Serverless Microservice Architecture for Cloud-Edge Intelligence in Sensor Networks
IEEE Sensors Journal, ISSN: 1558-1748, Page: 1-1
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Article Description
Machine Learning (ML) is increasingly exploited in a wide range of application areas to analyze data streams from large-scale sensor networks, train predictive models and perform inference. The Cloud-Edge Intelligence (CEI) computing paradigm integrates cloud infrastructures for resource-intensive ML tasks with devices at the border of a local network for distributed data preprocessing, small-scale model training and prediction tasks. This can achieve a tunable trade-off of ML accuracy with improved data privacy, response latency, and bandwidth usage. Prevalent CEI architectures are based on microservices encapsulated in containers, but serverless computing is emerging as an alternative model. It is based on stateless event-driven functions to facilitate development and provisioning of application components, increase infrastructure elasticity and reduce management effort. This paper proposes a novel CEI framework for sensor-based applications, exploiting serverless computing for data management and ML tasks. Small-scale model training occurs at the edge with local data for quick prediction response, while large-scale models are trained in the cloud with the full sensor network data and then they are fed back to edge nodes for a progressive accuracy improvement. A fully functional prototype has been built by leveraging open source software tools, selected devices for field sensing and edge computing, and a commercial cloud platform. Experiments validate the feasibility and sustainability of the proposal, compared to an existing container-oriented microservice architecture.
Bibliographic Details
Institute of Electrical and Electronics Engineers (IEEE)
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