Proceedings of the International Conference on Internet-of-Things Design and Implementation 2021
DOI: 10.1145/3450268.3453522
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MLIoT

Abstract: Modern Internet of Things (IoT) applications, from contextual sensing to voice assistants, rely on ML-based training and serving systems using pre-trained models to render predictions. However, real-world IoT environments are diverse, with rich IoT sensors and need ML models to be personalized for each setting using relatively less training data. Most existing general-purpose ML systems are optimized for specific and dedicated hardware resources and do not adapt to changing resources and different IoT applicat… Show more

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Cited by 8 publications
(1 citation statement)
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“…The IoT surroundings rich with sensor devices that are interconnected have also yielded the demand for the more efficient monitoring of sensor activities and events [24]. To support diverse IoT use-case scenarios, Machine Learning has emerged as an essential area of scientific study and employment to enable computers to automatically progress through experience [25].…”
Section: Machine Learning Classifiers For Tag Estimationmentioning
confidence: 99%
“…The IoT surroundings rich with sensor devices that are interconnected have also yielded the demand for the more efficient monitoring of sensor activities and events [24]. To support diverse IoT use-case scenarios, Machine Learning has emerged as an essential area of scientific study and employment to enable computers to automatically progress through experience [25].…”
Section: Machine Learning Classifiers For Tag Estimationmentioning
confidence: 99%