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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Wenhong Ma Xiangzhen Xiao Guoqi Xie Nan Guan Yu Jiang Wanli Chang Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University City University of Hong Kong Tsinghua University China

ISBN: (纸本)9798350323481

As an Ethernet-based communication protocol with timing determinism, TSN (time-sensitive networking) has become a well-recognized promising in-vehicle network solution for increasingly automated automobiles. To satisfy the reliability requirement of safety-critical applications, existing works towards fault-tolerant TSN trade too much bandwidth for redundancy, limiting their scope of applicability. Targeting mixed-critical traffic, which is widely found in practice, we define a shared fault-tolerant segment that is compatible with the TSN standard. It serves the critical flows when faults occur, to improve their reliability and serves the non-critical flows when otherwise, to improve their quality of service (QoS). On top of this, we propose a space-time redundancy scheduling algorithm, aiming to make the most efficient use of bandwidth, i.e., to fulfill both the reliability as well as hard real-time requirements, and maximize the QoS with the least bandwidth. In essence, we formulate a bi-objective design space exploration problem with hundreds of thousands of decision variables and solve it with customized heuristics. Experimental results show that compared to the state-of-the-art methods, our reported work increases the number of critical flows that can be accommodated on a resource-constrained network by 3 to 4 times, and achieve the highest QoS with an average reduction of 60.3% in bandwidth. As the first work along sharing of bandwidth between mixed-critical traffic in fault-tolerant TSN, this idea can be further pursued towards higher efficiency and may be applied in general autonomous systems.

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Holistic WCRT Analysis for Global Fixed-Priority Preemptive Multiprocessor Scheduling

Holistic WCRT Analysis for Global Fixed-Priority Preemptive ...

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Design Automation Conference

作者： Guoqi Xie Chenglai Xiong Wei Wu Renfa Li Wanli Chang Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University China

Many embedded applications demand both resource efficiency and timing guarantee. However, resource sharing naturally complicates the analysis that extracts the worst-case scenario out of contention. Global Fixed-Priority (GFP) preemptive multiprocessor scheduling is one of the mainstream strategies to resolve contention on computational resources. It allows jobs of the same task to be executed on different processors, hence potentially enabling better parallelism and more efficient resource utilization. Unfortunately, its worst-case response time (WCRT) analysis is challenging. Existing approaches divide a high-priority task into three workloads, namely, carry-in workload, body workload, and carry-out workload, trying to optimize them individually. In this work, we propose a holistic WCRT analysis for GFP preemptive multiprocessor scheduling, where a task is no longer divided. Specifically, (i) we establish the tight interference scenario for the task being analyzed to find the most interfering high-priority jobs in any time interval; (ii) we obtain the starting released instant of each high-priority task’s first job to determine the maximum interference from high-priority tasks’ first jobs to the task being analyzed; (iii) we build the worst-case tight interference scenario for the task being analyzed by combining the tight interference scenario and the starting released instants; (iv) we prove that the WCRT of the task being analyzed can be decided by the worst-case tight interference scenario. Evaluation on schedulability shows that our proposed analysis achieves 4.2%-8.6% higher acceptance ratio in randomly generated data sets than the state-of-the-art workload division approaches.

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Brief Industry Paper: Response Time Evaluation of Cross-Domain Communication in CAN-FD and TSN

Brief Industry Paper: Response Time Evaluation of Cross-Doma...

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Real-Time Systems Symposium (RTSS)

作者： Wenhong Ma Xiaoyi Huang Dongsheng Wei Renfa Li Yunfei Zhang Guoqi Xie Wanli Chang Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University China Tencent Future Network Lab Tencent Corporation China

With the advancement of intelligence and networked automotive, the domain-centralized architecture, which employs time sensitive networking (TSN) as the inter-domain backbone network and control area network with flexible data rate (CAN-FD) as the intra-domain network, has garnered significant attention. However, cross-domain end-to-end communication involves multiple components, and significant disparities between TSN and CAN-FD render response time analysis within domain-centralized architecture for mixed-critical traffic exceptionally complex. In this paper, we develop a cross-domain with TSN and CAN-FD end-to-end response time evaluation tool, which analyzes the response time of mixed-critical traffic under different design options segment by segment. We specifically analyze the waiting times of different messages in the domain control unit when faced with the design options of one-to-one and multi-to-one conversion of CAN-FD and TSN frames. The proposed evaluation tool can be easily extended to different design options to support more application scenarios. Theoretical computational analysis and real hardware measurements show the effectiveness of our tool.

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LIDS: A Lightweight Intrusion Detection System for Controller Area network

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Yu, Zhangwei Liu, Yan Li, Renfa Chang, Wanli Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Changsha410082 China Research Institute of Hunan University in Chongqing Research and Innovation Center for Convergence of Automobile and Cyberspace Chongqing401120 China

Controller Area network (CAN) is widely adopted in automobiles and susceptible to cyber attacks with the development of intelligent connected vehicles. While neural networks have demonstrated high accuracy in detection of such attacks, they consume a large amount of resources, hence unsuitable to be directly used for the automotive domain. In this work, we propose LIDS, a lightweight intrusion detection system for CAN. It first filters out DoS (Denial of Service) and Fuzzy attacks through list screening, following which, a Multi-Layer Perceptron (MLP) model is deployed to predict Impersonation attacks. Leveraging this combination, the detection accuracy is kept and the resources required are significantly reduced. LIDS is able to run on small hardware with 520KB memory and CPU of 240MHz. Its power consumption is one order of magnitude lower than the existing works, thus an excellent candidate for protection of CAN in automobiles. © 1982-2012 IEEE.

关键词： Automobiles

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Location Data Publishing Mechanism in Semi-Trusted Vehicle networks based on Differential Privacy

Location Data Publishing Mechanism in Semi-Trusted Vehicle N...

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2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhao, Zheng Zhang, Tianhao Mao, Qian Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Liaoning University Shenyang China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Vehicular ad-hoc networks (VANETs) are essential in mobile ad-hoc network applications, utilizing vehicle location data. However, safeguarding the privacy and security of this data is challenging, especially with untrusted roadside units. To address this, we propose a novel differential privacy-based location data publishing mechanism. It consists of three key components: dynamic group leader selection, data aggregation, and privacy-preserving data publishing. The group leader selection algorithm is based on directional thresholds, hop cycles, and lifespans, assuming semi-trusted vehicles. Additionally, the differential privacy-based algorithm uses map grid segmentation, privacy budget recovery, and a filtering mechanism to ensure location privacy. Comparative experiments show that our scheme outperforms existing models in total hops, group leader lifetime, and mean absolute error. © 2025 SPIE.

关键词： Differential privacy

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Zram Instance Pool Framework for Adaptive Memory Compression in Resource-Sensitive embedded Operating Systems

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Deng, Yin Xie, Guoqi Xiong, Chenglai Zhao, Sirong Ren, Wei Li, Kenli Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan Changsha410082 China Huawei Technology Co. Ltd. Central Software Institute Guangdong Shenzhen518129 China

Memory compression can reduce the size of the inactive data in the random access memory (RAM), thereby freeing up unused space and allowing more programs to run;however, current mainstream memory compression frameworks (e.g., Zram and Zswap) and algorithms (e.g., Zstd and Lz4) do not effectively solve the problem of increased CPU utilization, causing they cannot be directly applied to the resource-sensitive embedded operating system that is sensitive to both CPU utilization and memory usage. In this study, we develop a Zram instance pool framework called ZramPool for adaptive memory compression. The framework consists of the swap space with multiple Zram instances and the adaptive Zram compression module. Through introducing linear regression analysis, the number of Zram instances can be adaptively adjusted based on the size of the compressed data, allowing Zram instances to work in parallel to match the workload. In ZramPool, we achieve two different requirements of reducing CPU utilization while keeping compression speed and increasing compression speed while keeping CPU utilization. ZramPool is deployed in the embedded Linux OS with a 8GB memory size running on the ARMv8 architecture. For the first requirement, ZramPool can reduce CPU utilization by an average of 11.42% while the compression speed only decreases by an average of 2.4%. For the second requirement, ZramPool can increase compression speed by an average of 11.71% while the CPU utilization only increases by an average of 1.9%. © 1982-2012 IEEE.

关键词： Random access storage

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CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System

arXiv

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arXiv 2024年

作者： Guan, Qinghua Ouyang, Jinhui Wu, Di Yu, Weiren Zhu, Yi Yuan, Xiang The Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Hunan Changsha410082 China The Department of Computer Science University of Warwick CoventryCV4 7AL United Kingdom

The spatiotemporal data generated by massive sensors in the Internet of Things (IoT) is extremely dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (e.g. accuracy, reliability, and stability) in real-time analysis and decision making for different IoT applications. The complexity of IoT data prevents the common people from gaining a deeper understanding of it. Agentized systems help address the lack of data insight for the common people. We propose a generic framework, namely CityGPT, to facilitate the learning and analysis of IoT time series with an end-to-end paradigm. CityGPT employs three agents to accomplish the spatiotemporal analysis of IoT data. The requirement agent facilitates user inputs based on natural language. Then, the analysis tasks are decomposed into temporal and spatial analysis processes, completed by corresponding data analysis agents (temporal and spatial agents). Finally, the spatiotemporal fusion agent visualizes the system’s analysis results by receiving analysis results from data analysis agents and invoking sub-visualization agents, and can provide corresponding textual descriptions based on user demands. To increase the insight for common people using our framework, we have agnentized the framework, facilitated by a large language model (LLM), to increase the data comprehensibility. Our evaluation results on real-world data with different time dependencies show that the CityGPT framework can guarantee robust performance in IoT computing. Copyright © 2024, The Authors. All rights reserved.

关键词： Internet of things

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A recent survey on controllable text generation: A causal perspective

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Fundamental Research 2024年第3期5卷 1194-1203页

作者： Wang, Junli Zhang, Chenyang Zhang, Dongyu Tong, Haibo Yan, Chungang Jiang, Changjun Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai 201804 China National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai 201804 China

As an important subject of natural language generation, Controllable Text Generation (CTG) focuses on integrating additional constraints and controls while generating texts and has attracted a lot of attention. Existing controllable text generation approaches mainly capture the statistical association implied within training texts, but generated texts lack causality consideration. This paper intends to review recent CTG approaches from a causal perspective. Firstly, according to previous research on basic types of CTG models, it is discovered that their essence is to obtain the association, and then four kinds of challenges caused by absence of causality are introduced. Next, this paper reviews the improvements to address these challenges from four aspects, namely representation disentanglement, causal inference, knowledge enhancement and multi-aspect CTG respectively. Additionally, this paper inspects existing evaluations of CTG, especially evaluations for causality of CTG. Finally, this review discusses some future research directions for the causality improvement of CTG and makes a conclusion. © 2024

关键词： Causal inference Causality Controllable text generation Knowledge enhancement Representation disentanglement

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Fault Tolerance in Time-Sensitive networking with Mixed-Critical Traffic

Fault Tolerance in Time-Sensitive Networking with Mixed-Crit...

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Design Automation Conference

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Fine-Grained Multivariate Time Series Anomaly Detection in IoT

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Computers, Materials & Continua 2023年第6期75卷 5027-5047页

作者： Shiming He Meng Guo Bo Yang Osama Alfarraj Amr Tolba Pradip Kumar Sharma Xi’ai Yan School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computing Science University of AberdeenAberdeenAB243FXUK Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police AcademyChangsha410138China

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)*** time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and ***,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is ***,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between ***,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)*** avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained *** on this framework,an algorithm based on Graph Attention Neural network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal *** simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

关键词： Multivariate time series graph attention neural network fine-grained anomaly detection

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