Evidence map›Paper›PMID 39517773›Full record

ReviewSensors (Basel, Switzerland)2024

Theories and Methods for Indoor Positioning Systems: A Comparative Analysis, Challenges, and Prospective Measures.

Tesfay Gidey Hailu, Xiansheng Guo, Haonan Si, Lin Li, Yukun Zhang

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
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

5 authors.

Tesfay Gidey HailuDepartment of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa 16417, Ethiopia.ORCID 0000-0002-3229-8337
Xiansheng GuoDepartment of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0000-0002-8440-1607
Haonan SiDepartment of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Lin LiDepartment of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Yukun ZhangDepartment of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0000-0002-7283-5678

Funding

Municipal Government of Quzhou 2021D002National Natural Science Foundation of China 62171086
6 · The paper itself

Abstract

In the era of the Internet of Things (IoT), the demand for accurate positioning services has become increasingly critical, as location-based services (LBSs) depend on users' location data to deliver contextual functionalities. While the Global Positioning System (GPS) is widely regarded as the standard for outdoor localization due to its reliability and comprehensive coverage, its effectiveness in indoor positioning systems (IPSs) is limited by the inherent complexity of indoor environments. This paper examines the various measurement techniques and technological solutions that address the unique challenges posed by indoor environments. We specifically focus on three key aspects: (i) a comparative analysis of the different wireless technologies proposed for IPSs based on various methodologies, (ii) the challenges of IPSs, and (iii) forward-looking strategies for future research. In particular, we provide an in-depth evaluation of current IPSs, assessing them through multidimensional matrices that capture diverse architectural and design considerations, as well as evaluation metrics established in the literature. We further examine the challenges that impede the widespread deployment of IPSs and highlight the potential risk that these systems may not be recognized with a single, universally accepted standard method, unlike GPS for outdoor localization, which serves as the golden standard for positioning. Moreover, we outline several promising approaches that could address the existing challenges of IPSs. These include the application of transfer learning, feature engineering, data fusion, multisensory technologies, hybrid techniques, and ensemble learning methods, all of which hold the potential to significantly enhance the accuracy and reliability of IPSs. By leveraging these advanced methodologies, we aim to improve the overall performance of IPSs, thus paving the way for more robust and dependable LBSs in indoor environments.

Indexed as

comparative analysisdata fusionensemble learningfeature engineeringindoor positioningIoTsystem designtransfer learning

Identifiers

PMID39517773
PMCPMC11548171

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.