Positioning as Service for 5G IoT Networks

Conference paper


El Boudani, B., Kanaris, L., Kokkinis, A., Chrysoulas, C., Dagiuklas, A. and Stavrou, S. (2021). Positioning as Service for 5G IoT Networks. 2021 Telecoms Conference (ConfTELE). Leiria 10 - 12 Feb 2021 Institute of Electrical and Electronics Engineers (IEEE).
AuthorsEl Boudani, B., Kanaris, L., Kokkinis, A., Chrysoulas, C., Dagiuklas, A. and Stavrou, S.
TypeConference paper
Abstract

Big Data and Artificial Intelligence are new tech- nologies 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.

KeywordsIndoor Positioning; Internet of Things; 5G; Deep learning; Big data; RSS; Radiomap
Year2021
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Accepted author manuscript
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File Access Level
Open
Publication dates
Print11 Feb 2021
Publication process dates
Accepted18 Jan 2021
Deposited19 Feb 2021
Additional information

© 20 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

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