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 proceedingConference contributionpeer-review

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 languageEnglish
Title of host publication2021 Telecoms Conference, ConfTELE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665415880
DOIs
Publication statusPublished - 11 Feb 2021
Event2021 Telecoms Conference, ConfTELE 2021 - Leiria, Portugal
Duration: 11 Feb 202112 Feb 2021

Publication series

Name2021 Telecoms Conference, ConfTELE 2021

Conference

Conference2021 Telecoms Conference, ConfTELE 2021
Country/TerritoryPortugal
CityLeiria
Period11/02/2112/02/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

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

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