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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Liu, Pang Liao, Zhuofan Tang, Xiaoyong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China

ISBN: (纸本)9798331509712

Edge caching is a promising technique for effectively reducing backhaul pressure and content access latency in the Internet of Vehicles (IoV). However, the high mobility of vehicles and dynamic user requests often lead to outdated cached content. Expired cache wastes the limited storage space and transmission in vehicular edge computing. Improving the cache hit rate is an effective approach to address these issues. In this work, we propose a Cooperative Active Caching Strategy (CACS) which works as follows. First, user preferences are analyzed from the historical content data of vehicle users. After that, multiple vehicles cooperate to learn the global model under an asynchronous federated learning framework, and get the content popularity from user preferences and content features. To explore the problem of local models being outdated in asynchronous federated learning, CACS integrates model compression algorithms, enhancing system efficiency and prediction accuracy. Finally, a heuristic cooperative caching content placement algorithm is proposed based on a greedy policy to minimize average access latency. Simulation results show that the CACS can improve the cache hit rate by 15% at most compared to existing state-of-the-art caching strategies. © 2024 IEEE.

关键词： Federated learning

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A Task Dependency-based Deduplicated Task Offloading Mechanism in Vehicular Edge Computing 22

A Task Dependency-based Deduplicated Task Offloading Mechani...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Shao, Zhenyi Liao, Zhuofan Tang, Xiaoyong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China

ISBN: (纸本)9798331509712

The increasing demand for in-vehicle applications has raised the complexity and computational load, while the in-vehicle tasks exhibit a sensitivity to latency. Previous research has proposed utilizing the idle computational resources of roadside vehicles to alleviate this contradiction. However, the high mobility of vehicles leads to communication interruptions, and the time-varying nature of vehicle density makes resource allocation challenging. In this work, we leverage the dependencies between vehicular computing tasks and design a deduplication offloading mechanism for stable reduction of latency. This mechanism consists of two stages, named the Multi-hop Clustering Deduplication Offloading (MCDO) mechanism. Firstly, a Multi-hop Two Layer Clustering (MTLC) algorithm is designed to divide vehicles based on task dependencies, speed, and position information. Then, a Deduplication Layered Offloading (DLO) algorithm is proposed to identify and remove duplicated tasks within each cluster while maintaining their inter-dependencies. Simulation results demonstrate that MCDO effectively divides vehicle clusters and offloads tasks efficiently under various road conditions. Compared to existing approaches, MCDO significantly enhances system performance, achieving a minimum improvement of 15.1% in terms of latency. © 2024 IEEE.

关键词： Computation offloading

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Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

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computers, Materials & Continua 2024年第3期78卷 3909-3927页

作者： Yilei Wang Xin Sun Guiping Zheng Ahmar Rashid Sami Ullah Hisham Alasmary Muhammad Waqas Zhejiang Electric-Power Corporation Research Institute Zhejiang310014China Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Trusty Cloud Technology Co. Ltd.Beijing100022China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and EngineeringTopi23640Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir18050Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent *** sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and ***,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion ***,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart *** response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these *** scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT *** then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy *** comprehensive approach reduces the impact of subjective factors on trust ***,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious ***,in turn,enhances the security and reliability of the smart grid *** effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation ***,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

关键词： IIoT trusted measure energy efficient

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FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient

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Chinese Physics B 2022年第2期31卷 109-118页

作者： Fei Yu Zinan Zhang Hui Shen Yuanyuan Huang Shuo Cai Sichun Du School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha 410114China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China

A memristive Hopfield neural network(MHNN)with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network(HNN)with a special activation *** MHNN is simulated and dynamically analyzed,and implemented on ***,a new pseudo-random number generator(PRNG)based on MHNN is *** post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator,which effectively ensures the randomness of *** experiments in this paper comply with the IEEE 754-1985 high precision32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7 Z020 CLG400-2 FPGA chip and the Verilog-HDL hardware programming *** random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis,proving its randomness and high ***,an image encryption system based on PRNG is proposed and implemented on FPGA,which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things(Io T).

关键词： memristive Hopfield neural network(MHNN) pseudo-random number generator(PRNG) FPGA image encryption decryption system

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A Task Admission Scheduling Strategy Based on Adaptive Time Slicing in Mobile Edge Computing 22

A Task Admission Scheduling Strategy Based on Adaptive Time ...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Tang, Yanpu Liao, Zhuofan Tang, Xiaoyong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China

ISBN: (纸本)9798331509712

The rise of Multi-access Edge Computing (MEC) speeds up mobile user services and resolves service delays caused by long-distance transmission to cloud servers. However, in task-intensive scenarios, edge server processing limitations lead to buffer congestion, increasing latency and reducing Quality of Service (QoS). Furthermore, the challenges of edge server task processing are increased by the varying deadline requirements of different tasks, the time variability of task arrivals, and the real-time fluctuations of the network. In this paper, we propose an Adaptive Time Slice Admission Scheduling (ATSAS) strategy to solve these problems. Specifically, ATSAS proposes an Adaptive Time Slice Algorithm (ATSA) and a task Admission Scheduling Algorithm (ASA). ATSA dynamically adjusts the time slice based on real-time network conditions and task flow characteristics. ASA, assisted by ATSA, dynamically adjusts task priorities based on the remaining data amount, deadline requirements, real-time network conditions, and the location of the task buffer. After that, ASA makes different scheduling strategies for tasks. Through the coordination of ATSA and ASA, the proposed strategy ensures the high efficiency and fairness of processing tasks in different realistic scenarios. The simulation results show that, compared with the existing O2A and OTDS in different scenarios, the proposed ATSAS reduces the average number of waiting services in the edge server buffer by 38.63% and 35.34%, and accelerates the average response speed of tasks by about 39.76% and 32.41%. © 2024 IEEE.

关键词： Mobile edge computing

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A SFC Placement Strategy in Low-Visibility Multi-Domains Networks for Low Resource Usage Cost 22

A SFC Placement Strategy in Low-Visibility Multi-Domains Net...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Xiong, Qingli Liao, Zhuofan Tang, Xiaoyong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China

ISBN: (纸本)9798331509712

The concept of Network Function Virtualization (NFV) enables the realization of services as a Service Function Chain (SFC) that is composed of multiple Virtual Network Functions (VNFs), allowing for flexible deployment across the network. For wireless networks comprised of diverse domains which means be managed by different network providers, each domain has different resource and transmission costs. Due to commercial confidentiality reasons, the internal information of these domains is not interconnected, which increases the difficulty of SFC deployment. How to deploy SFCs in multi-domains networks at the lowest cost is becoming a challenge. This paper proposes a novel strategy for the scenario that information of sub-domain is invisible. The strategy firstly splits the SFC based on the processing dependency of its VNFs to reduce intermediate data transmission. Then it utilizes a Graph Matching Network (GMN) to evaluate the matching degree between sub-domain and SFC fragment. This matching degree acts as a reference for optimizing SFC allocation, thereby avoiding the direct acquisition of internal sub-domain information. Simulation results demonstrate that the proposed strategy not only significantly reduces resource, but also maintains a higher request acceptance ratio. © 2024 IEEE.

关键词： Network function virtualization

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Pricing-Based Task Offloading Considering User Energy Consumption in MEC System 22

Pricing-Based Task Offloading Considering User Energy Consum...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Han, Xiyu Liao, Zhuofan Tang, Xiaoyong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China

ISBN: (纸本)9798331509712

Multi-access Edge Computing (MEC) can find its wide applications in various resource-constrained scenario where the user needs to pay a price to the server for meeting the latency requirements of their own tasks. However, to the best of our knowledge, there is no effective pricing model in MEC taking into account the impact of the user's own energy consumption on the server's price scheme. In fact, the transmission energy consumption affects the users' offloading task decisions, which in turn affects the revenue of the edge servers. To fill in this gap, a novel pricing mechanism for energy-sensitive users is proposed in this paper, specifically: 1) We propose a joint optimization problem of computation offloading decisions and resource pricing that considers user energy consumption. 2) By constructing a two-stage Stackelberg game model, the first stage simplifies the problem by constraint relaxation theory and finds an approximate optimal offloading strategy through the Fast Genetic Algorithm (FGA). The second stage uses the gradient descent method to find the potential optimal prices, thereby achieving a balance between the lowest user cost and the highest server profit. Experimental results show that, compared to traditional algorithms, our approach optimizes the average user cost and user task completion time by 24.65% and 12.37%, respectively. © 2024 IEEE.

关键词： Computational cost

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Less Labeling Burden: A SAR Ship Detection Data Selection Method Based on K-means 2

Less Labeling Burden: A SAR Ship Detection Data Selection Me...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Yang, Yimin Yin, Junjun Lang, Ping Yang, Jian He, Yaomin Tsinghua University Department of Electronic Engineering Beijing China University of Science and Technology School of Computer and Communication Engineering Beijing China Academy of Military Sciences Institute of Systems Engineering Beijing China

ISBN: (纸本)9798331515669

In deep learning-based object detection, labeled data is crucial. However, annotating ship targets in Synthetic Aperture Radar (SAR) images is challenging because of the distinct characteristics of SAR images, especially when employing Oriented Bounding Boxes (OBB). These challenges arise from factors such as varying ship orientations, complex backgrounds, and the presence of noise, making accurate annotation more difficult. To reduce the labeling burden while minimizing its impact on ship detection performance, we propose a SAR data selection method. This method ensures that, despite limited quantities, the data contains a more representative range of scenarios when constructing a new dataset. By selecting 10 features of SAR images and performing K-means clustering based on these features, we choose the samples closest to the cluster centers as the selected data. We have validated the effectiveness of the proposed method by applying two typical object detection models to both randomly selected and clustering-selected datasets, thereby demonstrating the efficacy of proposed approach. © 2024 IEEE.

关键词： Radar imaging

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A Comprehensive Review on Deep Learning System Testing 24th

A Comprehensive Review on Deep Learning System Testing

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Ying Shan, Chun Liu, Zhen Liao, Shuyan Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789819615476

Deep learning(DL) systems exhibit multiple behavioral characteristics such as correctness, robustness, and fairness. Ensuring that these behavioral characteristics function properly is crucial for maintaining the accuracy of DL systems’ outputs. As a specialized form of software, DL systems’ security testing techniques have increasingly become a focus of research in quality assurance. We analyze and organize the testing techniques for DL systems based on an investigation of the current state of the art both domestically and internationally. This paper categorizes existing approaches as component-oriented and attribute-oriented methods, providing a detailed review based on this classification. Additionally, we forecast the future development of testing techniques for DL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Open Text Classification Based on Dynamic Boundary Balance 9th

Open Text Classification Based on Dynamic Boundary Balance

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Xu, Ganlin Feng, Jianzhou Wei, Qikai School of Information Science and Engineering Yanshan University Qinhuangdao066000 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100000 China

ISBN: (纸本)9783031466700

Open classification is the problem where there exist some unseen/unknown classes in the test set, i.e., these unknown/unseen classes don’t appear when the model is trained. Existing work often maps samples to high-dimensional space to make decisions, which leads to unobservable and inexplicable results. To address the issue, we shift perspectives to two-dimensional space and put forward a two-stage learning method built on the dynamic decision boundaries balance. We refer it to open classification with dynamic boundary balance (OCD2B). First, we construct a vanilla classifier via known classes with BERT model. Then, we use the prior knowledge of known classes to dynamically determine the decision boundaries between known classes and unknown classes in low-dimensional space. We propose a novel boundary loss function as a boundary balance strategy to reduce open space risk and empirical risk. Experimental results on two standard datasets show that our method achieves performance gain over existing methods, providing easily observable results. In particular, the larger the ratio of unseen classes is, the more obvious the performance advantage the model achieves. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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