The Cloudlet Accelerator: Bringing Mobile-Cloud Face Recognition into Real-Time

Powers, Nathaniel; Alling, Alexander; Osolinsky, Kiara; Soyata, Tolga; Zhu, Meng; Wang, Dan; Ba, He; Heinzelman, Wendi; Shi, Jiye; Kwon, Minseok

doi:10.1109/glocomw.2015.7414055

Cited by 27 publications

(8 citation statements)

References 23 publications

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“…Several prior works, consider the Edge-Cloud tradeoffs to decide where to place highly constrained applications and satisfy their requirements without compromising power and availability [4], [5], [6], [23], [24], [25], [26], [27], [28]. It is also worth highlighting that existing studies of Edge deployments relied on measurements obtained from either too simple devices (e.g.…”

Section: Edge Computingmentioning

confidence: 99%

“…In these studies, data are transferred from IoT sensors to local micro-datacenters for pre-processing and selection which of the data to forward to a centralized datacenter. Examples of IoT applications with a tight response time and QoS constraints include face recognition [27], [28], traffic counting and video processing applications [57], as well as, applications for detecting jamming attacks of wireless networks [7], [41]. All these applications consist of sensors that collect and send data to a processing device.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the Evaluation of the Total-Cost-of-Ownership Trade-Offs in Edge vs Cloud Deployments: A Wireless-Denial-of-Service Case Study

Nikolaou

Sazeides

Lampropoulos

et al. 2022

IEEE Trans. Sustain. Comput.

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Section: Edge Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Evaluation of the Total-Cost-of-Ownership Trade-Offs in Edge vs Cloud Deployments: A Wireless-Denial-of-Service Case Study

Nikolaou

Sazeides

Lampropoulos

et al. 2022

IEEE Trans. Sustain. Comput.

View full text Add to dashboard Cite

“…For instance, real-time video analysis, a likely killer app for edge computing [132], [133], has the potential to save vast amounts of bandwidth by only forwarding results of the analysis, e.g., the number of objects in the frame, instead of entire video streams. To give a concrete example, Powers et al [134] use cloudlets to pre-process data for a face recognition application. Both of these aspects can save bandwidth, depending on the ratio of data discarded and how much data is reduced by the pre-processing.…”

Section: Edge Computing Applicationsmentioning

confidence: 99%

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

et al. 2019

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Edge computing brings computing and storage resources closer to (mobile) end users and data sources, thus bypassing expensive and slow links to distant cloud computing infrastructures. Often leveraged opportunistically, these heterogeneous resources can be used to offload data and computations, enabling upcoming demanding applications such as augmented reality and autonomous driving. Research in this direction has addressed various challenges, from architectural concerns to runtime optimizations. As of today, however, we lack a widespread availability of edge computing-partly because it remains unclear which of the promised benefits of edge computing are relevant for what types of applications. This article provides a comprehensive snapshot of the current edge computing landscape, with a focus on the application perspective. We outline the characteristics of edge computing and its postulated benefits and drawbacks. To understand the functional composition of applications, we first define common application components that are relevant w.r.t. edge computing. We then present a classification of proposed use cases and analyze them according to their expected benefits from edge computing and which components they use. Furthermore, we illustrate existing products and industry solutions that have recently surfaced and outline future research challenges. INDEX TERMS Edge computing, heterogeneous networks, next generation networking mobile applications, Internet of Things, ubiquitous computing.

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“…With the face recognition system gradually being widely used and the rapid emerging of the edge computing, the related work on the optimization of the face recognition system is also ongoing. In Powers et al [Powers, Alling, Osolinsky et al (2015)], a mobilecloudlet-cloud architecture was proposed to perform real-time face recognition by using cloudlet to pre-process data and reduce communication time at the expense of computation. Wang et al [Wang, Xiong, Pei et al (2018)] introduces a method to protect the visual privacy by hiding the identity information of the face images.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Face Recognition System Based on Azure IoT Edg

Shen¹,

Liu²,

Liang³

et al. 2019

Computers, Materials &Amp; Continua

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With the rapid development of artificial intelligence, face recognition systems are widely used in daily lives. Face recognition applications often need to process large amounts of image data. Maintaining the accuracy and low latency is critical to face recognition systems. After analyzing the two-tier architecture "client-cloud" face recognition systems, it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded, and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network. This paper proposes a flexible and efficient edge computing accelerated architecture. By offloading part of the computing tasks to the edge server closer to the data source, edge computing resources are used for image preprocessing to reduce the number of images to be transmitted, thus reducing the network transmission overhead. Moreover, the application code does not need to be rewritten and can be easily migrated to the edge server. We evaluate our schemes based on the open source Azure IoT Edge, and the experimental results show that the three-tier architecture "Client-Edge-Cloud" face recognition system outperforms the state-of-art face recognition systems in reducing the average response time.

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The Cloudlet Accelerator: Bringing Mobile-Cloud Face Recognition into Real-Time

Cited by 27 publications

References 23 publications

On the Evaluation of the Total-Cost-of-Ownership Trade-Offs in Edge vs Cloud Deployments: A Wireless-Denial-of-Service Case Study

On the Evaluation of the Total-Cost-of-Ownership Trade-Offs in Edge vs Cloud Deployments: A Wireless-Denial-of-Service Case Study

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

Optimization of Face Recognition System Based on Azure IoT Edg

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