Processing assignment of deep learning according to sensor node capacity

Karin Umeda, Takashi Nishitsuji, Takuya Asaka, Takumi Miyoshi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages67-70
Number of pages4
ISBN (Electronic)9781728152684
DOIs
Publication statusPublished - 2019 Nov
Event7th International Symposium on Computing and Networking Workshops, CANDARW 2019 - Nagasaki, Japan
Duration: 2019 Nov 262019 Nov 29

Publication series

NameProceedings - 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

    Fingerprint

Keywords

  • Deep Learning
  • Distributed Processing
  • Wireless Sensor Network

ASJC Scopus subject areas

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

Cite this

Umeda, K., Nishitsuji, T., Asaka, T., & Miyoshi, T. (2019). Processing assignment of deep learning according to sensor node capacity. In Proceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019 (pp. 67-70). [8951731] (Proceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CANDARW.2019.00020