Computation Offloading Toward Edge Computing

Li, Lin; Liao, Xiaofei; Jin, Hai; Li, Peng

doi:10.1109/jproc.2019.2922285

Cited by 346 publications

(142 citation statements)

References 169 publications

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“…We believe that this step is necessary, in order to efficiently set up a comprehensive reference system architecture. However, we also expect that part of this implementation will smoothly pass towards the edge, exploiting the well-known benefits of edge processing [9], and we hope that our experience may be useful to define effective specifications and support development in an automotive embedded environment.…”

Section: Introductionmentioning

confidence: 94%

The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot

Hiller

Koskinen²,

Berta

et al. 2020

Electronics

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As industrial research in automated driving is rapidly advancing, it is of paramount importance to analyze field data from extensive road tests. This paper investigates the design and development of a toolchain to process and manage experimental data to answer a set of research questions about the evaluation of automated driving functions at various levels, from technical system functioning to overall impact assessment. We have faced this challenge in L3Pilot, the first comprehensive test of automated driving functions (ADFs) on public roads in Europe. L3Pilot is testing ADFs in vehicles made by 13 companies. The tested functions are mainly of Society of Automotive Engineers (SAE) automation level 3, some of them of level 4. In this context, the presented toolchain supports various confidentiality levels, and allows cross-vehicle owner seamless data management, with the efficient storage of data and their iterative processing with a variety of analysis and evaluation tools. Most of the toolchain modules have been developed to a prototype version in a desktop/cloud environment, exploiting state-of-the-art technology. This has allowed us to efficiently set up what could become a comprehensive edge-to-cloud reference architecture for managing data in automated vehicle tests. The project has been released as open source, the data format into which all vehicular signals, recorded in proprietary formats, were converted, in order to support efficient processing through multiple tools, scalability and data quality checking. We expect that this format should enhance research on automated driving testing, as it provides a shared framework for dealing with data from collection to analysis. We are confident that this format, and the information provided in this article, can represent a reference for the design of future architectures to implement in vehicles.

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Section: Introductionmentioning

confidence: 94%

The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot

Hiller

Koskinen²,

Berta

et al. 2020

Electronics

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show abstract

“…Lin et al [35] analyzed computation offloading for edge computing. An insight into the architecture and types of edge computing nodes was given.…”

Section: A Existing Surveys and Tutorialsmentioning

confidence: 99%

“…[34] 2018 × √ × Ref. [35] 2019 √ × × Ref. [36] 2019 √ Our survey √ √ √ complete comprehensive outlook of all aspects of computation offloading.…”

Section: B Contributionsmentioning

confidence: 99%

“…Warnings about floods, accidents, and emergency vehicles passing by can be passed quickly and to a large number of vehicles, especially when the network can not handle the sending of this information by other mediums. Also, there is a possibility of commercial exploitation of this type of application, with tourist information, blockchain [35], [222], [223], [224], [225], [226], business suggestions, and events nearby [227]. In general, this type of character-oriented applications or other data types has smaller files, depending on the application, than other types of files [209].…”

Section: D: Othermentioning

confidence: 99%

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Computation Offloading for Vehicular Environments: A Survey

et al. 2020

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With significant advances in communication and computing, modern day vehicles are becoming increasingly intelligent. This gives them the ability to contribute to safer roads and passenger comfort through network devices, cameras, sensors, and computational storage and processing capabilities. However, to run new and popular applications, and to enable vehicles operating autonomously requires massive computational resources. Computational resources available with the current day vehicles are not sufficient to process all these demands. In this situation, other vehicles, edge servers, and servers in remote data centers can help the vehicles by lending their computing resources. However, to take advantage of these computing resources, computation offloading techniques have to be leveraged to transfer tasks or entire applications to run on other devices. Such computation offloading can lead to improved performance and Quality of Service (QoS) for applications and for the network. However, computation offloading in a highly dynamic environment such as vehicular networks is a major challenge. Therefore, this survey aims to review and organize the computation offloading literature in vehicular environments. In addition, we demystify some concepts, propose a taxonomy with the most important aspects and classify most works in the area according to each category. We also present the main tools, scenarios, subjects, strategies, objectives, etc., used in the works. Finally, we present the main challenges and future directions to guide future research in this active research area.

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“…The implementation of deep learning in edge devices is challenging. Since the computation of deep learning requires high computing performance [10], edge computing gives many researchers another chance to run the deep learning model in a limited resource such as the edge device. The complexity of the model will increase the memory usage and execution time, which in turn increases power consumption.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Image Search System on Off-Line Situation

et al. 2020

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Implementation of deep learning in low-cost hardware, such as an edge device, is challenging. Reducing the complexity of the network is one of the solutions to reduce resource usage in the system, which is needed by low-cost system implementation. In this study, we use the general average pooling layer to replace the fully connected layers on the convolutional neural network (CNN) model, used in the previous study, to reduce the number of network properties without decreasing the model performance in developing image classification for image search tasks. We apply the cosine similarity to measure the characteristic similarity between the feature vector of image input and extracting feature vectors from testing images in the database. The result of the cosine similarity calculation will show the image as the result of the searching image task. In the implementation, we use Raspberry Pi 3 as a low-cost hardware and CIFAR-10 dataset for training and testing images. Base on the development and implementation, the accuracy of the model is 68%, and the system generates the result of the image search base on the characteristic similarity of the images.

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Computation Offloading Toward Edge Computing

Cited by 346 publications

References 169 publications

The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot

The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot

Computation Offloading for Vehicular Environments: A Survey

Low-Cost Image Search System on Off-Line Situation

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