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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Zhang, Jin Cao, Fan Zhang, Jianming Li, Ying Wu, Sheng Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798350383706

In the field of autonomous driving, it is of great significance for autonomous driving to detect traffic signs accurately. This paper proposes a method named MR_YOLO. This method addresses the issue of inadequate accuracy that plagues current systems. More specifically, to address varying sizes of traffic signs in images, our strategy refined anchor box dimensions using K-Means++ clustering and fine-tuned the model to enhance traffic sign recognition efficacy. Next, a multi-scale feature extraction model, C3Res2Net, is formed by combining the C3 and Res2Net models to address the different scales and sizes of traffic signs. This approach enables the network to accurately capture comprehensive information and intricate details of traffic signs, thus enhancing recognition capabilities. Experimental data reveal competitive outcomes when tested on the CCTSDB dataset. The method achieves values of 92.8% and 88.4% in precision and mAP, respectively. © 2023 IEEE.

关键词： Object detection

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Heterogeneous Device Collaboration Based Federated Learning for Big Data Applications

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IEEE Transactions on Big Data 2024年 1-11页

作者： Wang, Wenhua Yang, Quan Liang, Yuzhu Xu, Yang Liu, Qin Wang, Tian School of Artificial Intelligence Beijing Normal University Beijing China College of Computer Science and Technology Huaqiao University Xiamen China College of Computer Science and Electronic Engineering Hunan University Changsha China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China

In the era of Big Data, artificial intelligence and information science are the key technologies to extract the value of data and enhance the competitiveness of enterprises. The characteristics of distributed, small-scale, and sparse lead to the isolated data island problem. To solve these problems, Federated Learning is proposed. However, a large number of terminal models need to be uploaded to the server in Federated Learning, especially for the actual scenario of Internet of Things. Therefore, huge communication costs are required which dramatically increases the pressure on the backbone network. Furthermore, the low quality of the local model will lead to decreased accuracy and convergence rates of the model. To overcome the above limitations, we propose heterogeneous device collaboration based federated learning (HDCFL), which constructs a three-layer structure for Federated Learning by leveraging edge computing and designs a heterogeneous device collaboration method that groups the terminals based on their computing power, communication time, and data volume to train the model. Then, we conduct a theoretical analysis of the proposed algorithm which verifies its advantage. At last, the experimental result demonstrates that the proposed algorithm consistently achieves superior performance in terms of both convergence speed and accuracy compared with state-of-the-art baselines. IEEE

关键词： Competition

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Stealthy False Data Injection Attack Using Improved Singular Value Decomposition in Smart Grid 7

Stealthy False Data Injection Attack Using Improved Singular...

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7th International Conference on Information and computer Technologies, ICICT 2024

作者： Yang, Liqun Li, Chunan Wei, Chaoren Yang, Jian Qu, Gang Zhang, Liang Zhang, Yipeng Li, Tongliang Beihang University School Of Cyber Science And Technology Beijing China Beihang University School Of Computer Science And Engineering Beijing China State Grid Corporation Of China East China Branch Shanghai China North China University Of Technology School Of Information Science And Technology Beijing China Beijing Information Science And Technology University Computer School Beijing China

ISBN: (纸本)9798350385625

Smart grid is a crucial Cyber-Physical system and is prone to cyber-attacks. In this paper, we propose a novel false data injection attack (FDIA) construction mechanism. Firstly, the measurements are preprocessed to eliminate the outliers caused by the system failure, communication failure and data missing. Then, the preprocessed measurements are decomposed into a multiplication of the unitary matrixes by the proposed improved Singular Value Decomposition (SVD) algorithm. To achieve more accurate decomposed matrix, we introduce the regularization constraints into the objective of SVD. At last, the FDIA is constructed based on the decomposed matrix in a sparse way. We assess the performance of the proposed FDIA with comprehensive simulations on IEEE 14- and 118-bus systems. The results indicate that the proposed FDIA can be performed in a stealthy and sparse way. © 2024 IEEE.

关键词： State estimation

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KSKV:Key-Strategy for Key-Value Data Collection with Local Differential Privacy

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computer Modeling in engineering & sciences 2024年第6期139卷 3063-3083页

作者： Dan Zhao Yang You Chuanwen Luo Ting Chen Yang Liu Artificial Intelligence Development Research Center Institute of Scientific and Technical Information of ChinaBeijing100038China Industry Development Department NSFOCUS Inc.BeijingChina School of Information Science and Technology Beijing Forestry UniversityBeijing100083China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Institute of Computing Technology China Academy of Railway Sciences Corporation LimitedBeijing10081China

In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph ***,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data *** studies would simultaneously collect the frequencies of keys and the mean of values associated with each ***,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility *** the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under ***,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration *** strategies are adapted for scenarios in which users possess either a single or multiple key-value ***,we demonstrate that the variance of KS-UE is lower than that of existing *** claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.

关键词： Key-value local differential privacy frequency estimation mean estimation data perturbation

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Joint Uplink and Downlink Robust Transmission for Cell-Free Networks

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IEEE Transactions on Wireless communications 2024年第10期23卷 15308-15321页

作者： Zhang, Guangyang Lu, Yang Li, Mingchun Zhong, Zhangdui Quek, Tony Q. S. Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronic and Information Engineering Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing100044 China Singapore University of Technology and Design Tampines487372 Singapore Yonsei University Seoul03722 Korea Republic of

This paper investigates the joint uplink (UL) and downlink (DL) robust transmission design for cell-free networks. The total data amount of the UL and DL transmissions is maximized in the presence of the statistical channel state information error by optimizing the UL digital combiners, UL transmission power, DL transmit beamforming vectors and UL/DL time allocation subject to the tolerable UL/DL outage probability constraints, the UL/DL minimum data amount requirements and the UL/DL power budgets. To tackle the variables coupling in outage probability constraints, the property of perspective function and the quadratic transform are applied. Then, the Bernstein-type inequality is used to derive the computationally tractable forms of the outage probability constraints. The considered problem is further decomposed into four subproblems and solved by the proposed alternating optimization (AO)-based algorithm. Numerical results show the proposed algorithm outperforms three existing AO-based baselines in terms of convergence speed and optimality performance. The impacts of the UL transmission power and tolerable outage probability on the UL/DL transmission and time allocation are revealed. Moreover, the effective APs for each user are defined and illustrated to show the coordination among the APs. © 2024 IEEE.

关键词： Channel state information

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Reinforcement Learning Based Edge Microservice Migration Strategy under Resource Constraints 5

Reinforcement Learning Based Edge Microservice Migration Str...

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5th International science and technology Conference Modern Network Technologies, MoNeTec 2024

作者： Dong, Mengxue Huo, Dongkun Zhang, Hong Zhang, Yan Hao, Yixue Hu, Long Chen, Min Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China South China University of Technology School of Computer Science & Engineering Guangzhou China National University of Defense Technology College of Information and Communication Changsha China

ISBN: (纸本)9798350369007

Service migration can solve the issue of frequent user switching caused by the restricted coverage of edge cloud, which is currently a major focus of research. Simultaneously, microservice architecture gains traction in businesses for its benefits in maintainability and scalability. Existing migration strategies mainly target monolithic services, overlooking the complexities of microservice migration scenarios. Considering the limited edge cloud resources and the flexible combination of microservices to be migrated individually in the microservice migration process, we construct a microservice migration system architecture to solve this problem with the goal of minimizing latency and energy consumption. We transform the microservice migration problem to a Markov decision process and propose an intelligent microservice migration algorithm under resource constraints based on reinforcement learning algorithm. Simulation results indicate that our algorithm reduces user request latency and system energy consumption, while enhancing QoS compared to current approaches. © 2024 IEEE.

关键词： Adversarial machine learning

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High-quality Trajectory Generation for Autonomous Driving: A Lightweight Federated Learning-based Diffusion Model

High-quality Trajectory Generation for Autonomous Driving: A...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Gao, Runquan Kang, Jiawen Lai, Bingkun Xu, Minrui Sun, Geng Zhang, Tao Zhang, Weiting Yang, Dong Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Jilin University College of Computer Science and Technology Changchun China Beijing Jiaotong University School of Software Engineering Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China

ISBN: (纸本)9798350351255

Vehicle trajectory data plays a pivotal role in simulation testing for autonomous driving. Hence, there exist well-established trajectory generation methods employing deep generative models to generate trajectories mapping the distribution of the original dataset, thereby augmenting existing trajectory datasets. However, these methods typically rely on large datasets gathered by governmental or organizational entities for central training, which may pose data privacy, security, and accessibility issues. Therefore, it is challenging to generate high-quality traffic trajectory data while preserving privacy which involves a delicate balance between these two objectives. To deal with this challenge, we introduce Federated Learning into the diffusion model and propose a Federated Learning-based diffusion model (FedDifftraj) to generate traffic trajectory data. Unlike existing central training methods, FedDifftraj aggregates model parameters uploaded by different vehicles and then updates a global model. Additionally, there is a substantial communication overhead incurred during the training of the federated diffusion model. Therefore, we quantize the local diffusion model before uploading it to the parameter server. Through extensive simulations on real-world datasets, FedDifftraj can generate high-quality traffic trajectory data that is consistent with the results of the central training while preserving privacy and reducing communication overhead by 93.74% when utilizing 8-bit quantization. © 2024 IEEE.

关键词： Differential privacy

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Privacy-Preserving Arbitrary Hollow Polygon Geometric Range Query Scheme in Fog Computing-Based IoVs 9

Privacy-Preserving Arbitrary Hollow Polygon Geometric Range ...

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Chen, Dongdong Jiang, Luyao Wang, Yi Gu, Ke Changsha University of Science & Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798350383706

Geometric range query is an essential operation in many applications, including location-based service, querying on sensor networks, and computational geometry. In the Internet of Vehicles (IoVs) environment, the range query operations allow for retrieving vehicles or events within a specific spatial range, enabling applications such as traffic monitoring, incident detection, and task outsourcing. However, with the issues of latency, location and query range privacy, and geometric query range limits, the application of range query in IoVs still faces a number of serious challenges to its prosperity. In this paper, we design arbitrary hollow polygon preprocessing and points in polygon judgment strategies that support arbitrary hollow polygon range query. Furthermore, by integrating fog computing into IoVs, the low latency of the overall process is met. Finally, the theoretical analysis and experimental results show that the proposed scheme is secure and efficient. © 2023 IEEE.

关键词： Fog computing

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Adjusting Exploitation and Exploration Rates of Differential Evolution: A Novel Mutation Strategy 20th

Adjusting Exploitation and Exploration Rates of Differenti...

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20th International Forum on Digital TV and Wireless Multimedia communications, IFTC 2023

作者： Duan, Danting Zhang, Yuhui Zhong, Wei Ye, Long Zhang, Qin Ministry of Education Beijing China School of Computer Science and Technology Dongguan University of Technology Dongguan China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China

ISBN: (纸本)9789819736256

Differential evolution (DE) has attracted significant attention in recent years owing to its high performance in solving continuous problems. Up to now, a large number of variants of DE mutation strategy have been proposed and extensively studied. However, these mutation strategies rarely concentrate on adjusting the exploitation and exploration rates, which can make a great impact on DE’s performance. In this paper, a novel mutation strategy capable of visibly adjusting the exploration and exploitation rates is developed for DE algorithms. In the proposed mutation strategy, the difference vector is calculated according to the distance between selected individuals and the gravity center of these individuals. Then, the donor vector is generated by adding this difference vector to the base vector based on an asymmetrical normal distribution that determines the probability of the mutate direction to be inward for exploitation or outward for exploration. Experiments have been carried out on 13 classical benchmark functions and the CEC2013 test suite to study the proposed mutation strategy. The experimental results show that the exploitation and exploration rates have a considerable effect on both the algorithm’s convergence rate and the solution accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Normal distribution

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of computer science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalability,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalability brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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