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Positioning by Mult...
Positioning by Multicell Fingerprinting in UrbanNB-IoT Networks
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- De Nardis, Luca (författare)
- Sapienza University of Rome, Italy
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- Caso, Giuseppe (författare)
- Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)
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- Alay, Özgü (författare)
- Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013),University of Oslo, Norway
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- Neri, Marco (författare)
- Rohde & Schwarz, Italy
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- Brunstrom, Anna, 1967- (författare)
- Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)
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- Di Benedetto, Maria-Gabriella (författare)
- Sapienza University of Rome, Italy
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(creator_code:org_t)
- MDPI, 2023
- 2023
- Engelska.
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 23:9
- Relaterad länk:
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https://doi.org/10.3...
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https://kau.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
Nyckelord
- Energy efficiency
- Large dataset
- Long Term Evolution (LTE)
- Average positioning error
- Fingerprinting
- Leading technology
- Multicell
- Multiple cells
- Narrow bands
- Narrowband internet of thing
- Positioning
- State-of-the-art approach
- Internet of things
- Computer Science
- Datavetenskap
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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