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Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

computers, Materials & Continua 2024年第1期78卷 1095-1113页

作者： Fang Xu Songhao Jiang Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China School of Computer Science and Information Engineering Hubei UniversityWuhan430062China Artificial Intelligence Industrial Technology Research Institute Hubei Engineering UniversityXiaogan432000China

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

关键词： SIoT data forwarding social attributes social relations community

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UAV-Assisted Multi-Object Computing Offloading for Blockchain-Enabled Vehicle-to-Everything Systems

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computers, Materials & Continua 2024年第12期81卷 3927-3950页

作者： Ting Chen Shujiao Wang Xin Fan Xiujuan Zhang Chuanwen Luo Yi Hong School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer Science Qufu Normal UniversityRizhao276826China School of Information Science and Technology Beijing Forestry UniversityBeijing100083China

This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)*** to the presence of an eavesdropper(Eve),the system’s com-munication links may be *** paper proposes deploying an intelligent reflecting surface(IRS)on the UAV to enhance the communication performance of mobile vehicles,improve system flexibility,and alleviate eavesdropping on communication *** links for uploading task data from vehicles to a base station(BS)are protected by IRS-assisted physical layer security(PLS).Upon receiving task data,the computing resources provided by the edge computing servers(MEC)are allocated to vehicles for task *** blockchain-based computation offloading schemes typically focus on improving network performance,such as minimizing energy consumption or latency,while neglecting the Gas fees for computation offloading and the costs required for MEC computation,leading to an imbalance between service fees and resource *** paper uses a utility-oriented computation offloading scheme to balance costs and *** paper proposes alternating phase optimization and power optimization to optimize the energy consumption,latency,and communication secrecy rate,thereby maximizing the weighted total utility of the *** results demonstrate a notable enhancement in the weighted total system utility and resource utilization,thereby corroborating the viability of our approach for practical applications.

关键词： UAV intelligent reflecting surface vehicle to everything task offloading phase shift optimization

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OPTIMIZATION RESEARCH OF IMPROVED GENERALIZED NORMAL DISTRIBUTION OPTIMIZATION ALGORITHM IN WSN COVERAGE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2023年第2期85卷 95-110页

作者： Hu, Biling Hou, Mengshu Xie, Fei Liu, Yanan School of Computer science Hefei Normal University China School of Computer Science and Engineering University of Electronic Science and Technology of China China

A coverage control strategy based on an improved generalized normal distribution optimization algorithm is proposed for coverage optimization of sensor networks. Firstly, IGNDO uses a combination of Logistic and Tent chaotic mappings to initialize the original population in the initialization phase;secondly, it uses nonlinear control parameters to adjust the probability of local search and global search being selected in the iterative phase, and perturbs the update of individuals after iterative update with partial Lévy flight to Improve their ability to escape from their local best;Finally, the algorithm’s search ability is further improved by using random regression transgression. In the experimental phase, the improved algorithm is firstly tested with benchmark functions, and secondly IGNDO is employed to sensor network coverage optimization, and the optimal results are obtained in comparison with other optimization algorithms. © 2023, Politechnica university of Bucharest. All rights reserved.

关键词： Wireless sensor networks

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A study of stochastic evolutionary game prediction models based on SVM and random forests

A study of stochastic evolutionary game prediction models ba...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Jiang, Jinhao Li, Yuqian Zhu, Bolin Hefei University of Technology School of Physics Hefei China Hefei University of Technology School of Instrument Science and Opto-Electronics Engineering Hefei China Hefei University of Technology School of Computer and Information Technology Hefei China

ISBN: (纸本)9798331527624

This paper focuses on the application of four algorithms in data prediction. In the paper, the paper firstly establishes a SVM model and random forest prediction model to judge the performance effect of the model. By calculating the prediction accuracy of the models and observing the ROC curves and confusion matrix, it was found that the two models had the shortcomings of low sensitivity and biased precision, respectively. To address this problem, this paper innovatively complements the advantages and disadvantages of the two models through game theory, and establishes a new model based on the SVM model and the stochastic model. A stochastic evolutionary game model based on the SVM model and the random forest model is established, and the accuracy reaches 99.70%, with MSE=0.0027 and RMSE=0.0522. At the same time, this paper also tries to use the integrated classifier XGBoost, which has a better performance, and the prediction accuracy reaches 99.9%. © 2024 IEEE.

关键词： Prediction models

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Secure Transmission Scheme for Blocks in Blockchain-Based Unmanned Aerial Vehicle Communication Systems

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computers, Materials & Continua 2024年第11期81卷 2195-2217页

作者： Ting Chen Shuna Jiang Xin Fan Jianchuan Xia Xiujuan Zhang Chuanwen Luo Yi Hong School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer Science Qufu Normal UniversityRizhao276826China School of Information Science and Technology Beijing Forestry UniversityBeijing100083China

In blockchain-based unmanned aerial vehicle(UAV)communication systems,the length of a block affects the performance of the *** transmission performance of blocks in the form of finite character segments is also affected by the block ***,it is crucial to balance the transmission performance and blockchain performance of blockchain communication systems,especially in wireless environments involving *** paper investigates a secure transmission scheme for blocks in blockchain-based UAV communication systems to prevent the information contained in blocks from being completely eavesdropped during *** our scheme,using a friendly jamming UAV to emit jamming signals diminishes the quality of the eavesdropping channel,thus enhancing the communication security performance of the source *** the constraints of maneuverability and transmission power of the UAV,the joint design of UAV trajectories,transmission power,and block length are proposed to maximize the average minimum secrecy rate(AMSR).Since the optimization problem is non-convex and difficult to solve directly,we first decompose the optimization problem into subproblems of trajectory optimization,transmission power optimization,and block length ***,based on firstorder approximation techniques,these subproblems are reformulated as convex optimization ***,we utilize an alternating iteration algorithm based on the successive convex approximation(SCA)technique to solve these subproblems *** simulation results demonstrate that our proposed scheme can achieve secure transmission for blocks while maintaining the performance of the blockchain.

关键词： Unmanned aerial vehicles blockchain finite blocklength block transmission alternating optimization

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Lane Environment Detection with only On-Board Camera 3

Lane Environment Detection with only On-Board Camera

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3rd International Conference on Electronic information engineering and computer Communication, EIECC 2023

作者： Li, Chen Liu, Zhengqiong Ding, Zhizhong School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350359961

Lane environment detection is a vital prerequisite for realizing autonomous driving in complex scenarios. However, existing methods face significant challenges, primarily containing the absence of lane-level positioning information, the imbalance between accuracy and processing speed, and the prohibitively high cost of equipment. To address these challenges, this paper introduces a hybrid method that combines deep learning and traditional image processing techniques. The proposed approach uses the YOLO (You Only Look Once) for environmental vehicle detection, augmented by a post-processing module that provides detailed lane-level positioning information. Notably, the post-processing stage integrates deep learning and traditional computer vision methods, ensuring a fast detection speed while maintaining a desirable detection accuracy. An additional advantage of the proposed method is its reliance solely on an on-board camera, effectively lowering the equipment cost. Furthermore, the method provides more comprehensive detection by incorporating geometric method to estimate the distance between the ego-vehicle and surrounding vehicles. The proposed approach addresses the aforementioned challenges and provides a comprehensive solution for effective and low-cost lane environment detection. To be specific, our proposed method runs at 58 fps and achieves accuracy at 95.22%. © 2023 IEEE.

关键词： Deep Learning Image Processing Lane Environment Detection Lane-level Positioning Vehicle Detection

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Traffic Matrix Estimation based on Denoising Diffusion Probabilistic Model 28

Traffic Matrix Estimation based on Denoising Diffusion Proba...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Yuan, Xinyu Qiao, Yan Zhao, Pei Hu, Rongyao Zhang, Benchu School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350300482

The traffic matrix estimation (TME) problem has been widely researched for decades of years. Recent progresses in deep generative models offer new opportunities to tackle TME problems in a more advanced way. In this paper, we leverage the powerful ability of denoising diffusion probabilistic models (DDPMs) on distribution learning, and for the first time adopt DDPM to address the TME problem. To ensure a good performance of DDPM on learning the distributions of TMs, we design a preprocessing module to reduce the dimensions of TMs while keeping the data variety of each OD flow. To improve the estimation accuracy, we parameterize the noise factors in DDPM and transform the TME problem into a gradient-descent optimization problem. Finally, we compared our method with the state-of-the-art TME methods using two real-world TM datasets, the experimental results strongly demonstrate the superiority of our method on both TM synthesis and TM estimation. © 2023 IEEE.

关键词： Diffusion

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Enhanced Implicit Collaborative Knowledge Graph for Recommendation 4

Enhanced Implicit Collaborative Knowledge Graph for Recommen...

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4th International Conference on Intelligent Computing and Human-computer Interaction, ICHCI 2023

作者： Guo, Hao Sheng, Yicheng School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350343113

Knowledge-aware Recommendation (KGR) with Graph Neural Network (GNN) aggregates information on the knowledge graph to capture high-order knowledge representations for the recommendation task, which becomes the mainstream approaches in the field of KGR. While these studies have achieved great success, we emphasize that strict supervision signals of training can have negative impacts on the model. Specifically, the supervision signals force the model to align constantly with sparse positive interaction data, which can result in the loss of valuable knowledge semantics. Moreover, it may also weaken model's ability to explore the potential interests for user. To address the aforementioned issues, we propose a new model named Enhanced Implicit Collaborative Knowledge Graph for Recommendation (EICKR), which uses a multi-task learning schema to extract user interests. This model calculates implicit relations among users, items, and entities to structure rich implicit features across different views. Subsequently, it leverages the knowledge graph and users' historical interaction data to generate an implicit collaborative knowledge graph, bridging the semantic difference between the knowledge graph and interaction data. Furthermore, a graph-enhanced completion task based on implicit relations is introduced to explore unobserved yet valuable interaction features among users, items, and entities, which can be complementary to the recommendation task. We conduct extensive experiments on benchmark datasets, which demonstrates the effectiveness of our approach and its components. © 2023 IEEE.

关键词： Collaborative filtering

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A Few-shot Classification Method with Coordinate Attention and Cross Attention 6

A Few-shot Classification Method with Coordinate Attention a...

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Shu, Kai Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9781665491259

Deep learning tries to learn embedding functions from a large amount of data. A lot of work shows that deep learning has surpassed humans in areas such as image classification and image segmentation. Few-shot learning focuses on learning from unknown classes with limited labels, and the data scarcity of few-shot learning is destined to be a very challenging task. Our work studies the role of the attention mechanism in few-shot learning. In the pre-training process of the two-stage few-shot learning, this study introduces the coordinate attention mechanism to improve the ability of the model to focus on key areas. In the subsequent meta-training process, this study applies the cross-attention module to the support set embedding and the query set embedding, which generates cross-attention maps for each pair of class features and query sample features, alleviating the problem that the Transformer focuses more on the support set features during the meta-training process. Our work has significantly improved the accuracy rate based on the original works and is close to the existing state-of-the-art performance on the general few-shot classification dataset miniImageNet and tieredImageNet. © 2023 IEEE.

关键词： Image segmentation

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A Non-Euclidean Causal Discovery Algorithm in Metric Spaces 4

A Non-Euclidean Causal Discovery Algorithm in Metric Spaces

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4th International Conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Kuai, Fan Yang, Jing Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350373233

Causal information is implicit in the spatial characteristics of non-Euclidean data, and recently some researchers have proposed the Non-Euclidean Causal Model (NECM) to describe causal relationships in non-Euclidean data generation. In this paper, we first introduce distance covariance as the criterion for independence testing in the NECM and subsequently define strong or weak correlations between tensor variables containing potential causal relationships. Secondly, we propose a novel causal structure learning algorithm called Distance Covariance-based Non-Euclidean Hybrid Learning (D-NEHL), which can learn causal networks for non-Euclidean data in metric spaces. Finally, we conduct experiments on both simulated data and real stock market data. The results demonstrate that the D-NEHL algorithm exhibits higher accuracy and time performance compared to existing non-Euclidean causal learning methods. It is applicable to various types of non-Euclidean datasets and performs well on traditional Euclidean datasets. © 2023 IEEE.

关键词： Topology

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