@article{Pahl2019b,
title = {Machine-learning based IoT data caching},
author = {Marc Oliver Pahl and Stefan Liebald and Lars Wustrich},
isbn = {9783903176157},
year = {2019},
date = {2019-01-01},
journal = {2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019},
pages = {9--12},
abstract = {he Internet of Things (IoT) continuously produces big amounts of data. Data-centric middleware can therefore help reducing the complexity when orchestrating distributed Things. With its heterogeneity and resource limitations, IoT applications can lack performance, scalability, or resilience. Caching can help overcoming the limitations. We are currently working on establishing data caching within IoT middleware. The paper presents fundamentals of caching, major challenges, relevant state of the art, and a description of our current approaches. We show directions of using machine learning for caching in the IoT.},
keywords = {Caching, Data-centric, Internet of Things, Machine learning},
pubstate = {published},
tppubtype = {article}
}
he Internet of Things (IoT) continuously produces big amounts of data. Data-centric middleware can therefore help reducing the complexity when orchestrating distributed Things. With its heterogeneity and resource limitations, IoT applications can lack performance, scalability, or resilience. Caching can help overcoming the limitations. We are currently working on establishing data caching within IoT middleware. The paper presents fundamentals of caching, major challenges, relevant state of the art, and a description of our current approaches. We show directions of using machine learning for caching in the IoT.