Processing assignment of deep learning according to sensor node capacity

Karin Umeda, Takashi Nishitsuji, Takuya Asaka, Takumi Miyoshi

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

The use of sensor networks is expanding due to the spread of the Internet of Things (IoT). As this expansion continues, the amount of data to be acquired will increase and the communication bandwidth may become compressed. In addition, the processing of the retrieved data is currently performed by the server, and deep learning is often used when processing the data. This process is heavy, and the load on the server increases as the amount of data increases. In order to reduce the load on the server, conventional research has proposed a method of distributed processing using edge computing and parallel processing using mobile devices. However, although the data processing speed is fast with these methods, there are problems of increased communication traffic and increased power consumption. Therefore, in this study, we propose a method of assigning intermediate layers for deep learning according to the processing capacity of each sensor for the purpose of reducing the traffic and server load in the wireless sensor network.

本文言語English
ホスト出版物のタイトルProceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ67-70
ページ数4
ISBN(電子版)9781728152684
DOI
出版ステータスPublished - 2019 11
イベント7th International Symposium on Computing and Networking Workshops, CANDARW 2019 - Nagasaki, Japan
継続期間: 2019 11 262019 11 29

出版物シリーズ

名前Proceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019

Conference

Conference7th International Symposium on Computing and Networking Workshops, CANDARW 2019
CountryJapan
CityNagasaki
Period19/11/2619/11/29

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems
  • Artificial Intelligence
  • Computer Networks and Communications

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