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Edge computing for Industry 5.0: fundamental, applications and research challenges
Abstract
Industry 5.0 is the next stage in industrial evolution, collaborating between human ingenuity and intelligent technologies to provide manufacturing solutions. Integrating modern technology like Artificial Intelligence (AI), robotics, and the Internet of Things (IoT) into manufacturing and production processes characterizes Industry 5.0. On the other hand, edge computing provides real-time data processing and analysis at the networks edge, closer to the data source and a vital component of Industry 5.0. Edge computing enables Industry 5.0 to access and communicate information about their industrial sectors using more accessible, standard hardware and software resources. However, no recent survey papers have examined the importance of edge computing in Industry 5.0. This study aims to fill that gap by presenting a survey on the importance of edge computing in Industry 5.0 and discussing a variety of technologies that could be used to implement and support this new industrial paradigm. First, we outline an overview and fundamentals of edge computing in Industry 5.0 architecture. Then objectives of Industry 5.0 are summarized to address various research challenges, including privacy, human-robot co-working, sustainability, and robust networks. Afterwards, this paper provides an extensive overview of emerging technologies for Industry 5.0, such as collaborative robots, AI, Digital Twins, and many more. In addition, this survey highlights various open research challenges and potential solutions that should be addressed further to achieve Industry 5.0.
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“These three fundamental objectives are resilience, sustainability, and human-centricity [71].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [71]
In the citing paper
“According to the study conducted by Sharma et al. [10], edge computing reduces latency and facilitates real-time decision-making.”
From Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework (2024) · cites this paper as [10]
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Metadata
- Authors
- Sharma, M., Tomar, A., & Hazra, A.
- Affiliations
- Netaji Subhas University of Technology (IN)
- Indian Institute of Information Technology Sri City (IN)
- Venue
- IEEE Internet of Things Journal
- Published
- 2024-06-01
- Volume
- 11
- Issue
- 11
- Pages
- 19070-19093
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W4392543840
- Cited by
- 312
- References
- 94
- Citations by year
- 2026: 117 · 2025: 161 · 2024: 34
- FWCI
- 51.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Data and IoT Technologies
- Keywords
- edge computing, Industry 5.0, manufacturing industry, collaborative robots, digital twin, artificial intelligence in manufacturing, Internet of Things, privacy, sustainability, network robustness, real-time data processing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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