A new Edge Architecture for AI-IoT services deployment

Debauche, Olivier; Mahmoudi, Saïd; Mahmoudi, Sidi Ahmed; Manneback, Pierre; Lebeau, Frédéric

doi:10.1016/j.procs.2020.07.006

Cited by 73 publications

(45 citation statements)

References 24 publications

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“…Therefore, the proposed algorithm will considerably increase the management of converged heterogeneous networks and reduce resource management errors, reducing resource management costs and improving the reliability with which they are implemented. Debauche, Olivier, et al [53] defined a new Edge artificial intelligence of thing architecture through a partnership deployed in the Cloud with microservices. Besides the use or deployment of advanced AI algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT and Cloud Computing Issues, Challenges and Opportunities: A Review

Sadeeq

Abdulkareem

Zeebaree

et al. 2021

QAJ

248

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With the exponential growth of the Industrial Internet of Things (IIoT), multiple outlets are constantly producing a vast volume of data. It is unwise to locally store all the raw data in the IIoT devices since the energy and storage spaces of the end devices are strictly constrained. self-organization and short-range Internet of Things (IoT) networking also support outsourced data and cloud computing, independent of the distinctive resource constraint properties. For the remainder of the findings, there is a sequence of unfamiliar safeguards for IoT and cloud integration problems. The delivery of cloud computing is highly efficient, storage is becoming more and more current, and some groups are now altering their data from in house records Cloud Computing Vendors' hubs. Intensive IoT applications for workloads and data are subject to challenges while utilizing cloud computing tools. In this report, we research IoT and cloud computing and address cloud-compatible problems and computing techniques to promote the stable transition of IoT programs to the cloud.

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Section: Literature Reviewmentioning

confidence: 99%

IoT and Cloud Computing Issues, Challenges and Opportunities: A Review

Sadeeq

Abdulkareem

Zeebaree

et al. 2021

QAJ

248

View full text Add to dashboard Cite

show abstract

“…They further proposed an architecture that preserves the advantages of both edge processing and server-based/cloud-centric computing for AI algorithms. Debauche et al [29] proposed a new archi-tecture used to deploy at edge level microservices and adapted artificial intelligence algorithms and models. Li et al [30] introduced deep learning for IoT into the edge computing environment.…”

Section: Applying Ai To Iotmentioning

confidence: 99%

An edge-cloud collaborative computing platform for building AIoT applications efficiently

Rong

Tong³

et al. 2021

J Cloud Comp

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The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), or AIoT, has breathed a new life into IoT operations and human-machine interactions. Currently, resource-constrained IoT devices usually cannot provide sufficient capability for data storage and processing so as to support building modern AI models. An intuitive solution is to integrate cloud computing technology into AIoT and exploit the powerful and elastic computing as well as the storage capacity of the servers on the cloud end. Nevertheless, the network bandwidth and communication latency increasingly become serious bottlenecks. The emerging edge computing can complement the cloud-based AIoT in terms of communication latency, and hence attracts more and more attention from the AIoT area. In this paper, we present an industrial edge-cloud collaborative computing platform, namely Sophon Edge, that helps to build and deploy AIoT applications efficiently. As an enterprise-level solution for the AIoT computing paradigm, Sophon Edge adopts a pipeline-based computing model for streaming data from IoT devices. Besides, this platform supports an iterative way for model evolution and updating so as to enable the AIoT applications agile and data-driven. Through a real-world example, we demonstrate the effectiveness and efficiency of building an AIoT application based on the Sophon Edge platform.

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“…They further proposed an architecture which preserves the advantages of both edge processing and server-based/cloudcentric computing for AI algorithms. Debauche et al [31] proposed a new architecture used to deploy at edge level micro services and adapted artificial intelligence algorithms and models. Li et al [32] introduced deep learning for IoT into the edge computing environment.…”

Section: Edge Computing For Iotmentioning

confidence: 99%

An edge-cloud collaborative computing platform for building AIoT applications efficiently

Rong

Tong³

et al. 2021

Preprint

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The convergence of the Artificial Intelligence (AI) and the Internet of Things (IoT), i.e. the Artificial Intelligence of Things (AIoT), is a very promising technology that redefines the way people interact with the surrounding devices. Practical AIoT applications not only have high demands on computing and storage resources, but also desire for high responsiveness. Traditional cloud-based computing paradigm faces the great pressure on the network bandwidth and communication latency, hence the newly emerged edge computing paradigm gets involved. Consequently, AIoT applications can be implemented in an edge-cloud collaborative manner, where the model building and model inferencing are offloaded to the cloud and the edge, respectively. However, developers still face challenges building AIoT applications in practice due to the inherent heterogeneity of the IoT devices, the declining accuracy of once trained models, the security and privacy issues, etc. In this paper, we present the design of an industrial edge-cloud collaborative computing platform that aims to facilitate building AIoT applications in practice. Furthermore, a real-world use case is presented in this paper, which proved the efficiency of building an AIoT application on the platform.

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A new Edge Architecture for AI-IoT services deployment

Cited by 73 publications

References 24 publications

IoT and Cloud Computing Issues, Challenges and Opportunities: A Review

IoT and Cloud Computing Issues, Challenges and Opportunities: A Review

An edge-cloud collaborative computing platform for building AIoT applications efficiently

An edge-cloud collaborative computing platform for building AIoT applications efficiently

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