Current Issue

2024, Volume 33,  Issue 3

Survey on Fake Information Generation, Dissemination and Detection
CUI Wanqiu, WANG Dawei, HAN Na
2024, 33(3): 573-583. doi: 10.23919/cje.2022.00.362
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The current booming development of the Internet has put the public in an era of information overload, in which false information is mixed and spread unscrupulously. This phenomenon has seriously disturbed the social network order. Thus, a substantial amount of research is beginning to be devoted to the effective management of fake information. We analyze the abnormal characteristics of fake information from its mechanism of generation and dissemination. In view of different exceptional features, we systematically sort out and evaluate the existing studies on false content detection. The commonly used public datasets, metrics, and performance are categorized and compared, hoping to provide a basis and guidance for related research. The study found that the current active social platforms show different novelty. The future direction should point to mining platform features of multi-domain sources, multi-data forms, and multi-language heterogeneity to provide more valuable clues for fake information.
Review of GAN-Based Research on Chinese Character Font Generation
WANG Xuanhong, LI Cong, SUN Zengguo, HUI Luying
2024, 33(3): 584-600. doi: 10.23919/cje.2022.00.402
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With the rapid development of deep learning, generative adversarial network (GAN) has become a research hotspot in the field of computer vision. GAN has a wide range of applications in image generation. Inspired by GAN, a series of models of Chinese character font generation have been proposed in recent years. In this paper, the latest research progress of Chinese character font generation is analyzed and summarized. GAN and its development history are summarized. GAN-based methods for Chinese character font generation are clarified as well as their improvements, based on whether the specific elements of Chinese characters are considered. The public datasets used for font generation are summarized in detail, and various application scenarios of font generation are provided. The evaluation metrics of font generation are systematically summarized from both qualitative and quantitative aspects. This paper contributes to the in-depth research on Chinese character font generation and has a positive effect on the inheritance and development of Chinese culture with Chinese characters as its carrier.
A Survey on Graph Neural Network Acceleration: A Hardware Perspective
CHEN Shi, LIU Jingyu, SHEN Li
2024, 33(3): 601-622. doi: 10.23919/cje.2023.00.135
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Graph neural networks (GNNs) have emerged as powerful approaches to learn knowledge about graphs and vertices. The rapid employment of GNNs poses requirements for processing efficiency. Due to incompatibility of general platforms, dedicated hardware devices and platforms are developed to efficiently accelerate training and inference of GNNs. We conduct a survey on hardware acceleration for GNNs. We first include and introduce recent advances of the domain, and then provide a methodology of categorization to classify existing works into three categories. Next, we discuss optimization techniques adopted at different levels. And finally we propose suggestions on future directions to facilitate further works.
Method and Practice of Trusted Embedded Computing and Data Transmission Protection Architecture Based on Android
WANG Yichuan, GAO Wen, HEI Xinhong, DU Yanning
2024, 33(3): 623-634. doi: 10.23919/cje.2022.00.196
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In recent years, the rapid development of Internet technology has constantly enriched people’s daily life and gradually changed from the traditional computer terminal to the mobile terminal. But with it comes the security problems brought by the mobile terminal. Especially for Android system, due to its open source nature, malicious applications continue to emerge, which greatly threatens the data security of users. Therefore, this paper proposes a method of trusted embedded static measurement and data transmission protection architecture based on Android to reduce the risk of data leakage in the process of terminal storage and transmission. We conducted detailed data and feasibility analysis of the proposed method from the aspects of time consumption, storage overhead and security. The experimental results show that this method can detect Android system layer attacks such as self-booting of the malicious module and improve the security of data encryption and transmission process effectively. Compared with the native system, the additional performance overhead is small.
New Algebraic Attacks on Grendel with the Strategy of Bypassing SPN Steps
QIAO Wenxiao, SUN Siwei, HU Lei
2024, 33(3): 635-644. doi: 10.23919/cje.2023.00.127
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The rapid development of modern cryptographic applications such as zero-knowledge, secure multi-party computation, fully homomorphic encryption has motivated the design of new so-called arithmetization-oriented symmetric primitives. As designing ciphers in this domain is relatively new and not well-understood, the security of these new ciphers remains to be completely assessed. In this paper, we revisit the security analysis of arithmetization-oriented cipher Grendel. Grendel uses the Legendre symbol as a component, which is tailored specifically for the use in zero-knowledge and efficiently-varifiable proof systems. At FSE 2022, the first preimage attack on some original full GrendelHash instances was proposed. As a countermeasure, the designer adds this attack into the security analysis and updates the formula to derive the secure number of rounds. In our work, we present new algebraic attacks on GrendelHash. For the preimage attack, we can reduce the complexity or attack one more round than previous attacks for some instances. In addition, we present the first collision attack on some round-reduced instances by solving the constrained input/constrained output problem for the underlying permutations.
QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams
NIU Zequn, XUE Jingfeng, WANG Yong, LEI Tianwei, HAN Weijie, GAO Xianwei
2024, 33(3): 645-656. doi: 10.23919/cje.2022.00.360
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In practical abnormal traffic detection scenarios, traffic often appears as drift, imbalanced and rare labeled streams, and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic detection. Researchers have extensive studies on malicious traffic detection with single challenge, but the detection of complex traffic has not been widely noticed. Queried adaptive random forests (QARF) is proposed to detect traffic streams with concept drift, imbalance and lack of labeled instances. QARF is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling strategy. QARF achieves querying a small number of instances from unlabeled traffic streams to obtain effective training. We conduct experiments using the NSL-KDD dataset to evaluate the performance of QARF. QARF is compared with other state-of-the-art methods. The experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD dataset. QARF performs better than other state-of-the-art methods in comparisons.
Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network
WANG Hao, WANG Jinwei, HU Xuelong, HU Bingtao, YIN Qilin, LUO Xiangyang, MA Bin, SUN Jinsheng
2024, 33(3): 657-671. doi: 10.23919/cje.2022.00.179
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Detection of color images that have undergone double compression is a critical aspect of digital image forensics. Despite the existence of various methods capable of detecting double Joint Photographic Experts Group (JPEG) compression, they are unable to address the issue of mixed double compression resulting from the use of different compression standards. In particular, the implementation of Joint Photographic Experts Group 2000 (JPEG2000) as the secondary compression standard can result in a decline or complete loss of performance in existing methods. To tackle this challenge of JPEG+JPEG2000 compression, a detection method based on quaternion convolutional neural networks (QCNN) is proposed. The QCNN processes the data as a quaternion, transforming the components of a traditional convolutional neural network (CNN) into a quaternion representation. The relationships between the color channels of the image are preserved, and the utilization of color information is optimized. Additionally, the method includes a feature conversion module that converts the extracted features into quaternion statistical features, thereby amplifying the evidence of double compression. Experimental results indicate that the proposed QCNN-based method improves, on average, by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.
Related-Key Zero-Correlation Linear Attacks on Block Ciphers with Linear Key Schedules
2024, 33(3): 672-682. doi: 10.23919/cje.2022.00.419
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Related-key model is a favourable approach to improve attacks on block ciphers with a simple key schedule. However, to the best of our knowledge, there are a few results in which zero-correlation linear attacks take advantage of the related-key model. We ascribe this phenomenon to the lack of consideration of the key input in zero-correlation linear attacks. Concentrating on the linear key schedule of a block cipher, we generalize the zero-correlation linear attack by using a related-key setting. Specifically, we propose the creation of generalized linear hulls (GLHs) when the key input is involved; moreover, we indicate the links between GLHs and conventional linear hulls (CLHs). Then, we prove that the existence of zero-correlation GLHs is completely determined by the corresponding CLHs and the linear key schedule. In addition, we introduce a method to construct zero-correlation GLHs by CLHs and transform them into an integral distinguisher. The correctness is verified by applying it to SIMON16/16, a SIMON-like toy cipher. Based on our method, we find 12/13/14/15/15/17/20/22-round related-key zero-correlation linear distinguishers of SIMON32/64, SIMON48/72, SIMON48/96, SIMON64/96, SIMON64/128, SIMON96/144, SIMON128/192 and SIMON128/256, respectively. As far as we know, these distinguishers are one, two, or three rounds longer than current best zero-correlation linear distinguishers of SIMON.
New Related-Tweakey Boomerang Attacks and Distinguishers on Deoxys-BC
LIU Jiamei, TAN Lin, XU Hong
2024, 33(3): 683-693. doi: 10.23919/cje.2022.00.383
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Deoxys-BC is the primitive tweakable block cipher of the Deoxys family of authenticated encryption schemes. Based on existing related-tweakey boomerang distinguishers, this paper improves the boomerang attacks on 11-round Deoxys-BC-256 and 13-round Deoxys-BC-384 by the optimized key guessing and the precomputation technique. It transfers a part of subtweakey guess in the key-recovery phase to the precomputation resulting in a significant reduction of the overall time complexity. For 11-round Deoxys-BC-256, we give a related-tweakey boomerang attack with time/data/memory complexities of $2^{218.6}/2^{125.7}/2^{125.7}$, and give another attack with the less time complexity of $2^{215.8}$ and memory complexity of $2^{120}$ when the adversary has access to the full codebook. For 13-round Deoxys-BC-384, we give a related-tweakey boomerang attack with time/data/memory complexities of $2^{k-96}+2^{157.5}/2^{120.4}/2^{113}$. For the key size $k=256$, it reduces the time complexity by a factor of $2^{31}$ compared with the previous 13-round boomerang attack. In addition, we present two new related-tweakey boomerang distinguishers on 11-round Deoxys-BC-384 with the same probability as the best previous distinguisher.
IP-Pealing: A Robust Network Flow Watermarking Method Based on IP Packet Sequence
FENG Wangxin, LUO Xiangyang, LI Tengyao, YANG Chunfang
2024, 33(3): 694-707. doi: 10.23919/cje.2022.00.366
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Network flow watermarking (NFW) is usually used for flow correlation. By actively modulating some features of the carrier traffic, NFW can establish the correspondence between different network nodes. In the face of strict demands of network traffic tracing, current watermarking methods cannot work efficiently due to the dependence on specific protocols, demand for large quantities of packets, weakness on resisting network channel interferences and so on. To this end, we propose a robust network flow watermarking method based on IP packet sequence, called as IP-Pealing. It is designed to utilize the packet sequence as watermark carrier with IP identification field which is insensitive to time jitter and suitable for all IP based traffic. To enhance the robustness against packet loss and packet reordering, the detection sequence set is constructed in terms of the variation range of packet sequence, correcting the possible errors caused by the network transmission. To improve the detection accuracy, the long watermark information is divided into several short sequences to embed in turn and assembled during detection. By a large number of experiments on the Internet, the overall detection rate and accuracy of IP-Pealing reach 99.91% and 99.42% respectively. In comparison with the classical network flow watermarking methods, such as PROFW, IBW, ICBW, WBIPD and SBTT, the accuracy of IP-Pealing is increased by 13.70% to 54.00%.
Friendship Inference Based on Interest Trajectory Similarity and Co-occurrence
TIAN Junfeng, HOU Zhengqi
2024, 33(3): 708-720. doi: 10.23919/cje.2022.00.363
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Most of the current research on user friendship speculation in location-based social networks is based on the co-occurrence characteristics of users, however, statistics find that co-occurrence is not common among all users; meanwhile, most of the existing work focuses on mining more features to improve the accuracy but ignoring the time complexity in practical applications. On this basis, a friendship inference model named ITSIC is proposed based on the similarity of user interest tracks and joint user location co-occurrence. By utilizing MeanShift clustering algorithm, ITSIC clustered and filtered user check-ins and divided the dataset into interesting, abnormal, and noise check-ins. User interest trajectories were constructed from user interest check-in data, which allows ITSIC to work efficiently even for users without co-occurrences. At the same time, by application of clustering, the single-moment multi-interest trajectory was further proposed, which increased the richness of the meaning of the trajectory moment. The extensive experiments on two real online social network datasets show that ITSIC outperforms existing methods in terms of AUC score and time efficiency compared to existing methods.
Towards Semi-supervised Classification of Abnormal Spectrum Signals Based on Deep Learning
JIANG Tao, CHEN Wanqing, ZHOU Hangping, HE Jinyang, QI Peihan
2024, 33(3): 721-731. doi: 10.23919/cje.2022.00.395
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In order to cope with the heavy labor cost challenge of the manual abnormal spectrum classification and improve the effectiveness of existing machine learning schemes for spectral datasets with interference-to-signal ratios, we proposes a semi-supervised classification of abnormal spectrum signals (SSC-ASS), aimed at addressing some of the challenges in abnormal spectrum signal (ASS) classification tasks. A significant advantage of SSC-ASS is that it does not require manual labeling of every abnormal data, but instead achieves high-precision classification of ASSs using only a small number of labeled data. Furthermore, the method can to some extent avoid the introduction of erroneous information resulting from the complex and variable nature of abnormal signals, thereby improving classification accuracy. Specifically, SSC-ASS uses a memory AutoEncoder module to efficiently extract features from abnormal spectrum signals by learning from the reconstruction error. Additionally, SSC-ASS combines convolutional neural network and the K-means using a DeepCluster framework to fully utilize the unlabeled data. Furthermore, SSC-ASS also utilizes pre-training, category mean memory module and replaces pseudo-labels to further improve the classification accuracy of ASSs. And we verify the classification effectiveness of SSC-ASS on synthetic spectrum datasets and real on-air spectrum dataset.
Robust Regularization Design of Graph Neural Networks Against Adversarial Attacks Based on Lyapunov Theory
YAN Wenjie, LI Ziqi, QI Yongjun
2024, 33(3): 732-741. doi: 10.23919/cje.2022.00.342
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The robustness of graph neural networks (GNNs) is a critical research topic in deep learning. Many researchers have designed regularization methods to enhance the robustness of neural networks, but there is a lack of theoretical analysis on the principle of robustness. In order to tackle the weakness of current robustness designing methods, this paper gives new insights into how to guarantee the robustness of GNNs. A novel regularization strategy named Lya-Reg is designed to guarantee the robustness of GNNs by Lyapunov theory. Our results give new insights into how regularization can mitigate the various adversarial effects on different graph signals. Extensive experiments on various public datasets demonstrate that the proposed regularization method is more robust than the state-of-the-art methods such as $ L_1 $-norm, $ L_2 $-norm, $ L_{21} $-norm, Pro-GNN, PA-GNN and GARNET against various types of graph adversarial attacks.
Expression Complementary Disentanglement Network for Facial Expression Recognition
WANG Shanmin, SHUAI Hui, ZHU Lei, LIU Qingshan
2024, 33(3): 742-752. doi: 10.23919/cje.2022.00.351
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Disentangling facial expressions from other disturbing facial attributes in face images is an essential topic for facial expression recognition. Previous methods only care about facial expression disentanglement (FED) itself, ignoring the negative effects of other facial attributes. Due to the annotations on limited facial attributes, it is difficult for existing FED solutions to disentangle all disturbance from the input face. To solve this issue, we propose an expression complementary disentanglement network (ECDNet). ECDNet proposes to finish the FED task during a face reconstruction process, so as to address all facial attributes during disentanglement. Different from traditional reconstruction models, ECDNet reconstructs face images by progressively generating and combining facial appearance and matching geometry. It designs the expression incentive (EIE) and expression inhibition (EIN) mechanisms, inducing the model to characterize the disentangled expression and complementary parts precisely. Facial geometry and appearance, generated in the reconstructed process, are dealt with to represent facial expressions and complementary parts, respectively. The combination of distinctive reconstruction model, EIE, and EIN mechanisms ensures the completeness and exactness of the FED task. Experimental results on RAF-DB, AffectNet, and CAER-S datasets have proven the effectiveness and superiority of ECDNet.
Weighted Linear Loss Large Margin Distribution Machine for Pattern Classification
LIU Ling, CHU Maoxiang, GONG Rongfen, LIU Liming, YANG Yonghui
2024, 33(3): 753-765. doi: 10.23919/cje.2022.00.156
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Compared with support vector machine, large margin distribution machine (LDM) has better generalization performance. The central idea of LDM is to maximize the margin mean and minimize the margin variance simultaneously. But the computational complexity of LDM is high. In order to reduce the computational complexity of LDM, a weighted linear loss LDM (WLLDM) is proposed. The framework of WLLDM is built based on LDM and the weighted linear loss. The weighted linear loss is adopted instead of the hinge loss in WLLDM. This modification can transform the quadratic programming problem into a simple linear equation, resulting in lower computational complexity. Thus, WLLDM has the potential to deal with large-scale datasets. The WLLDM is similar in principle to the LDM algorithm, which can optimize the margin distribution and achieve better generalization performance. The WLLDM algorithm is compared with other models by conducting experiments on different datasets. The experimental results show that the proposed WLLDM has better generalization performance and faster training speed.
The Squeeze & Excitation Normalization Based nnU-Net for Segmenting Head & Neck Tumors
XIE Juanying, PENG Ying, WANG Mingzhao
2024, 33(3): 766-775. doi: 10.23919/cje.2022.00.306
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Head and neck cancer is one of the most common malignancies in the world. We propose SE-nnU-Net by adapting SE (squeeze and excitation) normalization into nnU-Net, so as to segment head and neck tumors in PET/CT images by combining advantages of SE capturing features of interest regions and nnU-Net configuring itself for a specific task. The basic module referred to convolution-ReLU-SE is designed for SE-nnU-Net. In the encoder it is combined with residual structure while in the decoder without residual structure. The loss function combines Dice loss and Focal loss. The specific data preprocessing and augmentation techniques are developed, and specific network architecture is designed. Moreover, the deep supervised mechanism is introduced to calculate the loss function using the last four layers of the decoder of SE-nnU-Net. This SE-nnU-net is applied to HECKTOR 2020 and HECKTOR 2021 challenges, respectively, using different experimental design. The experimental results show that SE-nnU-Net for HECKTOR 2020 obtained 0.745, 0.821, and 0.725 in terms of Dice, Precision, and Recall, respectively, while the SE-nnU-Net for HECKTOR 2021 obtains 0.778 and 3.088 in terms of Dice and median HD95, respectively. This SE-nnU-Net for segmenting head and neck tumors can provide auxiliary opinions for doctors’ diagnoses.
Extracting Integrated Features of Electronic Medical Records Big Data for Mortality and Phenotype Prediction
LI Fei, CHEN Yiqiang, GU Yang, WANG Yaowei
2024, 33(3): 776-792. doi: 10.23919/cje.2023.00.181
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The key to synthesizing the features of electronic medical records (EMR) big data and using them for specific medical purposes, such as mortality and phenotype prediction, is to integrate the individual medical event and the overall multivariate time series feature extraction automatically, as well as to alleviate data imbalance problems. This paper provides a general feature extraction method to reduce manual intervention and automatically process large-scale data. The processing uses two variational auto-encoders (VAEs) to automatically extract individual and global features. It avoids the well-known posterior collapse problem of Transformer VAE through a uniquely designed “proportional and stabilizing” mechanism and forms a unique means to alleviate the data imbalance problem. We conducted experiments using ICU-STAY patients’ data from the MIMIC-III database and compared them with the mainstream EMR time series processing methods. The results show that the method extracts visible and comprehensive features, alleviates data imbalance problems and improves the accuracy in specific predicting tasks.
Long Short-Term Memory Spiking Neural Networks for Classification of Snoring and Non-snoring Sound Events
ZHANG Rulin, LI Ruixue, LIANG Jiakai, YUE Keqiang, LI Wenjun, LI Yilin
2024, 33(3): 793-802. doi: 10.23919/cje.2022.00.210
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Snoring is a widespread occurrence that impacts human sleep quality. It is also one of the earliest symptoms of many sleep disorders. Snoring is accurately detected, making further screening and diagnosis of sleep problems easier. Snoring is frequently ignored because of its underrated and costly detection costs. As a result, this research offered an alternative method for snoring detection based on a long short-term memory based spiking neural network (LSTM-SNN) that is appropriate for large-scale home detection for snoring. We designed acquisition equipment to collect the sleep recordings of 54 subjects and constructed the sleep sound database in the home environment. And Mel frequency cepstral coefficients (MFCCs) were extracted from these sound signals and encoded into spike trains by a threshold encoding approach. They were classified automatically as non-snoring or snoring sounds by our LSTM-SNN model. We used the backpropagation algorithm based on an alternative gradient in the LSTM-SNN to complete the parameter update. The categorization percentage reached an impressive 93.4%, accompanied by a remarkable 36.9% reduction in computer power compared to the regular LSTM model.
Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy
SUN Yongkui, CAO Yuan, LI Peng, XIE Guo, WEN Tao, SU Shuai
2024, 33(3): 803-813. doi: 10.23919/cje.2022.00.075
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As one of the most important railway signaling equipment, railway point machines undertake the major task of ensuring train operation safety. Thus fault diagnosis for railway point machines becomes a hot topic. Considering the advantage of the anti-interference characteristics of vibration signals, this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals. A feature extraction method combining variational mode decomposition (VMD) and multiscale fluctuation-based dispersion entropy is developed, which is verified a more effective tool for feature selection. Then, a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed, which is validated more powerful than single feature selection methods. Finally, support vector machine is utilized for fault diagnosis. Experiment comparisons show that the proposed method performs best. The diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% respectively. Especially, it is a try to use new means for fault diagnosis on railway point machines, which can also provide references for similar fields.
Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises
LIU Junqi, WEN Tao, XIE Guo, CAO Yuan, Roberts Clive
2024, 33(3): 814-822. doi: 10.23919/cje.2022.00.234
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The fault diagnosis of railway point machines (RPMs) has attracted the attention of engineers and researchers. Seldom have studies considered diverse noises along the track. To fulfill this aspect, a multi-time-scale variational mode decomposition (MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple noises. MTSVMD decomposes condition monitoring signals after coarse-grained processing in varying degrees. In this manner, the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single scale. In the experimental validation, a random position, random type, random number, and random length (4R) noise-adding algorithm helps to verify the robustness of the approach. The adequate experimental results demonstrate the superiority of the proposed MTSVMD-based fault diagnosis.
Joint Optimization of Trajectory and Task Offloading for Cellular-Connected Multi-UAV Mobile Edge Computing
XIA Jingming, LIU Yufeng, TAN Ling
2024, 33(3): 823-832. doi: 10.23919/cje.2022.00.159
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Since the computing capacity and battery energy of unmanned aerial vehicle (UAV) are constrained, UAV as aerial user is hard to handle the high computational complexity and time-sensitive applications. This paper investigates a cellular-connected multi-UAV network supported by mobile edge computing. Multiple UAVs carrying tasks fly from a given initial position to a termination position within a specified time. To handle the large number of tasks carried by UAVs, we propose a energy cost of all UAVs based problem to determine how many tasks should be offloaded to high-altitude balloons (HABs) for computing, where UAV-HAB association, the trajectory of UAV, and calculation task splitting are jointly optimized. However, the formulated problem has nonconvex structure. Hence, an efficient iterative algorithm by applying successive convex approximation and the block coordinate descent methods is put forward. Specifically, in each iteration, the UAV-HAB association, calculation task splitting, and UAV trajectory are alternately optimized. Especially, for the nonconvex UAV trajectory optimization problem, an approximate convex optimization problem is settled. The numerical results indicate that the scheme of this paper proposed is guaranteed to converge and also significantly reduces the entire power consumption of all UAVs compared to the benchmark schemes.
A Secure Communicating While Jamming Approach for End-to-End Multi-Hop Wireless Communication Network
MA Xiao, LI Dan, WANG Liang, HAN Weijia, ZHAO Nan
2024, 33(3): 833-846. doi: 10.23919/cje.2022.00.448
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With the rapid development of wireless communications, cellular communication and distributed wireless network are fragile to eavesdropping due to distributed users and transparent communication. However, to adopt bigger transmit power at a given area to interfere potential eavesdroppers not only incurs huge energy waste but also may suppresses regular communication in this area. To this end, we focus on secure communication in multi-hop wireless communication network, and propose two communicating while jamming schemes for secure communication in presence of potential eavesdroppers for the narrow band and broad band point-to-point (P2P) systems respectively with the aid of artificial noise transmitted by a chosen cooperative interferer. Furthermore, to achieve the end-to-end (E2E) multi-hop secure communication, we devise the secure network topology discovering scheme via constructing a proper network topology with at least one proper node as the cooperative interferer in each hop, and then propose the secure transmission path planning scheme to find an E2E secure transmission route from source to destination, respectively. Experiments on the wireless open-access research platform demonstrate the feasibility of the proposed schemes. Besides, simulations results validate that the proposed schemes can achieve better performance compared with existing methods in both the P2P communication case and E2E multi-hop communication network scenario.
Energy-Efficient Driving Strategy for High-Speed Trains with Considering the Checkpoints
ZHANG Zixuan, CAO Yuan, SU Shuai
2024, 33(3): 847-858. doi: 10.23919/cje.2022.00.174
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With rising energy prices and concerns about environmental issues, energy-efficient driving strategies (EEDS) for high-speed trains have received a substantial amount of attention. In particular, energy-saving schemes play a huge role in reducing the energy and operating costs of trains. This article studies the EEDS of high-speed trains at a given time. A well-posed model is formulated, in which the constraints of the checkpoints, in addition to the speed limits, vehicle dynamics, and discrete control throttle, are first considered. For a given control sequence, the Karush-Kuhn-Tucker (KKT) conditions are used to obtain the necessary conditions for an EEDS. According to several key equations of the necessary conditions, the checkpoint constraints are satisfied. Some case studies are conducted based on the data of the Beijing-Shanghai high-speed line to illustrate the effectiveness of the proposed approach.