Abstract
Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Therefore, in this article, we first introduce deep learning for IoTs into the edge computing environment. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. In the performance evaluation, we test the performance of executing multiple deep learning tasks in an edge computing environment with our strategy. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT.
Keywords
Computer scienceEdge computingDeep learningEnhanced Data Rates for GSM EvolutionInternet of ThingsArtificial intelligenceEdge deviceDistributed computingMachine learningComputer architectureCloud computingEmbedded systemOperating system
Affiliated Institutions
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Publication Info
- Year
- 2018
- Type
- article
- Volume
- 32
- Issue
- 1
- Pages
- 96-101
- Citations
- 1522
- Access
- Closed
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Cite This
He Li,
Kaoru Ota,
Mianxiong Dong
(2018).
Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing.
IEEE Network
, 32
(1)
, 96-101.
https://doi.org/10.1109/mnet.2018.1700202
Identifiers
- DOI
- 10.1109/mnet.2018.1700202