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

Ahmed, AA, Kadhim, AK, Hasan, MK, AL-Ghuribi, SM ORCID: 0000-0001-9714-9677, Abd, DH ORCID: 0000-0003-0548-0616, Aliesawi, SA, Murshed, BAH ORCID: 0000-0003-2187-5044, Topham, L ORCID: 0000-0002-6689-7944, Khan, W ORCID: 0000-0002-7511-3873 and Hussain, AJ (2025) Autonomous vehicles with augmented reality internet of things and edge intelligence system for industry 5.0 based on 6G. Plos One, 20 (12). ISSN 1932-6203

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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.

Item Type:	Article
Uncontrolled Keywords:	Humans; Algorithms; Industry; Artificial Intelligence; Cloud Computing; Internet of Things; Augmented Reality; Internet of Things; Augmented Reality; Industry; Algorithms; Cloud Computing; Artificial Intelligence; Humans; 4605 Data Management and Data Science; 4606 Distributed Computing and Systems Software; 46 Information and Computing Sciences; Networking and Information Technology R&D (NITRD); 9 Industry, Innovation and Infrastructure; Internet of Things; Augmented Reality; Industry; Algorithms; Cloud Computing; Artificial Intelligence; Humans; General Science & Technology
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science and Mathematics
Publisher:	Public Library of Science (PLoS)
Date of acceptance:	1 December 2025
Date of first compliant Open Access:	15 January 2026
Date Deposited:	15 Jan 2026 14:59
Last Modified:	15 Jan 2026 14:59
DOI or ID number:	10.1371/journal.pone.0339022
Editors:	Zyrianoff, I
URI:	https://researchonline.ljmu.ac.uk/id/eprint/27913

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