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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

共 30762 条记录，以下是271-280 订阅

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Comprehensive Framework for Facilitating the Deployment of distributed On-Premise Analytics Applications in Resource-Constraint Environments 20

Comprehensive Framework for Facilitating the Deployment of D...

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ieee 20th international conference on Automation Science and Engineering (CASE)

作者： Schoch, Nicolai Becker, Pascal Ashiwall, Virendra Habib, Andrew ABB Corp Res Ctr Germany Ladenburg Germany

ISBN: (纸本)9798350358513;9798350358520

Data Analytics (DA) and Machine Learning (ML) algorithms today are inside many applications and systems. They provide state-of-the-art anomaly detection, pattern recognition, health monitoring, predictive maintenance, or help to optimize and calibrate parameter combinations. However, they usually require strong compute resources. It is therefore a challenge to deploy the corresponding algorithms on edge devices or in embedded systems, such as on cloud-decoupled drives and drive systems, as these usually have strict resource constraints. In this work, we look at the deployment of DA/ML algorithms on combinations of resource-constraint edge and device environments. We present our framework for the structured and guided definition, setup, distribution, and deployment of such DA/ML algorithms. A guided workflow leads a user through algorithm specification and configuration. While doing so, it extracts information, which is necessary for the distribution and deployment of the DA/ML algorithm to a suitable device-edge combination. With this solution, a DA/ML algorithm can be executed for sensor data processing and, in the ML context, for inference, on device-edge combinations without permanent cloud connection, e.g., on a drive/gateway combination on a cruise ship, so that it can be used for real-time anomaly detection or pattern analysis.

关键词： distributed analytics on-premise deployment resource-constraint analytics machine learning deployment industrial communication technologies

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PTP-Synchronized Tri-Level Sync Generation for Networked Multi-sensor systems 30

PTP-Synchronized Tri-Level Sync Generation for Networked Mul...

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30th ieee international conference on Embedded and Real-Time computing systems and Applications (RTCSA)

作者： Riggers, Christoph Schleusner, Jens Renke, Oliver Blume, Holger Leibniz Univ Hannover Inst Microelect Syst Hannover Germany

ISBN: (纸本)9798350387964;9798350387957

Synchronization of sensor devices is crucial for concurrent data acquisition. Numerous protocols have emerged for this task, and for some multi-sensor setups to operate synchronized, a conversion between deployed protocols is needed. This paper presents a bare-metal implementation of a Tri-Level Sync signal generator on a microcontroller unit (MCU) synchronized to a master clock via the ieee 1588 Precision Time Protocol (PTP). Cameras can be synchronized by locking their frame generators to the Tri-Level Sync signal. As this synchronization depends on a stable analog signal, a careful design of the signal generation based on a PTP-managed clock is required. The limited tolerance of a camera to clock frequency adjustments for continuous operations imposes rate-limits on the PTP-controller. Simulations using a software model demonstrate the resulting controller instabilities from rate-limiting. This problem is addressed by introducing a linear prediction mode to the controller, which estimates the realizable offset change during rate-limited frequency alignment. By adjusting the frequency in a timely manner, a large overshoot of the controller can be avoided. Additionally, a cascading controller design that decouples the PTP from the clock update rate proved to be advantageous to increase the camera's tolerable frequency change. This paper demonstrates that a MCU is a viable platform to perform PTP-synchronized Tri-Level Sync generation. Our open source implementation is available for use by the research community at https://***/IMS-AS-LUH/t41-tri-sync-ptp.

关键词： Precision Time Protocol PTP Tri-Level Sync camera SMPTE PI-controller synchronization MCU

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SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split computing 43

SplitBeam: Effective and Efficient Beamforming in Wi-Fi Netw...

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43rd ieee international conference on distributed computing systems (ICDCS)

作者： Bahadori, Niloofar Matsubara, Yoshitomo Levorato, Marco Restuccia, Francesco Univ Calif Irvine Donald Bren Sch Informat & Comp Sci Irvine CA 92697 USA Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA

ISBN: (纸本)9798350339864

Modern ieee 802.11 (Wi-Fi) networks extensively rely on multiple-input multiple-output (MIMO) to significantly improve throughput. To correctly beamform MIMO transmissions, the access point needs to frequently acquire a beamforming matrix (BM) from each connected station. However, the size of the matrix grows with the number of antennas and subcarriers, resulting in an increasing amount of airtime overhead and computational load at the station. Conventional approaches come with either excessive computational load or loss of beamforming precision. For this reason, we propose SplitBeam, a new framework where we train a split deep neural network (DNN) to directly output the BM given the channel state information (CSI) matrix as input. The DNN is designed with an additional "bottleneck" layer to "split" the original DNN into a head model and a tail model, respectively executed by the station and the access point. The head model generates a compressed representation of the BM, which is then used by the AP to produce the BM using the tail model. We formulate and solve a bottleneck optimization problem (BOP) to keep computation, airtime overhead, and bit error rate (BER) below application requirements. We perform extensive experimental CSI collection with off-the-shelf Wi-Fi devices in two distinct environments and compare the performance of SplitBeam with the standard ieee 802.11 algorithm for BM feedback and the state-of-the-art DNN-based approach LB-SciFi. Our experimental results show that SplitBeam reduces the beamforming feedback size and computational complexity by respectively up to 81% and 84% while maintaining BER within about 10(-3) of existing approaches. We also implement the SplitBeam DNNs on FPGA hardware to estimate the end-to-end BM reporting delay, and show that the latter is less than 10 milliseconds in the most complex scenario, which is the target channel sounding frequency in realistic multiuser MIMO scenarios. To allow full reproducibility, we will r

关键词： Split computing MIMO ieee 802.11 Wi-Fi Beamforming Experiments

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Distributing Compilation to Enable High Throughput Scalable Quantum Workloads 5

Distributing Compilation to Enable High Throughput Scalable ...

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2024 international conference on Quantum computing and Engineering

作者： Waring, Harry J. Oxford Quantum Circuits Reading Berks England

ISBN: (纸本)9798331541378

The increasing quality and availability of Quantum Processing Units (QPUs) is fueling a growing interest in quantum computing across many technological areas. The resulting increase in demand for QPU resources necessitates Quantum computing as a Service (QCaaS) providers to support a high throughput of quantum workloads. A major runtime bottleneck in current QCaaS software stacks is the computationally-intensive compilation step which requires significant compute. To address this, Oxford Quantum Circuits has introduced distributed compilation whereby quantum programs are compiled in parallel and stored until the QPU is available. This has replaced our previous serial compilation approach where each program was compiled immediately prior to execution. From experiments using our production compilers and a simulated backend representing the QPU, we show that distributed compilation has resulted in a 78% reduction in processing time as compared to serial compilation. This demonstrates that there are sizeable performance gains to program throughput attainable through the introduction of distributed compilation into a QCaaS architecture. We posit that the usefulness of this feature will only grow given the increasing complexity of quantum programs and the growing popularity of quantum -classical hybrid algorithms.

关键词： distributed Compilation Quantum computing Quantum computing as a Service Quantum systems Software

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SensoDat: Simulation-based sensor Dataset of Self-driving Cars 21

SensoDat: Simulation-based Sensor Dataset of Self-driving Ca...

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ieee/ACM 21st international conference on Mining Software Repositories (MSR)

作者： Birchler, Christian Rohrbach, Cyrill Kehrer, Timo Panichella, Sebastiano Zurich Univ Appl Sci Zurich Switzerland Univ Bern Bern Switzerland

ISBN: (纸本)9798350363982;9798400705878

Developing tools in the context of autonomous systems [22, 24], such as self-driving cars (SDCs), is time-consuming and costly since researchers and practitioners rely on expensive computing hardware and simulation software. We propose SensoDat, a dataset of 32,580 executed simulation-based SDC test cases generated with state-of-the-art test generators for SDCs. The dataset consists of trajectory logs and a variety of sensor data from the SDCs (e.g., rpm, wheel speed, brake thermals, transmission, etc.) represented as a time series. In total, SensoDat provides data from 81 different simulated sensors. Future research in the domain of SDCs does not necessarily depend on executing expensive test cases when using SensoDat. Furthermore, with the high amount and variety of sensor data, we think SensoDat can contribute to research, particularly for AI development, regression testing techniques for simulation-based SDC testing, flakiness in simulation, etc.

关键词： Autonomous vehicles

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Hierarchical Communication Optimization for distributed DNN Training 31

Hierarchical Communication Optimization for Distributed DNN ...

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31st ieee international conference on High Performance computing, Data, and Analytics Workshops, HiPCW 2024

作者： Verma, Om Shivam Malakar, Preeti Indian Institute of Technology Kanpur India

ISBN: (纸本)9798331509118

Motivation: Large scale deep neural network models use a lot of training data, evident from the large datasets curated for their training, such as ImageNet. Thus it is necessary to use high performance computing (HPC) systems for training of these large-scale models. Various techniques have been developed for parallelizing model training - data-parallelism, model-parallelism, and the more recent FSDP (Fully Sharded Data Parallel). © 2024 ieee

关键词： Neural network models

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Chord-based distributed middleware architecture for Internet of Things 19

Chord-based distributed middleware architecture for Internet...

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19th ieee international Wireless Communications and Mobile computing (ieee IWCMC)

作者： Fersi, Ghofrane Ben Hammouda, Amine Derbel, Faouzi Univ Sfax Res Lab Dev & Control Distributed Applicat ReDCAD Natl Sch Engn Sfax BP 1173-3038 Sfax Tunisia Leipzig Univ Appl Sci Smart Diagnost & Online Monitoring Leipzig Germany

ISBN: (纸本)9798350333398

The outstanding progress in the smart devices as well as the noticeable reduction in their cost has led to the invasion of the Internet of Things (IoT) in our real lives. The multiplicity of the IoT applications and the diversity and heterogeneity of the IoT devices present a real challenge in the IoT development. There is hence a need to have an intermediate layer between the physical layer including the smart devices and the application layer to facilitate the integration and the usage of the IoT devices in the IoT applications. This layer is the middleware layer. Most of the existing middleware are suitable for small-scale IoT networks including limited number of smart things. The performance of these middleware is reduced when the number of IoT devices increases. The main reason of the performance deterioration is the unsuitable middleware architecture that is unable to bear the increasing number of IoT devices. We present in this paper a distributed middleware architecture based on Chord protocol. Our architecture transforms any centralized middleware to a distributed one which improves its performance and makes it scalable. The performance evaluation have shown that our proposed approach has the ability to cope successfully with the increasing number of IoT devices and requests.

关键词： Middleware IoT distributed Hash Tables Chord

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CUPREDS: Multi-sensor Point Cloud Mapping for Local Navigation

CUPREDS: Multi-sensor Point Cloud Mapping for Local Navigati...

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international conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Tojal, Carlos Bento, Luis Conde Peixoto, Paulo Univ Coimbra Inst Syst & Robot Coimbra Portugal Polytech Inst Leiria ESTG Leiria Portugal Univ Coimbra Dept Elect & Comp Engn Coimbra Portugal

ISBN: (纸本)9798350352351;9798350352344

Robot mapping usually benefits from the combination of several sensors. However, most algorithms for Simultaneous Localization And Mapping using point clouds only consider a single point cloud as input. There are also scenarios where SLAM is not needed and a simple local map representation without probabilities is preferred, for example due to throughput requirements. For these scenarios, software is needed that combines multiple point clouds into a unified, short-lived, robot-centric representation. Mapping software is only one tool in the software stack and should not compromise other components, which is why its performance-oriented design is of utmost importance. CUPREDS presents a ROS package for local mapping that prioritizes robustness and performance, with minimal overhead for its internal mechanisms.

关键词： robot mapping point clouds software engineering parallel computing

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Malware Detection By distributed systems with Partial Centralization 12

Malware Detection By Distributed Systems with Partial Centra...

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12th ieee international conference on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications, IDAACS 2023

作者： Savenko, Bohdan Kashtalian, Antonina Lysenko, Sergii Savenko, Oleg Khmelnytskyi National University Khmelnytskyi29016 Ukraine

ISBN: (纸本)9798350358056

The paper proposes approach to use new tools for malware detection in corporate networks, which are distributed systems with partial centralization. To make decision about malware presence, the components which includes specified decision-making for the system's center are defined as a decentralized subsystem. To determine the states of the system components, characteristic indicators are proposed, and generalized analytical expressions for their calculation are developed. Such calculations make it possible to assess the state of the components in the system in order to determine its further steps. As a result, the system is the basis for usage of different malware detection methods in combination with the system components as an integral sensor. To test the system, a worm-virus detection method was implemented and experiments were conducted. The results of experimental studies approved the efficiency of the proposed solution. © 2023 ieee.

关键词： distributed systems malware malware detection network security partial centralization worm-virus

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A Prototype-Based Knowledge Distillation Framework for Heterogeneous Federated Learning 43

A Prototype-Based Knowledge Distillation Framework for Heter...

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43rd ieee international conference on distributed computing systems (ICDCS)

作者： Lyu, Feng Tang, Cheng Deng, Yongheng Liu, Tong Zhang, Yongmin Zhang, Yaoxue Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Tsinghua Univ Dept Comp Sci & Technol BNRist Beijing Peoples R China Cent South Univ Big Data Inst Changsha Peoples R China

ISBN: (纸本)9798350339864

Federated learning (FL) is an emerging distributed machine learning paradigm, which has shown great potential in collaborative learning with privacy preservation. However, FL clients usually have disparate system resource capabilities (e.g., data, computation, and communication) for model training and aggregation, which can cause a series of system heterogeneity issues with performance degradation. To this end, we propose FedPKD, a Prototype-based Knowledge Distillation framework for FL. FedPKD integrates knowledge distillation and prototype learning with FL, which enables heterogeneous clients and the server to learn collaboratively, with different model architectures and resource capability adaptations. Specifically, FedPKD proposes to transfer dual knowledge of clients including the model output logits and prototypes to the server, and a prototype-based ensemble distillation mechanism is proposed to aggregate the logits and prototypes from clients, which can be used to train the server model with an unlabeled public dataset. The server model knowledge is then transferred back to clients to improve the performance of client models. Moreover, to improve learning performance and reduce communication overhead, we propose a prototype-based data filter mechanism to filter out the samples with low-quality knowledge. Extensive experiments under various settings demonstrate the superiority of FedPKD in learning performance and communication efficiency when compared to state-of-the-art benchmarks.

关键词： distributed IoTs Federated Learning Heterogeneity Prototype-Based Knowledge Distillation

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