Machine learning-based RSSI prediction in factory environments

Julian Webber, Norisato Suga, Susumu Ano, Yafei Hou, Abolfazl Mehbodniya, Toshihide Higashimori, Kazuto Yano, Yoshinori Suzuki

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

This paper studies the prediction of the received signal strength at a receiver that tracks an automated guided vehicle (AGV) as it moves along a factory route. We apply machine learning to predict a sliding-window pattern of the received signal strength indication (RSSI) signal and further improve the prediction performance by using multiple receivers. The performance evaluation processes wireless data collected from actual received signal strength measurement experiments recorded from an OFDM transmitter in the 2.4 GHz band. The performance is evaluated for vehicle movement along routes with both repetitive and random sections and with and without position errors.

本文言語English
ホスト出版物のタイトルProceedings of 2019 25th Asia-Pacific Conference on Communications, APCC 2019
編集者Vo Nguyen Quoc Bao, Tran Thien Thanh
出版社Institute of Electrical and Electronics Engineers Inc.
ページ195-200
ページ数6
ISBN(電子版)9781728136790
DOI
出版ステータスPublished - 2019 11月
外部発表はい
イベント25th Asia-Pacific Conference on Communications, APCC 2019 - Ho Chi Minh City, Viet Nam
継続期間: 2019 11月 62019 11月 8

出版物シリーズ

名前Proceedings of 2019 25th Asia-Pacific Conference on Communications, APCC 2019

Conference

Conference25th Asia-Pacific Conference on Communications, APCC 2019
国/地域Viet Nam
CityHo Chi Minh City
Period19/11/619/11/8

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • ハードウェアとアーキテクチャ
  • 情報システムおよび情報管理
  • 安全性、リスク、信頼性、品質管理
  • 制御と最適化

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