Evidence map›Paper›PMID 41411273›Full record

ArticlePloS one2025

Autonomous vehicles with augmented reality internet of things and edge intelligence system for industry 5.0 based on 6G.

Amjed A Ahmed, Aqeel Kamil Kadhim, Mohammad Kamrul Hasan, Sumaia M Al-Ghuribi, Dhafar Hamed Abd, Salah A Aliesawi, Belal Abdullah Hezam Murshed, Luke Topham, Wasiq Khan, Abir Jaafar Hussain

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Amjed A AhmedUniversity of Imam Al-Kadhum, Baghdad, Iraq.
Aqeel Kamil KadhimDepartment of Computer Techniques Engineering, Imam Alkadhum College (IKC), Baghdad, Iraq.
Mohammad Kamrul HasanCenter for Cyber Security, Faculty of Information Science and Technology at Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia.
Sumaia M Al-GhuribiSoftware Engineering Department, College of Computer Engineering & Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia.ORCID https://orcid.org/0000-0001-9714-9677
Dhafar Hamed AbdCollege of Computer Science and Information Technology, University of Anbar Ramadi, Ramadi, Iraq.ORCID https://orcid.org/0000-0003-0548-0616
Salah A AliesawiCollege of Computer Science and Information Technology, University of Anbar Ramadi, Ramadi, Iraq.
Belal Abdullah Hezam MurshedFaculty of Engineering and Information Technology, Amran University, Amran City, Yemen.ORCID https://orcid.org/0000-0003-2187-5044
Luke TophamSchool of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom.ORCID https://orcid.org/0000-0002-6689-7944
Wasiq KhanSchool of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom.
Abir Jaafar HussainCollege of Engineering, University of Sharjah, Sharjah, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In an era of rapidly evolving technology, traditional cloud computing struggles to meet the demands of resource-intensive smart devices. This necessitates a shift towards Edge Computing (EC), which brings computation and data storage closer to the network's edge, enhancing efficiency and reducing latency. This is particularly crucial for the Internet of Things (IoT), where supporting mobility, location awareness, and real-time processing are paramount. However, the scalability of EC applications is significantly influenced by network parameters and the capabilities of the computing system. This paper proposes a novel system architecture for Industry 5.0 that leverages the synergy between 6G networks, autonomous vehicles, Augmented Reality (AR), IoT, and edge intelligence to revolutionize transportation systems. Our approach integrates AR for enhanced user interfaces, utilizes IoT for data acquisition and control, and employs edge computing for real-time decision-making. Our experimental results demonstrate a strong correlation between processing speed and network bandwidth. While increasing either parameter individually enhances overall system performance. The two-tier architecture, combined with the Entity Objects (EO) model, demonstrates superior scalability compared to traditional approaches. By distributing processing tasks and leveraging the resources of other edge servers, the system can handle increasing numbers of AVs and data loads without compromising performance.

Indexed as

Artificial IntelligenceAugmented RealityIndustryInternet of ThingsAlgorithmsCloud ComputingHumans

Identifiers

PMID41411273
PMCPMC12714194

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.