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25 July 2023, Volume 44 Issue 7

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Papers

AoI-oriented low-energy-consumption information collection and transmission scheduling mechanism for emergency UAV networks

Yuming ZHANG, Lianming XU, Siyuan YIN, Linrun JIANG, Li WANG, Aiguo FEI

2023, 44(7):  1-13.  doi:10.11959/j.issn.1000-436x.2023116

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To address the information collection and aggregation issue in emergency scenarios characterized by the lack of public infrastructure and unstable satellite signals, an information timeliness-oriented information collection and transmission scheduling mechanism was proposed for emergency unmanned aerial vehicle (UAV) networks where information collection and transmission capabilities were constrained by energy consumption.Considering the age of information (AoI) as the metric and constraint of information timeliness, a stochastics optimization problem was constructed with the objective of minimizing UAV information collection and transmission energy consumption.By resorting to the Lyapunov optimization technique, virtual queues were established to impose information timeliness constraints on queue lengths, and the original problem was decoupled into two sub-problems, information collection and transmission scheduling, with the premise of ensuring system stability.Corresponding heuristic algorithms were proposed for each sub-problem.Simulation results demonstrate that the proposed algorithm outperforms conventional queue scheduling approaches in convergence rates and system energy consumption with guaranteed information timeliness.

Semantic communication system with efficient integration of global and local context features

Peng LUO, Yueling LIU, Yuyuan ZHANG, Kuo CAO, Haitao ZHAO, Jibo WEI

2023, 44(7):  14-25.  doi:10.11959/j.issn.1000-436x.2023133

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A communication system based on extended contextual semantic features was proposed by using an end-to-end integrated design method based on deep learning.Unlike existing research that focused only on local context while neglecting global context, the proposed system integrated both local and global contextual knowledge, semantic encoding and decoding was utilized by extended contextual knowledge, thereby enhancing the reliability of the semantic communication system.At the transmitter, efficient semantic representation was achieved through extended contextual semantic encoding.At the receiver, the accuracy of semantic inference was improved by combining mechanisms such as historical communication text mining, contextual semantic feature learning, and heuristic graph-based decoding strategy.When comparing with the traditional communication system and the existing semantic communication systems, simulation results demonstrate that the proposed system significantly improves the reliability of the communication system under low signal-to-noise ratio.

Joint resource configuration method for secure fog computing Internet of things

Shibo ZHANG, Hongyuan GAO, Yumeng SU, Jianhua CHENG, Lishuai ZHAO

2023, 44(7):  26-37.  doi:10.11959/j.issn.1000-436x.2023134

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The current fog computing Internet of things (IoT) systems are prone to eavesdropping attacks in the process of physical communication transmission, and the existing research on fog computing has not effectively solved this problem.In order to realize the physical layer security (PLS) communication of the fog computing IoT to against the malicious eavesdropping of multiple eavesdroppers, a physical-layer secure fog computing Internet of things (PSFC-IoT) system was proposed.The secrecy rate of the proposed system in the secure communication scenario was analyzed, a joint resource management method based on the quantum bean optimization algorithm (QBOA) was proposed to solve the resource allocation problem of the network.The convergence performance of the proposed method was analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on system secrecy performance.In addition, the simulation results show that the proposed method can achieve the best secrecy performance for various communication scenarios.

Multi-UAV cooperative channel model for emergency communication

Lu BAI, Mingran SUN, Ziwei HUANG, Tao FENG, Xiang CHENG

2023, 44(7):  38-50.  doi:10.11959/j.issn.1000-436x.2023058

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To carry out the system design and technology development for emergency communications, and greatly improve our ability of risk prevention and emergency disposal, the research on multi-unmanned aerial vehicle (multi-UAV) cooperative channel model for emergency communications was carried out.First, the channel dataset of multi-UAV cooperative emergency communications in two typical emergency environments, i.e., the urban earthquake and suburban blizzard, was established.The channel parameters and statistical properties of multi-UAV cooperative emergency communications were analyzed.Second, a more general geometry-based stochastic channel model (GBSM) was proposed for multi-UAV cooperative emergency communications.The channel impulse response (CIR) and the cooperative non-station arity of multi-UAV cooperative channels in the space and time domains in multi-UAV cooperative emergency communications were calculated and captured.Finally, the channel characteristics of multi-UAV cooperative emergency communication in urban earthquake and suburban blizzard scenarios were analyzed, which could support the practical implementation of multi-UAV cooperative emergency communications.Meanwhile, the statistical characteristic of the proposed model can fit well with that of ray-tracing-based channel data, verifying the utility of the proposed model.

Joint optimization method of intelligent service arrangement and computing-networking resource allocation for MEC

Yun LI, Qian GAO, Zhixiu YAO, Shichao XIA, Jishen LIANG

2023, 44(7):  51-63.  doi:10.11959/j.issn.1000-436x.2023125

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To solve the problems of low efficiency of network service caching and computing-networking resource allocation caused by tasks differentiation, highly dynamic network environment, and decentralized computing-networking resource deployment in edge networks, a decentralized service arrangement and computing offloading model for mobile edge computing was investigated and established.Considering the multidimensional resource constraints, e.g., computing power, storage, and bandwidth, with the objective of minimizing task processing latency, the joint optimization of service caching and computing-networking resource allocation was abstracted as a partially observable Markov decision process.Considering the temporal dependency of service request and its coupling relationship with service caching, a long short-term memory network was introduced to capture time-related network state information.Then, based on recurrent multi-agent deep reinforcement learning, a distributed service arrangement and resource allocation algorithm was proposed to autonomously decide service caching and computing-networking resource allocation strategies.Simulation results demonstrate that significant performance improvements in terms of cache hit rate and task processing latency achieved by the proposed algorithm.

Outage performance analysis of IRS-aided cognitive satellite-terrestrial network

Min LIN, Huaibo GUO, Xiaoyu LIU, Lue HAN, Miaomiao TAN, Lyuxi YANG

2023, 44(7):  64-75.  doi:10.11959/j.issn.1000-436x.2023124

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The outage performance of intelligent reflecting surface (IRS) aided cognitive satellite-terrestrial network was analyzed.Firstly, for the scenario where the point beam was enabled at the satellite to serve the earth station, and the IRS and non-orthogonal multiple access (NOMA) were deployed at the base station to expand the communication range and serve multiple users, respectively, under the condition that cognitive radio technology was employed to enable spectrum sharing between the satellite primary network and the terrestrial secondary network, an optimization problem was established to maximize the average signal-to-noise ratio of terrestrial users.Secondly, a low complexity IRS phase shift design was proposed by using channel state information based on the angle domain, and the phase shift matrix of IRS was obtained.Then, the closed-form expressions of outage probabilities of terrestrial users were derived when users were static and moving, respectively.Furthermore, the asymptotic outage probability formulas at high signal-to-noise ratio were also derived to analyze the system performance.Finally, the correctness of derivation and the influence of main parameters on outage performance were verified by computer simulations.

Adaptive federated learning secure aggregation scheme based on threshold homomorphic encryption

Zhuo MA, Jiayu JIN, Yilong YANG, Yang LIU, Zuobin YING, Teng LI, Junwei ZHANG

2023, 44(7):  76-85.  doi:10.11959/j.issn.1000-436x.2023140

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Aiming at the communication bottleneck problem when the current federated learning security aggregation algorithm was applied in a complex network environment, an adaptive federated learning secure aggregation scheme based on threshold homomorphic encryption was proposed.While protecting gradient privacy, users adaptively compress gradients based on the current available bandwidth, greatly reduced communication overhead for federated users.Furthermore, the new dynamic decryption task distribution algorithm and gradient combination algorithm were designed in the phase of aggregation gradient decryption, which relieved the user’s uplink communication pressure.The experimental results show that the proposed scheme can sharply reduce the amount of communication to 4% compared with the existing federated learning scheme with a trivial model accuracy loss of 1%.

Research on intrusion detection method of marine meteorological sensor network based on anomalous behaviors

Xin SU, Tian TIAN, Gong Ziyang, Yiqing ZHOU

2023, 44(7):  86-99.  doi:10.11959/j.issn.1000-436x.2023132

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To deal with the abnormal data flow attacks faced by the marine meteorological sensor network (MMSN), analyze the security mechanism, and aim at the complex and huge network structure and the extremely imbalanced data flow in the nodes, the intrusion detection method of marine meteorological sensor network based on anomalous behaviors was studied, and intrusion detection system (IDS) was built.The imbalance of dataset was considered emphatically, and the effective data generation was realized by using depth generation network CVAE-GAN to learn the distribution of minority classes in the dataset.OPTICS-based denoising algorithm was used to remove the noise points in majority classes and clarify the category boundaries.From the data perspective, the imbalance rate of dataset was reduced, the influence of imbalanced dataset on IDS was reduced, and the ability of classifier to identify minority classes of abnormal traffic was improved.The simulation results show that the proposed system can effectively identify all kinds of abnormal traffic, especially minority classes of them, and the imbalanced dataset processing method can significantly improve the detection ability of the classifier.

Redactable blockchain supporting trapdoor revocation and limited number of redactions

Yue CHEN, Zenghang HAO, Jianghong WEI, Xuexian HU, Dongmei YANG

2023, 44(7):  100-113.  doi:10.11959/j.issn.1000-436x.2023135

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Aiming at the problem that although the existing redactable blockchain schemes that support trapdoor revocation could revoke the redaction permissions of trapdoor holders, but they were unable to limit the number of redactions using trapdoors, indicating an issue where the management of editing permissions was not adequately refined, a revocable chameleon hash with limited number of redactions was proposed, based on which a new redactable blockchain scheme was proposed.Specifically, the proposed scheme included a master trapdoor, a subordinate trapdoor, and the generated witness.The subordinate trapdoor was deployed for data modification, while the master trapdoor was employed to revoke the subordinate trapdoor, thus accomplishing the objective of revoking editing permissions.Meanwhile, the witness was developed to strictly limit the number of edits via the subordinate trapdoor to one time.The proposed scheme is proved to be secure under the standard complexity assumptions.Theoretical analysis and simulation experiments indicate that the proposed scheme has advantages in terms of security guarantee, when compared with the existing schemes supporting trapdoor revocation.At the same time it introduces little additional computation overhead, and thus has certain practicality.

Collusion node detection method based on fuzzy evaluation density clustering in Internet of vehicles

Haibo ZHANG, Dabin WANG, Ruyan WANG, Dongyu WANG

2023, 44(7):  114-123.  doi:10.11959/j.issn.1000-436x.2023096

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In order to solve the security problems caused by collusion of malicious vehicles in the open Internet of vehicles environment, a collusion node detection method based on fuzzy evaluation density clustering was designed.The method realized vehicle reputation update through subjective recommendation trust, objective data trust and historical reputation.Two rounds malicious node detection method was designed to detect malicious vehicles, in the first round, fuzzy comprehensive evaluation was used to screen out a single malicious vehicle node, and in the second round, the conspiracy malicious vehicle nodes were searched by the improved density clustering method according to a single malicious vehicle node to ensure the sustainable development of network environment security.Experimental results show that the proposed method has a high recognition rate for malicious vehicles, and when the proportion of malicious vehicles reaches 30%, the detection accuracy can still remain above 90%.At the same time, under the proportion of different malicious vehicles, the detection recall rate and detection F value still remain higher value, indicating that the proposed method has a high stability.For the change of total vehicle nodes, the performance evaluation index shows a small fluctuation and remain within the range from 80% to 100%.

Computation offloading and resource allocation strategy based on deep reinforcement learning

Feng ZENG, Zheng ZHANG, Zhigang CHEN

2023, 44(7):  124-135.  doi:10.11959/j.issn.1000-436x.2023139

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In order to expand the coverage and computing power of vehicle edge network, a computation offloading architecture was proposed for space-air-ground integrated vehicle network (SAGVN).With the consideration of the delay and energy consumption constraints of computing tasks, as well as the spectrum, computing and storage constraints in the SAGVN, the joint optimization problem of computation offloading decision and resource allocation was modeled as a mixed integer nonlinear programming problem.Based on the reinforcement learning method, the original problem was transformed into a Markov process, and a deep reinforcement learning algorithm was proposed to solve the problem.The proposed algorithm has the good convergence.The simulation results show that the proposed algorithm outperforms other algorithms in terms of task delay and success rate.

D2D cooperative caching strategy based on graph collaborative filtering model

Ningjiang CHEN, Linming LIAN, Pingjie OU, Xuemei YUAN

2023, 44(7):  136-148.  doi:10.11959/j.issn.1000-436x.2023131

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A D2D cooperative caching strategy based on graph collaborative filtering model was proposed for the problem of difficulty in obtaining sufficient data to predict user preferences in device-to-device (D2D) caching due to the limited signal coverage of base stations.Firstly, a graph collaborative filtering model was constructed, which captured the higher-order connectivity information in the user-content interaction graph through a multilayer graph convolutional neural network, and a multilayer perceptron was used to learn the nonlinear relationship between users and content to predict user preferences.Secondly, in order to minimize the average access delay, considering user preference and cache delay benefit, the cache content placement problem was modeled as a Markov decision process model, and a cooperative cache algorithm based on deep reinforcement learning was designed to solve it.Simulation experiments show that the proposed caching strategy achieves optimal performance compared with existing caching strategies for different content types, user densities, and D2D communication distance parameters.

Stereo robust watermark algorithm based on parameter optimization

Yiming XUE, Jinyu ZHANG, Botao CHEN, Zhenghong YANG

2023, 44(7):  149-158.  doi:10.11959/j.issn.1000-436x.2023123

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Aiming at issues such as illegal dissemination of network audio data and privacy leakage, a stereo robust watermark algorithm based on parameter optimization was proposed.Firstly, the watermark was embedded by modifying the singular value ratio of odd segment based on the insensitivity of stereo channels to attack, and aiming at minimizing the relative distortion of the two channels.The embedding process was optimized and the perceived quality of the audio was improved.Then, a parameter optimization method was proposed to find optimal parameters on each audio file, and the imperceptibility and robustness were well balanced.The simulation results show that the proposed algorithm has good imperceptibility and robustness against conventional signal processing.

Machine learning-based detection, identification and restoration method of jamming attacks in optical networks

Xiaoxue GONG, Jiahao PANG, Qihan ZHANG, Changle XU, Wenshuai QIN, Lei GUO

2023, 44(7):  159-170.  doi:10.11959/j.issn.1000-436x.2023127

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Optical networks are vulnerable to signal jamming attacks aimed at disrupting communication services due to their structural fragility.Based on this, a machine learning-based jamming attacks detection, identification and restoration framework was proposed.In terms of attacks detection and identification, the performances of BiLSTM, 1DCNN, and seven conventional machine learning classifiers (ANN, DT, KNN, LDA, NB, RF, and SVM) were evaluated in detecting the presence of attacks, and identifying different types of jamming attacks.In terms of attacks restoration, a BiLSTM-BiGRU-based jamming attacks restoration model was proposed to restore light-in-band, strong-in-band, light-out-of-band, and strong-out-of-band jamming attacks.Numerical simulation results reveal that the proposed model demonstrates excellent performance with a detection and identification accuracy of 99.20%, with attack restoration ratios of 95.05%, 97.03%, 94.06%, and 61.88%, respectively.

Novel distinguisher for SM4 cipher algorithm based on deep learning

Huijiao WANG, Xin ZHANG, Yongzhuang WEI, Lingchen LI

2023, 44(7):  171-184.  doi:10.11959/j.issn.1000-436x.2023141

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A method was proposed to construct a deep learning distinguisher model for large state block ciphers with large-block and long-key in view of the problem of high data complexity, time complexity and storage complexity of large state block cipher distinguishers, and the neural distinguishers were constructed for SM4 algorithm.Drawing inspiration from the idea that ciphertext difference could improve the performance of distinguishers, a new input data format for neural distinguisher was designed by using partial difference information between ciphertext pairs as part of the training data.The residual neural network model was used to construct the neural distinguisher.The training dataset for large blocks was preprocessed.Additionally, an improved strategy for model relearning was proposed to address the high specificity and low sensitivity of the constructed distinguisher.Experimental results show that the proposed deep learning model for SM4 can achieve 9 rounds neural distinguisher.The accuracy of 4～9 rounds distinguishers can reach up to 100%, 76.14%, 65.20%, 59.28%, 55.89% and 53.73% respectively.The complexity and accuracy of the constructed differential neural distinguisher are significantly better than those of traditional differential distinguishers, and it is currently the best neural distinguisher for the block cipher SM4 to our knowledge.It also proves that the deep learning method is effective and feasible in the security analysis of block cipher of large block.

Research on dynamic cooperative technology of manned and unmanned networked information system

Haijun YE, Guofeng WANG, Zhiyong FENG

2023, 44(7):  185-196.  doi:10.11959/j.issn.1000-436x.2023129

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To meet the needs of information security sharing and systematic dynamic cooperation of manned and unmanned formations and to build a dynamic communication network, firstly, a networked high-precision system time synchronization scheme was designed, using integrated network link management strategy and wireless channel on-demand preference algorithm, and the time synchronization accuracy could be up to 100 nanosecond level.Secondly, a data privacy protection and secure sharing solution for airborne networks was built, implementing communication data security of manned and unmanned formation, and analyzing and discussing the performance and security to prove that the proposed solution has a provable high security strength.Meanwhile, a dynamic cooperation model of manned and unmanned formation was designed to discover, authenticate and revoke each node in real time, perform legitimacy identity verification, optimize allocation scheme for multi-objective information acquisition through multi-machine collaborative construction, differentiated data was used to transmit synchronize data, save network resources, improve data transmission efficiency, and meet the demand of systematic cooperative operations.

Trust evaluation model for distributed home photovoltaic collection scenarios in new power system

Li LI, Xiaolong WANG, Zhixin ZHANG, Rongliang SHI, Xu GUO

2023, 44(7):  197-206.  doi:10.11959/j.issn.1000-436x.2023137

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Aiming at the problem that the existing sensor network trust evaluation model could not be directly applied to the new power system distributed home photovoltaic collection scenario, which was difficult to meet the require-ments of strong computing power and high defense power of the new power system, a distributed dynamic trust evaluation model based on multi-index detection was proposed.Firstly, the communication trust evaluation based on Bayes was carried out according to the historical interaction of terminal acquisition nodes.Then, the currently col-lected data was evaluated by perceptual trust based on its historical data support degree and regional trust based on probability density.Finally, the entropy weight method was used to decentralize each trust module’s values dynamically.The node activeness and double reward and punishment mechanism were introduced to calculate the comprehensive trust value and realize the dynamic update.The experimental results show that the four levels of the trust evaluation model are suitable for the new power system environment and can be used to detect the distributed in 20 round period of household photovoltaic power generation collection given the signal in the scene, achieve dynamic and accurate trust evaluation of abnormal nodes in the case of physical environment factors, equipment quality factors, human misoperation and malicious intrusion.

Correspondences

Dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning

Runzi LIU, Tianci MA, Weihua WU, Chenhong YAO, Qinghai YANG

2023, 44(7):  207-217.  doi:10.11959/j.issn.1000-436x.2023130

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In recent years, with the increasing number of various emergency tasks, how to control the impact on common tasks while ensuring system revenue has become a huge challenge for the dynamic scheduling of relay satellite networks.Aiming at this problem, with the goal of maximizing the total revenue of emergency tasks and minimizing the damage to common tasks, a dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning was proposed.Specifically, in order to take into account the long-term and short-term performance of the system at the same time, a two-layer scheduling framework implemented by upper-level and lower-level DQN was designed.The upper-level DQN was responsible for determining the temporary optimization goal based on long-term performance, and the lower-level DQN determined the scheduling strategy for current task according to the optimization goal.Simulation results show that compared with traditional deep learning methods and the heuristic methods dealing with dynamic scheduling problems, the proposed method can improve the total revenue of urgent tasks while reducing the damage to common tasks.

Steganographer identification of JPEG image based on feature selection and graph convolutional representation

Qianqian ZHANG, Yi ZHANG, Hao LI, Yuanyuan MA, Xiangyang LUO

2023, 44(7):  218-229.  doi:10.11959/j.issn.1000-436x.2023128

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Aiming at the problem that the feature dimension of JPEG image steganalysis is too high, which leads to the complexity of distance calculation between users and a decrease in the identification performance of the steganographer, a method for steganographer recognition based on feature selection and graph convolutional representation was proposed.Firstly, the steganalysis features of the user’s images were extracted, and the feature subset with highseparability was selected.Then, the users were represented as a graph, and the features of users were obtained by training the graph convolutional neural network.Finally, because inter-class separability and intra-class aggregation were considered, the features of users that could capture the differences between users were learned.For steganographers who use JPEG steganography, such as nsF5, UED, J-UNIWARD, and so on, to embed secret information in images, the proposed method can reduce the feature dimensions and computing.The identification accuracy of various payloads can reach more than 80.4%, and it has an obvious advantage at the low payload.

Research on the application of weighted-Laguerre-FDTD method with “unidirectional explicit” matrix in cylindrical structure

Dawei ZHU, Hailin CHEN, Zhongwen SHEN, Xiuling JIA, Zhen LI

2023, 44(7):  230-242.  doi:10.11959/j.issn.1000-436x.2023126

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To solve the shortcomings of FDTD algorithm in electromagnetic wave propagation in communication applications.A FDTD algorithm under “unidirectional explicit” matrix was constructed and applied to cylindrical structure.Firstly, a new coefficient matrix was established by the ideology of WCS and WLP.Secondly, the perturbation terms were introduced to form the initial value and iterative equations.Finally, the fields on the z axis were treated in special way.Numerical examples prove that the proposed algorithm is superior to the WCS-FDTD and WLP-FDTD in electromagnetic wave propagation.The best improvement efficiency is 83%, accuracy is 88%, and memory consumption is reduced by 44%.Meanwhile, comparing with the conventional FDTD algorithm, it can improve the efficiency of 7 time steps size and reduce the memory consumption by 53% without losing accuracy.Moreover, under the boundary truncation of the PML, the proposed algorithm also shows good electromagnetic wave energy absorption performance, which can reach below -60 dB.