Focal Loss was proposed, which improved the cross-entropy loss function and balanced sample distribution by introducing balance factor and modulation coefficient.In addition, for the problem that threat intelligence had a complex structure and a wide range of sources, and contained a large number of professional words, token and character features were added to the model, which effectively improved OOV (out of vocabulary) problem in threat intelligence.Experiment results show that compared with existing mainstream model BiLSTM and BiLSTM-CRF, the F1 scores of the proposed model is increased by 7.07% and 4.79% respectively, which verifies the effectiveness of introducing Focal Loss and character features.

High maneuvering target tracking ATPM-IMM algorithm

Hao ZENG, Wangqiang MU, Shunping YANG

2022, 43(7):  93-101.  doi:10.11959/j.issn.1000-436x.2022135

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For high maneuvering target tracking, the accuracy of tracking will degrade in common IMM algorithm due to the fixed transition probability matrix.Therefore, a new ATPM-IMM algorithm for high maneuvering target tracking was proposed, which could update the transition probability matrix adaptively.The proposed algorithm requires less prior information of model posterior probability and transition probability matrix, it is suitable for both high and weak maneuvering target tracking.Simulation results demonstrate that the filtering accuracy of the proposed algorithm is improved about 11% compared with the existing algorithms.

Traffic evaluation and modeling for spatio-temporal slicing based VR panoramic video transmission

Shengqian HAN, Han LOU, Junlai WANG

2022, 43(7):  102-112.  doi:10.11959/j.issn.1000-436x.2022139

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To characterize the traffic load of virtual reality (VR) panoramic video under the spatio-temporal slicing transmission mode, firstly, based on a real-word VR video viewing dataset, the relationship between the traffic load and the spatio-temporal slicing strategy was evaluated via simulations.Secondly, on the basis of the simulation results, a statistical traffic model was established, describing the interaction between the traffic load and the number of spatial tiles as well as the duration of temporal chunks.Finally, based on the established model, a hierarchical spatial-temporal slicing strategy optimization method was proposed, where the outer layer found the optimal number of spatial tiles by exhaustive searching while the inner layer could directly compute the optimal duration of temporal chunks.Research and simulation results show that the traffic load approximately follows a normal distribution, where the mean and variance are functions of the spatio-temporal slicing strategy.By establishing the traffic model for each type of videos, the optimized slicing strategy based on the established model performs close to the optimal strategy obtained by searching by simulations.

Efficient resource allocation with context-awareness for parked car road side unit-based Internet of vehicles

Peng QIN, Haoting HE, Xiongwen ZHAO, Yang FU, Yu ZHANG, Miao WANG, Shuo WANG, Xue WU

2022, 43(7):  113-125.  doi:10.11959/j.issn.1000-436x.2022129

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In order to reduce the number of road side unit (RSU) and meet the needs of mobile vehicle users for processing latency-sensitive and computation-intensive tasks, a context-aware resource allocation algorithm was proposed based on parked cars.Road side parked vehicles were selected to serve the vehicle users instead of RSU using parking time as a factor for determining whether a parked vehicle could become a parked car roadside unit (PCRSU).In order to further reduce the system response time, a mechanism was designed considering content caching and distribution.In respect to user content caching, PCRSU made personalized content recommendation for vehicle users with awareness by using two elements of user historical search data and point of interest (PoI) area type.In respect to content distribution, PCRSU allocated bandwidth efficiently by sensing the data transmission needs of vehicle users.Extensive experiments show that compared with the existing benchmark methods, the proposed algorithm can select PCRSU more reasonably, effectively reduce the demand response delay, improve the stability of the network while ensuring network coverage, and provide more accurate service content for vehicle users.

Construction de Bruijn sequence based on whole LFSR with 4 cycles

Congwei ZHOU, Bin HU, Jie GUAN

2022, 43(7):  126-133.  doi:10.11959/j.issn.1000-436x.2022108

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In order to improve the construction efficiency and widen the application depth of cycle-joining method, from the view of the number of cycles in the cycle structure, a method for constructing de Bruijn sequence based on whole LFSR with 4 cycles was proposed.Based on the characteristic of cascade connection of LFSR, the cycle structure of a class of cascaded feedback shift registers was determined.Accordingly, the exact number of whole n-order LFSR with 4 cycles was given, and the total number of n-order de Bruijn sequences constructed from whole n-order LFSR with 4 cycles as well.

Lightweight decentralized learning-based automatic modulation classification method

Jie YANG, Biao DONG, Xue FU, Yu WANG, Guan GUI

2022, 43(7):  134-142.  doi:10.11959/j.issn.1000-436x.2022145

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In order to solve the problems in centralized learning, a lightweight decentralized learning-based AMC method was proposed.By the proposed decentralized learning, a global model was trained through local training and model weight sharing, which made full use of the dataset of each communication nodes and avoided the user data leakage.The proposed lightweight network was stacked by a number of different lightweight neural network blocks with a relatively low space complexity and time complexity, and achieved a higher recognition accuracy compared with traditional DL models, which could effectively solve the problems of computing power and storage space limitation of edge devices and high communication overhead in decentralized learning based AMC method.The experimental results show that the classification accuracy of the proposed method is 62.41% based on RadioML.2016.10 A.Compared with centralized learning, the training efficiency is nearly 5 times higher with a slight classification accuracy loss (0.68%).In addition, the experimental results also prove that the deployment of lightweight models can effectively reduce communication overhead in decentralized learning.

Multiview clustering method for view-unaligned data

Ao LI, Cong FENG, Yutong NIU, Shibiao XU, Yingtao ZHANG, Guanglu SUN

2022, 43(7):  143-152.  doi:10.11959/j.issn.1000-436x.2022134

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A new challenge for multi-view learning was posed by corrupted view-correspondences.To address this issue， an effective multi-view learning method for view-unaligned data was proposed.First，to capture cross-view latent affinity in multi-view heterogenous feature spaces，representation learning was employed based on multi-view non-negative matrix factorization to embed original features into a measurable low-dimensional subspace.Second, view-alignment relationships were modeled as optimal matching of a bipartite graph, which could be generalized to multiple-views situations via the proposed concept reference view.Representation learning and data alignment were further integrated into a unified Bi-level optimization framework to mutually boost the two learning processes, effectively enhancing the ability to learn from view-unaligned data.Extensive experimental results of view-unaligned clustering on three public datasets demonstrate that the proposed method outperforms eight advanced multiview clustering methods on multiple evaluation metrics.

Person re-identification with intra-domain similarity grouping based on semantic fusion

Qiqi KOU, Ji HUANG, Deqiang CHENG, Yunlong LI, Jianying ZHANG

2022, 43(7):  153-162.  doi:10.11959/j.issn.1000-436x.2022136

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Unsupervised cross-domain person re-identification aims to adapt a model trained on a labeled source-domain dataset to a target-domain dataset.However, the cluster-based unsupervised cross-domain pedestrian re-identification algorithm often generates noise due to the different input pedestrian pictures during the network feature learning process, which affects the clustering results.To solve this problem, An intra-domain similarity grouping pedestrian re-identification network based on semantic fusion was proposed.Firstly, a semantic fusion layer was added on the basis of the Baseline network, and the semantic fusion of similar features was performed on the intermediate feature maps from the two aspects of space and channel in turn, so as to improve the adaptive perception ability of the network.In addition, by making full use of the fine-grained information of intra-domain similarity, the network’s clustering accuracy of global and local features was improved.Experiments were carried out on three public datasets, DukeMTMC-ReID, Market1501, MSMT17, and the results demonstrate that the mAP and Rank recognition accuracy are significantly improved compared with recent unsupervised cross-domain person re-identification algorithms.

Self-supervised speech representation learning based on positive sample comparison and masking reconstruction

Wenlin ZHANG, Xuepeng LIU, Tong NIU, Qi CHEN, Dan QU

2022, 43(7):  163-171.  doi:10.11959/j.issn.1000-436x.2022142

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To solve the problem that existing contrastive prediction based self-supervised speech representation learning methods need to construct a large number of negative samples, and their performance depends on large training batches, requiring a lot of computing resources, a new speech representation learning method based on contrastive learning using only positive samples was proposed.Combined with reconstruction loss, the proposed method could obtain better representation with lower training cost.The proposed method was inspired by the idea of the SimSiam method in image self-supervised representation learning.Using the siamese network architecture, two random augmentations of the input speech signals were processed by the same encoder network, then a feed-forward network was applied on one side, and a stop-gradient operation was applied on the other side.The model was trained to maximize the similarity between two sides.During training processing, negative samples were not required, so small batch size could be used and training efficiency was improved.Experimental results show that the representation model obtained by the new method achieves or exceeds the performance of existing mainstream speech representation learning models in multiple downstream tasks.

Comprehensive Reviews

Review of threat discovery and forensic analysis based on system provenance graph

Tao LENG, Lijun CAI, Aimin YU, Ziyuan ZHU, Jian’gang MA, Chaofei LI, Ruicheng NIU, Dan MENG

2022, 43(7):  172-188.  doi:10.11959/j.issn.1000-436x.2022105

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By investigating works of literature related to provenance graph research, a research framework for network threat discovery and forensic analysis based on system-level provenance graph was proposed.A detailed overview of data collection, data management, data query, and visualization methods based on provenance graphs was provided.The rule-based, anomaly-based, and learning-based threat detection classification methods were proposed.Threats based on threat intelligence or based on strategy, technology, and process-driven threats hunting methods were summarized.Forensic analysis methods based on causality, sequence learning, language query and semantic reconstruction in special fields were summarized.Finally, the future research trends were pointed out.

Key technologies of 6G mobile network

Haijun ZHANG, Anqi CHEN, Yabo LI, Keping LONG

2022, 43(7):  189-202.  doi:10.11959/j.issn.1000-436x.2022140

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The development and social needs of mobile communication networks were firstly introduced.Then four key technologies of 6G network were respectively introduced from the perspectives of communication spectrum, coverage dimension, communication empowerment, and new paradigm, namely terahertz (THz) communication, space-air-ground-sea integration network, artificial intelligence (AI), and semantic communication.The related researches of the four key technologies in recent years were analyzed, and some typical application scenarios, coverage scheme, technique principles, etc., were summarized.Finally, based on the summary, main problems of the four technologies were proposed for the future development.Besides, other candidate technologies of 6G network, including integrated sensing and communication, reconfigurable intelligence surface and new materials, blockchain, digital twin, and deterministic network technology, etc., were briefly discussed in the conclusion part.

Correspondences

Dispatching and control information freshness guaranteed resource optimization in simplified power Internet of things

Haijun LIAO, Zehan JIA, Zhenyu ZHOU, Nian LIU, Fei WANG, Zhong GAN, Xianjiong YAO

2022, 43(7):  203-214.  doi:10.11959/j.issn.1000-436x.2022124

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Information freshness conducts an important impact on the training accuracy of the distributed energy dispatching and control model.Poor dispatching and control information freshness will increase the loss function of the training model, reduce the reliability and economy of dispatching and control, and effect the real-time balance of energy supply and demand.Simplified power Internet of things can provide plug-and-play and multi-mode fusion communication support for distributed energy dispatching and control, but it still faces challenges of the inadaptability between cross-domain resource optimization and model training, and the difficulty in guaranteeing dispatching and control information freshness.To solve the above challenges, an information freshness aware-based communication-and-computation collaborative optimization algorithm (IFAC³O) was proposed, and the information freshness deviation was regulated by the awareness of deficit virtual queue evolution.On this basis, IFAC³O leveraged deep Q network and dispatching and control information freshness awareness to learn the channel allocation and batch size joint optimization strategy, thereby minimizing model loss function while guaranteeing long-term dispatching and control information freshness constraints.Compared with the federated DRL based low-latency resource allocation algorithm and adaptive federated learning-based batch size optimization algorithm, IFAC³O can reduce global loss function by 63.29% and 38.88% as well as improve information freshness by 20.59% and 57.69%.

Joint QoS prediction for Web services based on deep fusion of features

Jianxun LIU, Linghang DING, Guosheng KANG, Buqing CAO, Yong XIAO

2022, 43(7):  215-226.  doi:10.11959/j.issn.1000-436x.2022107

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In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed with considering of the hidden environmental preference information in QoS and the common features of multi-class QoS.First, QoS data was modeled as a user-service bipartite graph and multi-component graph convolution neural network was used for feature extraction and mapping, and the weighted fusion method was used for the same dimensional mapping of multi-class of QoS features.Subsequently, the attention factor decomposition machine was used to extract the first-order features, second-order interactive features, and high-order interactive features of the mapped feature vector.Finally, the results of each part were combined to achieve the joint QoS prediction.The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE).