A Smart Service Rebuilding Scheme across Cloudlets via Mobile AR Frame Feature Mapping

Wang, Haoxin; Xie, Jiang

doi:10.1109/icc.2018.8422226

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…We make the following observations. (23) In contrary to the local execution, raising the CNN model size in the remote execution decreases the average CPU frequency, as shown in Figs. 17(a)-17(f).…”

Section: B the Impact Of Cnn Model Sizementioning

confidence: 99%

Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications

Wang

Kim

Xie

et al. 2021

IEEE Trans. on Green Commun. Netw.

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Applying deep learning to object detection provides the capability to accurately detect and classify complex objects in the real world. However, currently, few mobile applications use deep learning because such technology is computationintensive and energy-consuming. This paper, to the best of our knowledge, presents the first detailed experimental study of a mobile augmented reality (AR) client's energy consumption and the detection latency of executing Convolutional Neural Networks (CNN) based object detection, either locally on the smartphone or remotely on an edge server. In order to accurately measure the energy consumption on the smartphone and obtain the breakdown of energy consumed by each phase of the object detection processing pipeline, we propose a new measurement strategy. Our detailed measurements refine the energy analysis of mobile AR clients and reveal several interesting perspectives regarding the energy consumption of executing CNN-based object detection. Furthermore, several insights and research opportunities are proposed based on our experimental results. These findings from our experimental study will guide the design of energy-efficient processing pipeline of CNN-based object detection.

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Section: B the Impact Of Cnn Model Sizementioning

confidence: 99%

Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications

Wang

Kim

Xie

et al. 2021

IEEE Trans. on Green Commun. Netw.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [96], a different approach is taken to reduce service migration latency across cloudlets. The proposed approach considers the case of an AR application and therefore leverages the features extracted from the user's camera to predict the target cloudlet prior to the radio handoff.…”

Section: Proactive Actionsmentioning

confidence: 99%

“…This could not only enhance the user's service performance, but it is also likely to result in a more efficient usage of the network and the edge computing infrastructure. So far, only a few contributions, such as [66] and [96], have addressed these aspects in a joint manner. • Mobile user-mobile FN scenario: Even though the mobile user-mobile FN scenario is likely to occur frequently in the context of fog computing, especially with the advent of smart vehicles that can act as fog nodes, this scenario has not been explored much in the literature, as pointed out in Section 3.…”

Section: Gap Analysis and Research Opportunitiesmentioning

confidence: 99%

A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms

2019

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With the advent of fog and edge computing paradigms, computation capabilities have been moved toward the edge of the network to support the requirements of highly demanding services. To ensure that the quality of such services is still met in the event of users’ mobility, migrating services across different computing nodes becomes essential. Several studies have emerged recently to address service migration in different edge-centric research areas, including fog computing, multi-access edge computing (MEC), cloudlets, and vehicular clouds. Since existing surveys in this area focus on either VM migration in general or migration in a single research field (e.g., MEC), the objective of this survey is to bring together studies from different, yet related, edge-centric research fields while capturing the different facets they addressed. More specifically, we examine the diversity characterizing the landscape of migration scenarios at the edge, present an objective-driven taxonomy of the literature, and highlight contributions that rather focused on architectural design and implementation. Finally, we identify a list of gaps and research opportunities based on the observation of the current state of the literature. One such opportunity lies in joining efforts from both networking and computing research communities to facilitate future research in this area.

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Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles

Wang

Liu

Kim

et al. 2020

IEEE Commun. Surv. Tutorials

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107

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As vehicles playing an increasingly important role in people's daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have attracted widespread attentions from both academia and industry. These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications. Computation offloading techniques, such as cloud, edge, and fog computing, can help CVs process computationintensive and large-scale computing tasks. Additionally, different cloud/edge/fog computing architectures are suitable for supporting different types of CV applications with highly different QoS requirements, which demonstrates the importance of the computing architecture design. However, most of the existing surveys on cloud/edge/fog computing for CVs overlook the computing architecture design, where they (i) only focus on one specific computing architecture and (ii) lack discussions on benefits, research challenges, and system requirements of different architectural alternatives. In this paper, we provide a comprehensive survey on different architectural design alternatives based on cloud/edge/fog computing for CVs. The contributions of this paper are: (i) providing a comprehensive literature survey on existing proposed architectural design alternatives based on cloud/edge/fog computing for CVs, (ii) proposing a new classification of computing architectures based on cloud/edge/fog computing for CVs: computation-aided and computation-enabled architectures, (iii) presenting a holistic comparison among different cloud/edge/fog computing architectures for CVs based on functional requirements of CV systems, including advantages, disadvantages, and research challenges, (iv) presenting a holistic overview on the design of CV systems from both academia and industry perspectives, including activities in industry, functional requirements, service requirements, and design considerations, and (v) proposing several open research issues of designing cloud/edge/fog computing architectures for CVs.

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A Smart Service Rebuilding Scheme across Cloudlets via Mobile AR Frame Feature Mapping

Cited by 12 publications

References 8 publications

Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications

Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications

A Survey on Mobility-Induced Service Migration in the Fog, Edge, and Related Computing Paradigms

Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles

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