Machine learning-based RSSI prediction in factory environments

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

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

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of 2019 25th Asia-Pacific Conference on Communications, APCC 2019
EditorsVo Nguyen Quoc Bao, Tran Thien Thanh
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages195-200
Number of pages6
ISBN (Electronic)9781728136790
DOIs
Publication statusPublished - Nov 2019
Event25th Asia-Pacific Conference on Communications, APCC 2019 - Ho Chi Minh City, Viet Nam
Duration: Nov 6 2019Nov 8 2019

Publication series

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

Conference

Conference25th Asia-Pacific Conference on Communications, APCC 2019
CountryViet Nam
CityHo Chi Minh City
Period11/6/1911/8/19

Keywords

  • Anomaly detection
  • Channel prediction
  • Factory environment
  • Machine-learning
  • Neural-network
  • RSSI measurements

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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  • Cite this

    Webber, J., Suga, N., Ano, S., Hou, Y., Mehbodniya, A., Higashimori, T., Yano, K., & Suzuki, Y. (2019). Machine learning-based RSSI prediction in factory environments. In V. N. Q. Bao, & T. T. Thanh (Eds.), Proceedings of 2019 25th Asia-Pacific Conference on Communications, APCC 2019 (pp. 195-200). [9026476] (Proceedings of 2019 25th Asia-Pacific Conference on Communications, APCC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APCC47188.2019.9026476