research-article

Learning-based indoor localization for industrial applications

Authors:

Gunes Karabulut Kurt,

Guido DartmannAuthors Info & Claims

CF '18: Proceedings of the 15th ACM International Conference on Computing Frontiers

Pages 355 - 362

https://doi.org/10.1145/3203217.3203227

Published: 08 May 2018 Publication History

Abstract

Modern process automation and the industrial evolution heading towards Industry 4.0 require a huge variety of information to be fused in a Cyber-Physical System. Important for many applications is the spatial position of an arbitrary object given directly or indirectly in terms of data that has to be processed to obtain position information. Starting point for the idea of the technical reflection-based sound localization system presented in this paper is the biological role model of humans being able to learn how to localize sound sources. Compared to other forms of sound localization, this nature-inspired method has no need for high spatial and temporal accuracy or big microphone arrays. Possible applications for this system are indoor robot localization or object tracking.

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Cited By

Khalil RSaeed NAlmutiry MAlenezi A(2023)Energy-efficient anchor activation protocol for non-cooperative localization of Industrial Internet of Things (IIoT)ICT Express10.1016/j.icte.2023.01.0049:5(815-820)Online publication date: Oct-2023
https://doi.org/10.1016/j.icte.2023.01.004
DÖNMEZ EOKUMUŞ FKOCAMAZ F(2021)ADAPTIVE OPERATION MODEL FOR INTERIOR SMART LOGISTICS IN CYBER PHYSICAL SYSTEMSSiber Fiziksel Sistemlerde İç-Mekân Akıllı Lojistik için Adaptif İşletim ModeliKonya Journal of Engineering Sciences10.36306/konjes.8335579:4(965-980)Online publication date: 4-Dec-2021
https://doi.org/10.36306/konjes.833557
Woschank MRauch EZsifkovits H(2020)A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart LogisticsSustainability10.3390/su1209376012:9(3760)Online publication date: 6-May-2020
https://doi.org/10.3390/su12093760
Show More Cited By

Learning-based indoor localization for industrial applications
1. Computing methodologies

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cover image ACM Conferences

CF '18: Proceedings of the 15th ACM International Conference on Computing Frontiers

May 2018

401 pages

ISBN:9781450357616

DOI:10.1145/3203217

General Chairs:
David Kaeli
Northeastern University
,
Miquel Pericàs
Chalmers University of Technology, SE

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing

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Association for Computing Machinery

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Published: 08 May 2018

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federal ministry of education and science (BMBF)

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CF '18

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CF '18: Computing Frontiers Conference

May 8 - 10, 2018

Ischia, Italy

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Overall Acceptance Rate 273 of 785 submissions, 35%

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May 28 - 30, 2025

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Cited By

Khalil RSaeed NAlmutiry MAlenezi A(2023)Energy-efficient anchor activation protocol for non-cooperative localization of Industrial Internet of Things (IIoT)ICT Express10.1016/j.icte.2023.01.0049:5(815-820)Online publication date: Oct-2023
https://doi.org/10.1016/j.icte.2023.01.004
DÖNMEZ EOKUMUŞ FKOCAMAZ F(2021)ADAPTIVE OPERATION MODEL FOR INTERIOR SMART LOGISTICS IN CYBER PHYSICAL SYSTEMSSiber Fiziksel Sistemlerde İç-Mekân Akıllı Lojistik için Adaptif İşletim ModeliKonya Journal of Engineering Sciences10.36306/konjes.8335579:4(965-980)Online publication date: 4-Dec-2021
https://doi.org/10.36306/konjes.833557
Woschank MRauch EZsifkovits H(2020)A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart LogisticsSustainability10.3390/su1209376012:9(3760)Online publication date: 6-May-2020
https://doi.org/10.3390/su12093760
Machhamer RDziubany MCzenkusch LLaux HSchmeink AGollmer KNaumann SDartmann G(2019)Online Offline Learning for Sound-Based Indoor Localization Using Low-Cost HardwareIEEE Access10.1109/ACCESS.2019.29475817(155088-155106)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2947581

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