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

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Intent-based Management for the distributed computing Continuum 17

Intent-based Management for the Distributed Computing Contin...

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17th ieee international Congress on Intelligent and Service-Oriented systems Engineering (CISOSE)

作者： Morichetta, Andrea Spring, Nikolaus Raith, Philipp Dustdar, Schahram TU Wien Distributed Syst Grp Vienna Austria

ISBN: (纸本)9798350322392

Managing digital and connected systems has become increasingly challenging in the past decade due to their scale and complexity. A new perspective is required to manage these systems, considering the infrastructure and components from edge to cloud, i.e., in the distributed computing continuum. Serverless computing offers improved scalability and cost efficiency, but balancing and coordinating serverless systems remain complex. Intent-based systems, popular in networking, can provide a solution by translating stakeholder inputs into actions that meet Service Level Objectives (SLOs). Their application in the computing continuum can be highly beneficial, but it has yet to be deeply explored. To bridge this gap, we propose a methodology for deploying an intent-based system for the computing continuum. We implement an architectural framework leveraging the serverless paradigm. Furthermore, we focus on defining and implementing the main components for translating the management requirements into actions executed by serverless functions inspired by a threelayer model. Through a Proof of Concept (PoC) deployed in Amazon's AWS cloud and detailed simulations, we showcase how such an approach can resolve conflicts in a complex system, i.e., balancing efficiency and availability. Our work aims to contribute to effectively managing the computing continuum and highlight the potential of intent-based systems in this domain. The experiments' results show our framework's ability to make appropriate scaling decisions, fulfilling both objectives.

关键词： computing continuum intents PoC serverless SLO

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A Flexible Inkjet-Printed Memristive sensor: Modeling and Simulation 67

A Flexible Inkjet-Printed Memristive Sensor: Modeling and Si...

引用

67th ieee international Midwest Symposium on Circuits and systems (MWSCAS)

作者： Adry, Tasnim Zaman Gardner, Steven D. Eliza, Sazia Afreen Haider, Mohammad Rafiqul Univ Missouri Columbia MIZZOU Dept EECS Columbia MO 65211 USA Univ Alabama Birmingham EITD Birmingham AL USA

ISBN: (纸本)9798350387186;9798350387179

The exploration of neuromorphic computing, driven by the demand for efficient data-intensive operations, relies on memristive devices for versatile information storage. In this study, a low-cost, nonlinear, current-controlled memristor using hexagonal boron nitride (hBN) and graphene inkjet-printed materials is proposed for neuromorphic computing applications. The memristor device, designed with a standard office piezoelectric printer, demonstrates the feasibility of inkjet printing technology for flexible and cost-effective computing solutions. The research investigates the use of nanoparticle-based inks and explores the potential of inkjet-printed circuits as an eco-friendly and power-efficient alternative. An empirical model is developed using MATLAB to analyze the behavior of the inkjet-printed memristor device, with insights complemented by a physical Simscape model. This work contributes to the advancement of flexible and low-cost memristor devices, facilitating their integration into neuromorphic computing architectures and other applications requiring versatile information storage.

关键词： Neuromorphic computing Memristors MATLAB modeling Inkjet printing Flexible Electronics

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Labeled Multi-Bernoulli Filter for distributed multi-target Tracking with Detection and Class Measurements 27

Labeled Multi-Bernoulli Filter for Distributed multi-target ...

引用

27th international conference on Information Fusion (FUSION)

作者： Klupacs, James Gostar, Amirali K. Bab-Hadiashar, Alireza Hoseinnezhad, Reza RMIT Univ Sch Engn Melbourne Vic Australia

ISBN: (纸本)9798350371420;9781737749769

Advanced machine vision and deep learning models are now widely used as virtual sensors in various applications to detect and classify objects in image measurements. Typically, these virtual sensors output measurements as a set of class probabilities for each detected object within the sensor's field of view. However, integrating this type of data into multi-target tracking systems, traditionally based on point measurement detections, presents some challenges. This paper proposes a solution by introducing a labeled multi-Bernoulli filter formulated to handle detections with class measurements from virtual sensors for multi-target tracking. Furthermore, we explore the application of this filter for multi-sensor multi-target tracking within a distributed sensor network. Through numerical experiments involving vehicles equipped with cameras and deep learning classification modules navigating complex roads, we demonstrate that incorporating class information into the tracking process improves tracking accuracy. This improvement is further enhanced when vehicles share and fuse their information in a distributed manner. Our findings highlight the benefits of integrating class probability data into multi-target tracking filters, offering substantial improvements in the accuracy of automated monitoring systems in dynamic and complex environments.

关键词： Random Finite Sets Intelligent Transport systems Multi-Object Tracking Information Fusion Class-Based systems

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MOTLEE: distributed Mobile Multi-Object Tracking with Localization Error Elimination

MOTLEE: Distributed Mobile Multi-Object Tracking with Locali...

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ieee/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Peterson, Mason B. Lusk, Parker C. How, Jonathan P. MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

ISBN: (纸本)9781665491907

We present MOTLEE, a distributed mobile multi-object tracking algorithm that enables a team of robots to collaboratively track moving objects in the presence of localization error. Existing approaches to distributed tracking make limiting assumptions regarding the relative spatial relationship of sensors, including assuming a static sensor network or that perfect localization is available. Instead, we develop an algorithm based on the Kalman-Consensus filter for distributed tracking that properly leverages localization uncertainty in collaborative tracking. Further, our method allows the team to maintain an accurate understanding of dynamic objects in the environment by realigning robot frames and incorporating frame alignment uncertainty into our object tracking formulation. We evaluate our method in hardware on a team of three mobile ground robots tracking four people. Compared to previous works that do not account for localization error, we show that MOTLEE is resilient to localization uncertainties, enabling accurate tracking in distributed, dynamic settings with mobile tracking sensors.

关键词： Errors

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Noise Analysis and Modeling of the PMD Flexx2 Depth Camera for Robotic Applications

Noise Analysis and Modeling of the PMD Flexx2 Depth Camera f...

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ieee international conference on Omni-Layer Intelligent systems (ieee COINS)

作者： Cai, Yuke Plozza, Davide Marty, Steven Joseph, Paul Magno, Michele Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9798350349603;9798350349597

Time of Flight (ToF) cameras, renowned for their ability to capture real-time 3D information, have become indispensable for agile mobile robotics. These cameras utilize light signals to accurately measure distances, enabling robots to navigate complex environments with precision. Innovative depth cameras, characterized by their compact size and lightweight design, such as the recently released PMD Flexx2, are particularly suited for mobile robots. Capable of achieving high frame rates while capturing depth information, this innovative sensor is suitable for tasks such as robot navigation and terrain mapping. Operating on the ToF measurement principle, the sensor offers multiple benefits over classic stereo-based depth cameras. However, the depth images produced by the camera are subject to noise from multiple sources, complicating their simulation. This paper proposes an accurate quantification and modeling of the non-systematic noise of the PMD Flexx2. We propose models for both axial and lateral noise across various camera modes, assuming Gaussian distributions. Axial noise, modeled as a function of distance and incidence angle, demonstrated a low average Kullback-Leibler (KL) divergence of 0.015 nats, reflecting precise noise characterization. Lateral noise, deviating from a Gaussian distribution, was modeled conservatively, yielding a satisfactory KL divergence of 0.868 nats. These results validate our noise models, crucial for accurately simulating sensor behavior in virtual environments and reducing the sim-to-real gap in learning-based control approaches.

关键词： Depth sensor ToF PMD Flexx2 noise model noise characterization robot perception

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ADSwitch: Adaptive CPU Scheduling for distributed systems 21

ADSwitch: Adaptive CPU Scheduling for Distributed Systems

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21st ieee international Symposium on Parallel and distributed Processing with Applications, 13th ieee international conference on Big Data and Cloud computing, 16th ieee international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhong, Yidong Ao, Yang Li, Dingding Li, Jianguo Zeng, Deze South China Normal University School of Computer Science Guangdong Guangzhou510631 China China University of Geosciences School of Computer Science Hubei Wuhan430074 China

ISBN: (纸本)9798350329223

In traditional CPU scheduling systems, it is challenging to customize scheduling policies for datacenter workloads. Therefore, distributed cluster managers can only perform coarse-grained job scheduling rather than fine-grained CPU scheduling, although the latter is also critical to application performance in the clusters. Some scheduling systems have been proposed to facilitate the implementation and deployment of custom policies. However, these systems neither adequately capture the resource requirements of workloads nor cooperatively manage the CPU scheduling of distributed nodes, thus may failing to fully utilize CPU resources in the clusters. To tackle these issues, we present ADSwitch, a distributed CPU scheduling system that manages CPU resources adaptively in datacenter clusters. We train an efficient workload classification model based on a recurrent neural network (RNN), which enables ADSwitch to dynamically identify the demand of the running workload and switch to an appropriate scheduling policy through a reliable online update mechanism. We implement ADSwitch and evaluate it in a distributed cluster with synthetic workloads. The experimental results demonstrate that ADSwitch achieves up to 33% lower tail latency and 28% higher throughput than the existing scheduling systems without workload characteristic awareness. © 2023 ieee.

关键词： Recurrent neural networks

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Q-Segment: Segmenting Images In-sensor for Vessel-Based Medical Diagnosis 6

Q-Segment: Segmenting Images In-Sensor for Vessel-Based Medi...

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6th international conference on AI Circuits and systems (AICAS)

作者： Bonazzi, Pietro Li, Yawei Bian, Sizhen Magno, Michele Swiss Fed Inst Technol D ITET Zurich Switzerland

ISBN: (纸本)9798350383638;9798350383645

This paper addresses the growing interest in deploying deep learning models directly in-sensor. We present "Q-Segment", a quantized real-time segmentation algorithm, and conduct a comprehensive evaluation on a low-power edge vision platform with an in-sensors processor, the Sony IMX500. One of the main goals of the model is to achieve end-to-end image segmentation for vessel-based medical diagnosis. Deployed on the IMX500 platform, Q-Segment achieves ultra-low inference time in-sensor only 0.23 ms and power consumption of only 72mW. We compare the proposed network with state-of-the-art models, both float and quantized, demonstrating that the proposed solution outperforms existing networks on various platforms in computing efficiency, e.g., by a factor of 75x compared to ERFNet. The network employs an encoder-decoder structure with skip connections, and results in a binary accuracy of 97.25 % and an Area Under the Receiver Operating Characteristic Curve (AUC) of 96.97 % on the CHASE dataset. We also present a comparison of the IMX500 processing core with the Sony Spresense, a low-power multi-core ARM Cortex-M microcontroller, and a single-core ARM Cortex-M4 showing that it can achieve in-sensor processing with end-to-end low latency (17 ms) and power consumption (254mW). This research contributes valuable insights into edge-based image segmentation, laying the foundation for efficient algorithms tailored to low-power environments. [GRAPHICS] .

关键词： AIoT neural networks image segmentation blood vessel segmentation TinyML edge processing medical imagining retina vessel

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VPFL: A Verifiable Property Federated Learning Framework Against Invisible Attacks in distributed IoT 25

VPFL: A Verifiable Property Federated Learning Framework Aga...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wu, Yuanxiang Cheng, Hang Guan, Lin Liu, Peihao Chen, Fei Wang, Meiqing College of Computer Science and Big Data Fuzhou University Fuzhou China

ISBN: (纸本)9798350330014

The development of Federated Learning (FL) offers an efficient Machine Learning (ML) approach with privacy protection to solve the data island issue in distributed Internet of Things (IoT). However, existing FL frameworks still suffer from invisible attacks in IoT environments, such as free-rider attacks, backdoor attacks, and model theft. In this paper, we propose a Verifiable Property Federated Learning (VPFL) framework to overcome the above invisible attacks. We present a black-box watermarking task distribution mechanism to prevent free-rider attacks by verifying the property of local models. Our adversarial fine-tuning embedding technique can not only eliminate backdoors in global models, but also simultaneously embed white-box watermarks into model parameters to prevent model theft. Comprehensive experimental evaluations demonstrate that our framework outperforms state-of-the-art schemes in terms of security and feasibility against invisible attacks. © 2023 ieee.

关键词： Internet of things

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ONU: A Reinforcement Load Balancing Approach for Resource-Aware Task Allocation in distributed systems 6

ONU: A Reinforcement Load Balancing Approach for Resource-Aw...

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6th ieee international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Shahakar, Minal Mahajan, S.A. Patil, Lalit Smt. Kashibai Navale College of Engineering Savitribai Phule Pune University India IOM Department of Information Technology Savitribai Phule Pune University India Smt. Kashibai Navale College of Engineering Savitribai Phule Pune University Department of Information Technology India

ISBN: (纸本)9798350306613

distributed systems have revolutionized modern computing by enabling the parallel execution of large-scale tasks across interconnected nodes. However, the heterogeneity of resources in distributed systems often leads to load imbalances and underutilization of certain nodes, resulting in suboptimal performance. To address these challenges, this research paper presents a novel approach for resource-aware task distribution based on reinforcement load balancing in distributed systems with over utilized, normally utilized, and underutilized nodes. The proposed approach leverages reinforcement learning algorithms to dynamically distribute tasks among nodes based on their resource utilization levels. ONU module is designed to cater to the specific needs of each node category: the ONU Algorithm focuses on mitigating overburdened nodes, moderate resource usage nodes, and the underutilized nodes operating below capacity. The results demonstrate significant improvements in load balancing and resource utilization compared to traditional load balancing techniques. © 2024 ieee.

关键词： distributed systems Heterogeneous Resources Load Distribution Reinforcement Load Balancing Resource Utilization Task Allocation

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Enhancing Visual Inertial Odometry Performance using Deep Learning-based sensor Fusion 17

Enhancing Visual Inertial Odometry Performance using Deep Le...

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ieee Congress on Cybermatics: 17th ieee international conference on Internet of Things, iThings 2024, 20th ieee international conference on Green computing and Communications, GreenCom 2024, 17th ieee international conference on Cyber, Physical and Social computing, CPSCom 2024, 10th ieee international conference on Smart Data, SmartData 2024

作者： Doorshi, Raoof Saleem, Hajira Malekian, Reza Malmö University Department of Computer Science and Media Technology Malmö20506 Sweden Malmö University Internet of Things and People Research Centre Malmö20506 Sweden

ISBN: (纸本)9798350351637

Odometry estimation plays a key role in facilitating autonomous navigation systems. While significant consideration has been devoted to research on monocular odometry estimation, sensor fusion techniques for Stereo Visual Odometry (SVO) have been relatively neglected due to their demanding computational requirements, posing practical challenges. However, recent advancements in hardware, particularly the integration of CPUs with dedicated artificial intelligence units, have alleviated these concerns. In this paper, we investigate the efficacy of attention mechanisms and the incorporation of stereo input in comparison to monocular odometry, aiming to enhance the performance of SVO. We tested two different types of attention mechanisms, i.e., Triplet Attention (TA) and Convolutional Block Attention Module (CBAM), and their fusion in two stages Early and Late. Our results show that the fusion of the second camera improves the performance of the model, as well as early fusion with TA provided the best results. © 2024 ieee.

关键词： Deep learning

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