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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Hapka, Robin Ernst, Rolf Institute of Computer and Network Engineering Tu Braunschweig Germany

ISBN: (纸本)9798350361230

Safety-critical industrial systems are subject to many stringent requirements, including non-functional design constraints that are essential for robustness, reliability, and safety. System timing is often treated as an afterthought, even though data age, race conditions, or missed deadlines can affect functional safety. This challenge has been addressed in real-time system design, but the situation has worsened in recent system designs. Many applications require high performance to achieve behavioral safety goals, i.e., safety of intended functionality, using complex heterogeneous architectures with multi-level memory, interconnected chiplets, and accelerators based on the latest technologies. Machine learning applications in factory automation, robotics, and autonomous systems are important examples. The timing of such architectures is heavily influenced by the physical effects of temperature control and chip variations, including aging, leading to even more complex chip-specific software execution timing. The paper shows that identical chips differ in their timing behavior, and that most of the timing variation of a single chip can be effectively modeled using additive white Gaussian noise. We explain how such timing behavior can be exploited in high-assurance design for safety and reliability, following established reliability analysis, test, and redundancy techniques. © 2024 IEEE.

关键词： Reliability analysis

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Fast Vehicular TSN network Reconfiguration with Application Aware network Synchronization 29

Fast Vehicular TSN Network Reconfiguration with Application ...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Stöhrmann, Dominik Ernst, Rolf Institute of Computer and Network Engineering Technische Universität Braunschweig Germany

ISBN: (纸本)9798350361230

In-Vehicle networks (IVN) are under intense pressure to be both resource efficient at low cost and flexible enough for the future. The deployment of a large number of new sensors for autonomous driving and self-awareness significantly increases the demand for network resources. In addition, statically designed networks lack flexibility or have large over-provisioning of resources due to the dynamics of network traffic. Application-aware resource management addresses both of these issues. It allows the network to be reconfigured while controlling application requirements, increasing resource utilization, and providing flexibility for future demands. Our timestamp-based reconfiguration protocol ensures fast, lossless reconfiguration of high bandwidth data connections. Based on global time synchronization, it enables precise reconfiguration without increasing the risk of data loss and reduces the risk of inconsistent network configurations by exploiting the object slack created by large object transfers consisting of hundreds of individual packets. Using software-based Linux switches as the network, we demonstrate the use of the reconfiguration protocol under real-world conditions. © 2024 IEEE.

关键词： Resource allocation

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Deploying AI-Based Applications with Serverless Computing in 6G networks: An Experimental Study 100

Deploying AI-Based Applications with Serverless Computing in...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Michalke, Marc Muonagor, Chukwuemeka Jukan, Admela Technische Universität Institute of Computer and Network Engineering Braunschweig Germany

ISBN: (纸本)9798331517786

Future 6G networks are expected to heavily utilize machine learning capabilities in a wide variety of applications with features and benefits for both, the end user and the provider. While the options for utilizing these technologies are almost endless, from the perspective of network architecture and standardized service, the deployment decisions on where to execute the AI-tasks are critical, especially when considering the dynamic and heterogeneous nature of processing and connectivity capability of 6G networks. On the other hand, conceptual and standardization work is still in its infancy, as to how to categorize ML applications in 6G landscapes;some of them are part of network management functions, some target the inference itself, while many others emphasize model training. It is likely that future mobile services may all be in the AI domain, or combined with AI. This work makes a case for the serverless computing paradigm to be used to this end. We first provide an overview of different machine learning applications that are expected to be relevant in 6G networks. We then create a set of general requirements for software engineering solutions executing these workloads from them and propose and implement a high-level edge-focused architecture to execute such tasks. We then map the ML-serverless paradigm to the case study of 6G architecture and test the resulting performance experimentally for a machine learning application against a setup created in a more traditional, cloud-based manner. Our results show that, while there is a tradeoff in predictability of the response times and the accuracy, the achieved median accuracy in a 6G setup remains the same, while the median response time decreases by around 25% compared to the cloud setup. © 2024 IEEE.

关键词： Queueing networks

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TGAIN:Geospatial Data Recovery Algorithm Based on GAIN-LSTM

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computers, Materials & Continua 2024年第10期81卷 1471-1489页

作者： Lechan Yang Li Li Shouming Ma School of Soft Engineering Jinling Institute of TechnologyNanjing211169China School of Computer Jinling Institute of TechnologyNanjing211169China School of Network Security Jinling Institute of TechnologyNanjing211169China

Accurate geospatial data are essential for geographic information systems(GIS),environmental monitoring,and urban *** deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial *** this paper,we consider abnormal spatial data as missing data and focus on abnormal spatial data *** geospatial data recovery methods require complete datasets for training,resulting in time-consuming data recovery and lack of *** address these issues,we propose a GAIN-LSTM-based geospatial data recovery method(TGAIN),which consists of two main works:(1)it uses a long-short-term recurrent neural network(LSTM)as a generator to analyze geospatial temporal data and capture its temporal correlation;(2)it constructs a complete TGAIN network using a cue-masked fusion matrix mechanism to obtain data that matches the original distribution of the input *** experimental results on two publicly accessible datasets demonstrate that our proposed TGAIN approach surpasses four contemporary and traditional models in terms of mean absolute error(MAE),root mean square error(RMSE),mean square error(MSE),mean absolute percentage error(MAPE),coefficient of determination(R2)and average computational time across various data missing ***,TGAIN exhibits superior accuracy and robustness in data recovery compared to existing models,especially when dealing with a high rate of missing *** model is of great significance in improving the integrity of geospatial data and provides data support for practical applications such as urban traffic optimization prediction and personal mobility analysis.

关键词： Geospatial data data recovery generative adversarial networks temporal correlation

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Resonant Beam Communications:A New Design Paradigm and Challenges

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Journal of Communications and Information networks 2024年第1期9卷 80-87页

作者： Yuanming Tian Dongxu Li Chuan Huang Qingwen Liu Shengli Zhou Future Network of Intelligence Institute and School of Science and Engineering the Chinese University of Hong KongShenzhen 518172China School of Science and Engineering and the Future Network of Intelligence Institute the Chinese University of Hong KongShenzhen 518172China College of Electronics and Information Engineering Tongji UniversityShanghai 201804China Department of Electrical and Computer Engineering University of ConnecticutStorrsCT 06250USA

Resonant beam communications (RBCom), which adopts oscillating photons between two separate retroreflectors for information transmission, exhibits potential advantages over other types of wireless optical communications (WOC). However, echo interference generated by the modulated beam reflected from the receiver affects the transmission of the desired information. To tackle this challenge, a synchronization-based point-to-point RBCom system is proposed to eliminate the echo interference, and the design for the transmitter and receiver is discussed. Subsequently,the performance of the proposed RBCom is evaluated and compared with that of visible light communications(VLC)and free space optical communications (FOC). Finally, future research directions are outlined and several implementation challenges of RBCom systems are highlighted.

关键词： resonant beam communications(RBCom) wireless optical communications(WOC) echo interference elimination mobility multiple access

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AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping

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Journal of Ambient Intelligence and Humanized Computing 2024年第7期15卷 3009-3033页

作者： Inani, Hardik Mehta, Veerangi Bhavsar, Drashti Gupta, Rajeev Kumar Jain, Arti Akhtar, Zahid Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Network and Computer Security State University of New York Polytechnic Institute NY United States

Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Chemical activation

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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Hard Real-Time Streaming of Large Data Objects with Overlapping Backward Error Correction 49

Hard Real-Time Streaming of Large Data Objects with Overlapp...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Bendrick, Alex Ernst, Rolf Technische Universität Braunschweig Institute of Computer and Network Engineering Germany

ISBN: (纸本)9798350331820

Despite expectations in roadmaps, the reliable V2X exchange of large data objects under age constraints remains an open challenge. Earlier work proposed a protocol that improved reliability by an application-level Backward Error Correction (BEC) mechanism that operates on data objects rather than packets. In this paper, we extend the protocol to overlapping data object transmission and investigate how far we can increase reliability by relaxing the object transmission deadline to realistic latency requirements as found in the literature. The extended protocol can be used for reliable coupling of nodes and wired network segments in the automotive and industrial domain that exchange large data. As an example, a camera stream transmission from truck platooning is used that aims at improving effectiveness of logistics as part of a large-scale industrial process. We present an analytical model for the extended error correction protocol and evaluate the reliability improvement with an OMNeT++ simulation model that incorporates a state-of-the-art burst error model. For realistic application deadlines, the results show a significant reliability improvement especially for burst errors, at constant protocol overhead. © 2023 IEEE.

关键词： Error correction

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Cache Allocation in Multi-Tenant Edge Computing: An Online Model-Based Reinforcement Learning Approach

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IEEE Transactions on Cloud Computing 2025年第2期13卷 459-472页

作者： Ben-Ameur, Ayoub Araldo, Andrea Chahed, Tijani Dán, György Institut Polytechnique de Paris SAMOVAR Telecom SudParis Palaiseau91120 France KTH Royal Institute of Technology Division of Network and Systems Engineering School of Electrical Engineering and Computer Science Stockholm11428 Sweden

We consider a network Operator (NO) that owns Edge Computing (EC) resources, virtualizes them and lets third party Service Providers (SPs) run their services, using the allocated slice of resources. We focus on one specific resource, i.e., cache space, and on the problem of how to allocate it among several SPs in order to minimize the backhaul traffic. Due to confidentiality guarantees, the NO cannot observe the nature of the traffic of SPs, which is encrypted. Allocation decisions are thus challenging, since they must be taken solely based on observed monitoring information. Another challenge is that not all the traffic is cacheable. We propose a data-driven cache allocation strategy, based on Reinforcement Learning (RL). Unlike most RL applications, in which the decision policy is learned offline on a simulator, we assume no previous knowledge is available to build such a simulator. We thus apply RL in an online fashion, i.e., the model and the policy are learned by directly perturbing and monitoring the actual system. Since perturbations generate spurious traffic, we thus need to limit perturbations. This requires learning to be extremely efficient. To this aim, we devise a strategy that learns an approximation of the cost function, while interacting with the system. We then use such an approximation in a Model-Based RL (MB-RL) to speed up convergence. We prove analytically that our strategy brings cache allocation boundedly close to the optimum and stably remains in such an allocation. We show in simulations that such convergence is obtained within few minutes. We also study its fairness, its sensitivity to several scenario characteristics and compare it with a method from the state-of-the-art. © 2013 IEEE.

关键词： Contrastive Learning

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Research on intelligent perception of computer vision environment for autonomous driving in complex environment based on cloud computing and improved Bayesian algorithm 3

Research on intelligent perception of computer vision enviro...

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3rd International Conference on Electronic Information engineering and Data Processing, EIEDP 2024

作者： Xu, Wang Department of Network Engineering School of Computer Science Guangdong Neusoft Institute Guangdong Foshan528225 China

ISBN: (纸本)9781510680531

In the field of autonomous driving, vision-based environmental perception methods usually use monocular or multi-ocular cameras to capture images, and use computer vision technology to analyze images to complete perceptual tasks such as object recognition, target detection and tracking, semantic segmentation, three-dimensional reconstruction, and distance measurement. Where there is a lack of prior knowledge or relational structure, the application of existing methods is limited. A method for calculating similarity matching between azimuth and polarity eigenvectors of components is proposed, and 5 defects of common components are accurately detected. The experimental results show that the proposed algorithm can quickly and accurately locate and identify PCB components, and effectively detect the defects of different components of the circuit board, because information is only passed between tightly connected nodes, this approach fails to capture novel interactions between distant nodes. The way to solve this limitation is to introduce an attention layer with induced sparsity to find. Therefore, based on specific projects in the laboratory, this paper conducts in-depth research on the visual environment perception for automatic driving in complex environments, aiming at the actual needs of intelligent unmanned units for automatic driving on complex off-road road conditions, and the problems such as low accuracy and insufficient robustness of existing algorithms. © 2024 SPIE.

关键词： Autonomous vehicles

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