A Deep Learning Based Distributed Smart Surveillance Architecture using Edge and Cloud Computing

Kaskavalci, Halil Can; Gören, Sezer

doi:10.1109/deep-ml.2019.00009

Cited by 30 publications

(17 citation statements)

References 9 publications

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“…Model packaging comprises adding of model(s) into executable or loadable file(s) (e.g. adding models into Docker images [45]). Serving was added to 'Data transformation and serving'-FA based on an another prototype, where a web proxy served end users with augmented streaming video [32].…”

Section: Discussionmentioning

confidence: 99%

“…A few reviewed prototypes included sensor-based devices, which were located between the primary edge-device, and the final point of use. In those cases [45,46], architectural elements were identified in on-site computing devices.…”

Section: Discussionmentioning

confidence: 99%

“…When the location of architectural elements is analysed in detail, it can be seen that Data transformation and Serving functionality was executed in the edge server (transformation and serving of end users with augmented video streaming content [32]). Models were trained mainly in private/public clouds [36,37,39] or in edge devices [35,46] while the results were inferred from models in all environments [27,28,30,31,45]. Also, resource demanding Deep analytics and Machine learning, and Job scheduling tasks were executed mostly in private/public clouds, and/or edge environments.…”

Section: Discussionmentioning

confidence: 99%

“…Data processing functionality was executed either in end user devices [29,30], in edge servers [40,41], or in the public cloud [45,46]. Data loading and pre-processing was performed in end user devices [29,34] or in on-site devices [45]. Data was extracted either from end-user devices [29], or from the edge environment [30,40].…”

Section: Discussionmentioning

confidence: 99%

“…3)] may be needed in the synchronization process. After training, the models are packaged [45] and scheduled for distribution. Particularly, it has to be determined how to partition DNNs to be executed on edge/mobile devices to achieve optimal performance in terms of latency.…”

Section: Discussionmentioning

confidence: 99%

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Extending reference architecture of big data systems towards machine learning in edge computing environments

Pääkkönen

Pakkala

2020

J Big Data

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IntroductionMany big data systems have been developed and realised to provide end user services (Netflix, Facebook, Twitter, LinkedIn etc.). Also, underlying architectures and technologies of the enabling systems have been published [1-3], and RAs have been designed and proposed [4][5][6]. Edge/5G computing is an emerging technological field [7], and the first products are being shipped to the markets. However, the utilisation of machine learning (ML) as part of the edge computing infrastructure is still an area for further research [8]. Particularly, it should be understood, how data is collected, and how models are Abstract Background: Augmented reality, computer vision and other (e.g. network functions, Internet-of-Things (IoT)) use cases can be realised in edge computing environments with machine learning (ML) techniques. For realisation of the use cases, it has to be understood how data is collected, stored, processed, analysed, and visualised in big data systems. In order to provide services with low latency for end users, often utilisation of ML techniques has to be optimized. Also, software/service developers have to understand, how to develop and deploy ML models in edge computing environments. Therefore, architecture design of big data systems to edge computing environments may be challenging. Findings:The contribution of this paper is reference architecture (RA) design of a big data system utilising ML techniques in edge computing environments. An earlier version of the RA has been extended based on 16 realised implementation architectures, which have been developed to edge/distributed computing environments. Also, deployment of architectural elements in different environments is described. Finally, a system view is provided of the software engineering aspects of ML model development and deployment. Conclusions:The presented RA may facilitate concrete architecture design of use cases in edge computing environments. The value of RAs is reduction of development and maintenance costs of systems, reduction of risks, and facilitation of communication between different stakeholders.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%