Edge optimized and personalized lifelogging framework using ensembled metaheuristic algorithms

•A four-layer edge optimized and user-personalized framework for life-logging human activities is proposed.•A lightweight edge intelligence module requiring low computation is designed, which reduces data transmission to the cloud.•A novel Max Score Pooling (MSP) algorithm based on ensembled metaheu...

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Vydané v:	Computers & electrical engineering Ročník 100; s. 107884
Hlavní autori:	Agarwal, Preeti, Alam, Mansaf
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier Ltd 01.05.2022 Elsevier BV
Predmet:	Activity recognition Algorithms Big Data Analytics (BDA) Classifiers Cloud Computing (CC) Communications traffic Customization Data transmission Decision trees Edge Computing (EC) Edge intelligence Energy consumption Feature recognition Heuristic methods Human Activity Recognition (HAR) Internet of Things (IoT) Metaheuristic algorithms Internet of Things (IoT) Human Activity Recognition (HAR) Cloud Computing (CC) Big Data Analytics (BDA) Edge intelligence Metaheuristic algorithms Edge Computing (EC)
ISSN:	0045-7906, 1879-0755
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