Evidence map›Paper›PMID 37896691›Full record

ArticleSensors (Basel, Switzerland)2023

On Indoor Localization Using WiFi, BLE, UWB, and IMU Technologies.

Samuel G Leitch, Qasim Zeeshan Ahmed, Waqas Bin Abbas, Maryam Hafeez, Pavlos I Lazaridis, Pradorn Sureephong, Temitope Alade

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Samuel G LeitchDepartment of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK.ORCID 0009-0006-0575-0945
Qasim Zeeshan AhmedDepartment of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK.ORCID 0000-0002-3957-5341
Waqas Bin AbbasDepartment of Electrical and Electronic Engineering, University of Bristol, Bristol BS8 1QU, UK.ORCID 0000-0003-3860-1576
Maryam HafeezDepartment of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK.
Pavlos I LazaridisDepartment of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK.ORCID 0000-0001-5091-2567
Pradorn SureephongCollege of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand.
Temitope AladeDepartment of Computer Science, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.

Funding

Engineering and Physical Sciences Research Council DTP
6 · The paper itself

Abstract

Indoor localization is a key research area and has been stated as a major goal for Sixth Generation (6G) communications. Indoor localization faces many challenges, such as harsh wireless propagation channels, cluttered and dynamic environments, non-line-of-sight conditions, etc. There are various technologies that can be applied to address these issues. In this paper, four major technologies for implementing an indoor localization system are reviewed: Wireless Fidelity (Wi-Fi), Ultra-Wide Bandwidth Radio (UWB), Bluetooth Low Energy (BLE), and Inertial Measurement Units (IMU). Sections on Data Fusion (DF) and Machine Learning (ML) have been included as well due to their key role in Indoor Positioning Systems (IPS). These technologies have been categorized based on the techniques that they employ and the associated errors in localization. A brief comparison between these technologies is made based on specific performance metrics. Finally, the limitations of these techniques are identified to aid future research.

Indexed as

6GBLEdata fusionIMUindoor localizationPDRUWBwi-fi

Identifiers

PMID37896691
PMCPMC10610672

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.