Towards a Dynamic Composability Approach for using Heterogeneous Systems in Remote Sensing

Altıntaş, Ilkay; Perez, Ismael; Mishin, Dmitry; Trouillaud, Adrien; Irving, Christopher; Graham, John H.; Tatineni, Mahidhar; DeFanti, Thomas A.; Strande, Shawn; Smarr, Larry; Norman, Michael L.

doi:10.1109/escience55777.2022.00047

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Year Application Area Description EI Level [75] 2023 Federated learning Dynamic FL deployment and learning scheme 4 [76] 2021 Robotics Design methodology for ROS-based applications 4 [77] 2021 Healthcare Readmission prediction system for healthcare facilities 3 [78] 2022 Healthcare Electronic health records decomposing the patient's body into containers - [79] 2021 Virtual assistant Voice control for human-machine interaction 3 [81] 2022 Composite AI Ontology model for development of multi-agent AI systems - [82] 2018 Healthcare Human activity recognition 3 [83] 2021 Security Re-identification of people across multiple cameras 2 [84] 2022 Wildfire modelling A federation architecture to enable a composable infrastructure - [85] 2019 Computer vision Architecture for image processing 3…”

Section: Referencementioning

confidence: 99%

Containerization in Edge Intelligence: A Review

Urblik,

Kajati,

Papcun

et al. 2024

Electronics

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The onset of cloud computing brought with it an adoption of containerization—a lightweight form of virtualization, which provides an easy way of developing and deploying solutions across multiple environments and platforms. This paper describes the current use of containers and complementary technologies in software development and the benefits it brings. Certain applications run into obstacles when deployed on the cloud due to the latency it introduces or the amount of data that needs to be processed. These issues are addressed by edge intelligence. This paper describes edge intelligence, the deployment of artificial intelligence close to the data source, the opportunities it brings, along with some examples of practical applications. We also discuss some of the challenges in the development and deployment of edge intelligence solutions and the possible benefits of applying containerization in edge intelligence.

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