Positioning as service for 5G IoT networks

Brahim El Boudani; Loizos Kanaris; Akis Kokkinis; Christos Chrysoulas; Tasos Dagiuklas; Stavros Stavrou

doi:10.1109/ConfTELE50222.2021.9435586

Positioning as service for 5G IoT networks

Brahim El Boudani, Loizos Kanaris, Akis Kokkinis, Christos Chrysoulas, Tasos Dagiuklas, Stavros Stavrou

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

Big Data and Artificial Intelligence are new technologies to improve indoor localization. It focuses on the use of machine learning probabilistic algorithms to extract, model and analyse live and historical signal data obtained from several sources. In this respect, the data generated by 5G network and the Internet of Things is quintessential for precise indoor positioning in complex building environments. In this paper, we present a new architecture for assets and personnel location management in 5G network with an emphasis on vertical sectors in smart cities. Moreover, we explain how Big Data and Machine learning can be used to offer positioning as service. Additionally, we implement a new deep learning model for 3D positioning using the proposed architecture. The performance of the proposed model is compared against other Machine Learning algorithms.

Original language	English
Title of host publication	2021 Telecoms Conference (ConfTELE)
Publisher	IEEE
ISBN (Electronic)	9781665415880
ISBN (Print)	9781665446808
DOIs	https://doi.org/10.1109/ConfTELE50222.2021.9435586
Publication status	Published - 26 May 2021
Event	Telecoms Conference 2021 - Leiria, Portugal Duration: 11 Feb 2021 → 12 Feb 2021 https://www.aconf.org/conf_177737.2021_Telecoms_Conference.html

Conference

Conference	Telecoms Conference 2021
Abbreviated title	ConfTELE 2021
Country/Territory	Portugal
City	Leiria
Period	11/02/21 → 12/02/21
Internet address	https://www.aconf.org/conf_177737.2021_Telecoms_Conference.html

Keywords

5G
Big Data
Deep Learning
Indoor Positioning
Internet of Things
Radiomap
RSS

ASJC Scopus subject areas

Information Systems
Electrical and Electronic Engineering
Communication

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ConfTELE50222.2021.9435586

Cite this

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title = "Positioning as service for 5G IoT networks",

abstract = "Big Data and Artificial Intelligence are new technologies to improve indoor localization. It focuses on the use of machine learning probabilistic algorithms to extract, model and analyse live and historical signal data obtained from several sources. In this respect, the data generated by 5G network and the Internet of Things is quintessential for precise indoor positioning in complex building environments. In this paper, we present a new architecture for assets and personnel location management in 5G network with an emphasis on vertical sectors in smart cities. Moreover, we explain how Big Data and Machine learning can be used to offer positioning as service. Additionally, we implement a new deep learning model for 3D positioning using the proposed architecture. The performance of the proposed model is compared against other Machine Learning algorithms.",

keywords = "5G, Big Data, Deep Learning, Indoor Positioning, Internet of Things, Radiomap, RSS",

author = "Boudani, \{Brahim El\} and Loizos Kanaris and Akis Kokkinis and Christos Chrysoulas and Tasos Dagiuklas and Stavros Stavrou",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; Telecoms Conference 2021 , ConfTELE 2021 ; Conference date: 11-02-2021 Through 12-02-2021",

year = "2021",

month = may,

day = "26",

doi = "10.1109/ConfTELE50222.2021.9435586",

language = "English",

isbn = "9781665446808",

booktitle = "2021 Telecoms Conference (ConfTELE)",

publisher = "IEEE",

address = "United States",

url = "https://www.aconf.org/conf\_177737.2021\_Telecoms\_Conference.html",

}

TY - GEN

T1 - Positioning as service for 5G IoT networks

AU - Boudani, Brahim El

AU - Kanaris, Loizos

AU - Kokkinis, Akis

AU - Chrysoulas, Christos

AU - Dagiuklas, Tasos

AU - Stavrou, Stavros

PY - 2021/5/26

Y1 - 2021/5/26

N2 - Big Data and Artificial Intelligence are new technologies to improve indoor localization. It focuses on the use of machine learning probabilistic algorithms to extract, model and analyse live and historical signal data obtained from several sources. In this respect, the data generated by 5G network and the Internet of Things is quintessential for precise indoor positioning in complex building environments. In this paper, we present a new architecture for assets and personnel location management in 5G network with an emphasis on vertical sectors in smart cities. Moreover, we explain how Big Data and Machine learning can be used to offer positioning as service. Additionally, we implement a new deep learning model for 3D positioning using the proposed architecture. The performance of the proposed model is compared against other Machine Learning algorithms.

AB - Big Data and Artificial Intelligence are new technologies to improve indoor localization. It focuses on the use of machine learning probabilistic algorithms to extract, model and analyse live and historical signal data obtained from several sources. In this respect, the data generated by 5G network and the Internet of Things is quintessential for precise indoor positioning in complex building environments. In this paper, we present a new architecture for assets and personnel location management in 5G network with an emphasis on vertical sectors in smart cities. Moreover, we explain how Big Data and Machine learning can be used to offer positioning as service. Additionally, we implement a new deep learning model for 3D positioning using the proposed architecture. The performance of the proposed model is compared against other Machine Learning algorithms.

KW - 5G

KW - Big Data

KW - Deep Learning

KW - Indoor Positioning

KW - Internet of Things

KW - Radiomap

KW - RSS

UR - https://www.scopus.com/pages/publications/85107804072

U2 - 10.1109/ConfTELE50222.2021.9435586

DO - 10.1109/ConfTELE50222.2021.9435586

M3 - Conference contribution

AN - SCOPUS:85107804072

SN - 9781665446808

BT - 2021 Telecoms Conference (ConfTELE)

PB - IEEE

T2 - Telecoms Conference 2021

Y2 - 11 February 2021 through 12 February 2021

ER -

Positioning as service for 5G IoT networks

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this