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检索条件"任意字段=2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024"

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Accuracy-Oriented Data Offloading for Cooperative Video Analytics in Internet of Connected Vehicles

Accuracy-Oriented Data Offloading for Cooperative Video Anal...

引用

ieee/CIC international conference on Communications in China (ICCC)

作者： Huang, Jianhua Rong, Shichu Zhong, Weifeng Kang, Jiawen Huang, Xumin Yu, Peng Liu, Yuan Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou Peoples R China

ISBN: (纸本)9798350378412

Onboard cameras are widely deployed in vehicles for enhancing their sensing abilities and further supporting their real-time control on the road. However, the sensing range of a single vehicle is limited and even blocked by the inevitable occlusion among the moving vehicles. Cooperative computing is an effective and promising solution for this single-vehicle issue. This paper studies cooperative video analytics for a cluster of wireless connected vehicles. First, the raw video frames generated on the vehicles are downsampled to reduce the data size, improving the efficiency of communication and computing. The downsampled video data are then processed by using the collective computing resources of the vehicles and road side units (RSUs). A non-convex optimization problem maximizing the overall video analytics accuracy in the vehicle cluster is formulated subject to the latency constraints. To quickly solve the problem, an efficient three-phase auction-based method is proposed, making decisions on optimal data downsampling and offloading. Results demonstrate the time-efficiency of the proposed method and its advantage in boosting video analytics accuracy by efficiently exploiting the limited bandwidth and computing resources in the vehicle cluster.

关键词： Cooperative computing connected vehicles data offloading video analytics

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A Layered Strategy for Reducing Offloading Latency in Fog computing 9

A Layered Strategy for Reducing Offloading Latency in Fog Co...

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9th ieee international conference on Fog and Mobile Edge computing (FMEC)

作者： Chebaane, Ahmed Arshad, Muhammad Kamran Burger, Florian Khelil, Abdelmajid Landslutt Univ Appl Sci Inst Data & Proc Sci CS Dept Landshut Germany Univ Salerno CS Dept Fisciano Italy

ISBN: (纸本)9798350366495;9798350366488

Moving applications from the local Internet of Things devices to neighboring infrastructures has been the highlight of the current distributed computing era. Due to high latency and network bottlenecks, Cloud computing has become rather problematic for real-time applications. Accordingly, Fog computing has been presented as the missing piece to provide along with cloud computing for virtually all applications. Application virtualization through containers has become more and more popular in the context of Fog computing as it simplifies live migration and, in particular, offloading of time-critical applications or their selected tasks. However, the measurement study of the overall offloading latency in Fog computing has not been sufficiently investigated. This article proposes a measurement analysis of the offloading latency of time-critical application tasks from the end device, such as a vehicle, to surrounding Fog nodes based on Docker containers, Kubernetes, and checkpointing. Based on our layered offloading framework, several offloading scenarios have been studied. The experimental results show that by preparing the Fog nodes in advance through careful, easy arrangements, considerable time can be saved when offloading the application to the Fog node within the Fog cluster. We further consider Vehicular Fog computing (VFC) as a popular example of time-critical Fog computing and assess the suitability of various offloading strategies for VFC use cases.

关键词： Fog computing Vehicular Fog computing Task Offloading Checkpoints Application Virtualization Latency-Critical Applications

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Dynamic Clustering-based Task Orchestrator in Mobile Edge computing 20

Dynamic Clustering-based Task Orchestrator in Mobile Edge Co...

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20th ieee international Wireless Communications and Mobile computing (ieee IWCMC) - Green and Intelligent Communications

作者： Alghamdi, Mona Alam, Atm Nallanathan, Arumugam Cherif, Asma Queen Mary Univ London Sch Elect Engn & Comp Sci London England King AbdulAziz Univ Dept Informat Technol Fac Comp & Informat Technol Ctr Excellence Smart Environm Res Jeddah Saudi Arabia

ISBN: (纸本)9798350361261;9798350361278

Multi-access Edge computing (MEC) is an emerging paradigm designed to provide storage, computing and communication capabilities in the proximity of end-user devices. This approach facilitates the deployment of real-time on mobile devices with limited capabilities. To realize the MEC goals, it is essential to effectively manage and offload computing tasks to both edge and cloud-based resources. However, the dynamic nature, uncertainty and mobility within edge computing environments pose significant challenges to resource management. Furthermore, the inherent software and hardware heterogeneity, coupled with the distributed nature of architecture, complicates the development of efficient task offloading strategies that can adeptly manage resources across both edge and cloud platforms. In this paper, we propose a cluster-based task edge orchestrator, where edge servers are grouped based on service demands, resource ability and other factors to improve the overall service. Our proposed method leverages the K-Medoids clustering algorithm to dynamically form clusters of suitable edge servers for offloading computing tasks with minimum response time. To validate our proposed solution, we have orchestrated a comprehensive series of tests using EdgeCloudSim. Results show that our approach outperforms its competitor in terms of average service time by around 8%.

关键词： Edge computing Orchestration Edge Orchestrator Task Offloading Machine Learning

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2024 24th ieee international conference on Automation, Quality and Testing, robotics, AQTR 2024 - Proceedings

2024 24th IEEE International Conference on Automation, Quali...

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24th ieee international conference on Automation, Quality and Testing, robotics, AQTR 2024

ISBN: (纸本)9798350361933

The proceedings contain 61 papers. The topics discussed include: alternating aeration control and optimization in activated sludge processes;a knowledge graph approach to cyber threat mitigation derived from data flow diagrams;autonomous robot relocalization with motion constraints;a digital twin framework for applications in ophthalmology;real-time simultaneous optimistic planning for hybrid-input nonlinear optimal control;anomaly detection in water consumption patterns using prediction and clustering approaches;one phase open-circuit fault-tolerant vector control for a five phase PMSM machine;adapting supervised machine learning for analysis of neurobiological signals;and optimizing the test plan for a mobile financial technology application in the scaled agile framework methodology.

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2024 22nd international conference on Research and Education in Mechatronics, REM 2024

2024 22nd International Conference on Research and Education...

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22nd international conference on Research and Education in Mechatronics, REM 2024

ISBN: (纸本)9798331505974

The proceedings contain 69 papers. The topics discussed include: attitudes and perspectives for installing EV charging station: a case study at Isra University, Jordan;polymer-polymer bonding for enhanced functional micro-fluidic chips fabrication;intelligent exoskeleton-based parallel robot for guiding blind people;optimization of pneumatic actuation arrays for upper-limb exoskeleton suit applications based on experimental modeling;exploring the impact of Arduino robotics instruction on physical computing and programming skills;design of a real-time detection system for potholes and bumps using deep learning;classification of production process phases with multivariate time series techniques;optimizing formula student car chassis to enhance torsional stiffness for improved vehicle handling performance;and optimizing of IT-tools and production processes for supporting the participation of workers with disabilities in the industry.

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Hardware Acceleration for Transposed Convolution Based on NVDLA 3

Hardware Acceleration for Transposed Convolution Based on NV...

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3rd international conference on Artificial Intelligence, Human-Computer Interaction and robotics, AIHCIR 2024

作者： Tian, Hanxi Lin, Xuehong School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798331534035

As the parameters of deep learning models continue to scale, the need for high-performance computing becomes more critical. At the same time, it places greater demands on the types of operators, memory size, and computational volume required by neural network accelerators. Transposed convolution, a key operation for tasks like super-resolution and image generation, is essential for preserving spatial information during processing. This paper explores the relationship between convolution and transposed convolution and presents an efficient hardware implementation of the transposed convolution operator based on the convolution operator of the NVIDIA Deep Learning Accelerator (NVDLA). We introduce an enhancement to NVDLA that adds support for transposed convolution with a stride greater than or equal to 1, yielding substantial gains without significant overhead in hardware resources. © 2024 ieee.

关键词： hardware acceleration high-performance computing NVDLA transposed convolution

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Furcifer: a Context Adaptive Middleware for real-world Object Detection Exploiting Local, Edge, and Split computing in the Cloud Continuum 22

Furcifer: a Context Adaptive Middleware for Real-world Objec...

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ieee international conference on Pervasive computing and Communications (PerCom)

作者： Mendula, Matteo Bellavista, Paolo Levorato, Marco Contreras, Sharon Ladron de Guevara Univ Bologna Dept Comp Sci & Engn Bologna Italy Univ Calif Irvine Donald Bren Sch Informat & Comp Sci Irvine CA USA

ISBN: (纸本)9798350326031;9798350326048

Modern real-time applications widely embed compute intense neural algorithms at their core. Current solutions to support such algorithms either deploy highly-optimized Deep Neural Networks at mobile devices or offload the execution of possibly larger higher-performance neural models to edge servers. While the former solution typically maps to higher energy consumption and lower performance, the latter necessitates the low-latency wireless transfer of high volumes of data. time-varying variables describing the state of these systems, such as connection quality and system load, determine the optimality of the different computing configurations in terms of energy consumption, task performance, and latency. Herein, we propose Furcifer, a framework capable of dynamically adapting the cloud continuum computing configuration in response to the perceived state of the system. Our container-based approach incorporates low-complexity predictors that generalize well across operating environments. In addition, we develop a highly optimized split Deep Neural Network model, which achieves in-model supervised compression and enhances task offloading. Experimental results for object detection across diverse conditions, environments, and wireless technologies, show Furcifer's remarkable outcomes, including a 2x energy reduction, 30% higher mean Average Precision score than pure local computing, and a notable three-fold increase in frame per second rate compared to static offloading.

关键词： Edge computing Machine Learning Object Detection Mobile agents Image compression

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Game-Hypothetical Methodology for Continuous Undertaking Planning in Distributed computing Conditions

Game-Hypothetical Methodology for Continuous Undertaking Pla...

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2024 international conference on Computer Communication, Networks and Information Science, CCNIS 2024

作者： Gautam, Puneet Harrisburg University of Science and Technology Information Systems Engineering HarrisburgPA United States

ISBN: (纸本)9798331507046

Cloud computing, with its fundamental virtualization technique, offers a flexible and powerful platform for deploying various applications. real-time task scheduling in cloud environments, however, presents significant challenges due to timing constraints and the need for efficient resource allocation. This paper proposes a game-theoretic method for real-time task scheduling, leveraging both cooperative and non- cooperative game models. A systematic framework is developed, considering tasks as players and virtual machines as strategies. Through extensive experimental analysis, the effectiveness of the proposed approach is demonstrated, showing superior perfor- mance in terms of completion time and waiting time compared to traditional scheduling methods. Additionally, future research directions are outlined, including the integration of additional evaluation metrics to enhance scheduling efficiency in cloud computing systems further. © 2024 ieee.

关键词： Cloud platforms

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Edge-computing Enabled real-time Respiratory Monitoring and Breathing Pattern Detection 20

Edge-Computing Enabled Real-Time Respiratory Monitoring and ...

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20th international conference on Body Sensor Networks

作者： Utsha, Ucchwas Talukder Morshed, Bashir I. Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

ISBN: (纸本)9798331530143

Detecting respiratory disorders can be improved with real-time diagnostic solutions, where monitoring respiration rates and patterns can act as early indicators for various cardiorespiratory diseases. We present a cost-effective edge-computing method utilizing wearable technology for respiratory disorder detection. Our system employs a wearable device with an IMU sensor for real-time signal transmission to a custom smartphone app which enables thorough visualization of respiratory signals, continuous monitoring of respiration rates, and rapid alarms for breathing anomalies, alongside ECG functionalities. A novel approach is introduced for respiratory pattern detection that includes a pre-trained AI model for apnea detection and classification of normal, bradypnea, and tachypnea patterns from non-apnea signals. During model building using the Apnea-ECG dataset, the proposed hyper-feature algorithm for apnea detection demonstrates excellent performance, achieving accuracies of 92.33%, 94.89%, and 97.66% for Chest, Abdominal, and Nasal respiration signals, respectively. With an average inference time of 5 ms for respiratory event detection, the classifier achieves outstanding accuracy rates of 91.24%, 92.42%, and 93.26% for these signals on the validation dataset after being implemented and tested at the edge device. real-time data analysis from 10 subjects further underscores the system's potential for continuous respiratory and cardiac monitoring in real-world scenarios.

关键词： Apnea Cardiorespiratory Pre-trained model Respiratory pattern Smartphone application

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NedgeSIM-RL: Simulating Smart Offloading in NOMA-VEC Environments 20

NedgeSIM-RL: Simulating Smart Offloading in NOMA-VEC Environ...

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2024 international conference on Wireless and Mobile computing Networking and Communications

作者： Dulout, Romain Ariouich, Yassine Mendiboure, Leo Pousset, Yannis Deniau, Virginie Univ Poitiers XLIM UMR CNRS 7252 F-86000 Poitiers France Univ Gustave Eiffel COSYS ERENA F-77454 Marne La Vallee France Univ Gustave Eiffel COSYS LEOST F-59666 Villeneuve Dascq France

ISBN: (纸本)9798350387452;9798350387445

The advent of cooperative intelligent transport systems (C-ITS) and connected vehicles has brought with it a raft of novel services, including autonomous driving and dynamic traffic management, which necessitate both real-time processing and substantial data processing capacity. These technological advancements have imposed significant network constraints, requiring the development of robust network infrastructures to guarantee the reliable transmission of critical information in real time. In consequence, numerous studies have concentrated on NOMA-assisted Multi-Access Edge computing (NOMA-MEC) to facilitate the provision of new services and, in particular, on their management through the utilisation of Multi-Agent Deep Reinforcement Learning (MADRL) algorithms. Nevertheless, the absence of a unified framework for the rigorous evaluation and comparison of these different approaches represents a significant issue. For this reason, we have established a unified, adaptable, and extensible framework, NedgeSIM-RL, with the objective of evaluating decentralised offloading strategies within the context of NOMA-VEC architectures. In this paper, we illustrate the functionality of the framework, the potential configurations, and the areas of research that can be pursued. We also present a basic simulation that can be executed on the framework to manage decentralised offloading via NOMA on a single MEC server by multiple vehicles.

关键词： Vehicular Edge computing NOMA Offloading Reinforcement Learning Simulation Evaluation

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