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Edge-centric inferential modeling & analytics
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DOI：10.1016/j.jnca.2020.102696.png)
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Abstract
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This work contributes to a real-time, edge-centric inferential modeling and analytics methodology introducing the fundamental mechanisms for (i) predictive models update and (ii) diverse models selection in distributed computing. Our objective in edge-centric analytics is the time-optimized model caching and selective forwarding at the network edge adopting optimal stopping theory, where communication overhead is significantly reduced as only inferred knowledge and sufficient statistics are delivered instead of raw data obtaining high quality of analytics. Novel model selection algorithms are introduced to fuse the inherent models' diversity over distributed edge nodes to support inferential analytics tasks to end-users/analysts, and applications in real-time. We provide statistical learning modeling and establish the corresponding mathematical analyses of our mechanisms along with comprehensive performance and comparative assessment using real data from different domains and showing its benefits in edge computing.
Keywords:
Edge inferential analytics
Quality of analytics
Communication efficiency
Hetero-associative learning vector quantization
Optimal stopping theory
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