The DOI matches its Crossref record; 92 of the 94 references it cites were found in a registry or an index.
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]
Citations
Cites
Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2444095 · Cited by 8,630 · cited 1×
“It increases customer satisfaction and loyalty and creates customized experiences using data supplied by IoE [61].”
Industry 4.0: state of the art and future trends
Xu, L. D., Xu, E. L., & Li, L. (2018) · International Journal of Production Research · doi:10.1080/00207543.2018.1444806 · Cited by 3,405
The expected contribution of Industry 4.0 technologies for industrial performance
Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018) · International Journal of Production Economics · doi:10.1016/j.ijpe.2018.08.019 · Cited by 2,201
Chowdhury, M. Z., Shahjalal, M. A., Ahmed, S., & Jang, Y. M. (2020) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2020.3010270 · Cited by 1,925
Industry 5.0: A survey on enabling technologies and potential applications
Maddikunta, P. K. R., Pham, Q.-V., B, P., Deepa, N., Dev, K., Gadekallu, T. R., Ruby, R., & Liyanage, M. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2021.100257 · Cited by 1,890
Lu, Y., Liu, C., Wang, K. I., Huang, H., & Xu, X. W. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101837 · Cited by 1,753
A Survey on Metaverse: Fundamentals, Security, and Privacy
Wang, Y., Su, Z., Zhang, N., Xing, R., Liu, D., Luan, T. H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3202047 · Cited by 1,655
6G Wireless Communications: Vision and Potential Techniques
Yang, P., Xiao, Y., Xiao, M., & Li, S. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800418 · Cited by 1,153
Survey on fog computing: architecture, key technologies, applications and open issues
Hu, P., Dhelim, S., Ning, H., & Qiu, T. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.09.002 · Cited by 880
Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook
Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., & Barata, J. (2020) · IEEE Access · doi:10.1109/access.2020.3042874 · Cited by 820
Research works printed but not linked: 5
- Hasegawa, Industrial wireless standardization—Scope and implementation of ISA SP100 standard, 2059
- Mattila, 2022, The fifth industrial revolution: Enlightenment of 5ire towards industry 5.0, Int. J. Creat. Res. Thoughts, 10, 178
- 10.1016/j.ecmx.2020.100048 · DOI
- Breque, 2021, Industry 5.0: Towards a Sustainable, Human-Centric and Resilient European Industry
- Van der Valk, A taxonomy of digital twins, 1
Cited by
Sorted by citations
Items 301–338 of 338
Gürkan, D., Sağbaş, B., & DURAKBASA, N. (2026) · Lecture notes in mechanical engineering · doi:10.1007/978-3-032-22784-3_22
Seisa, A. S., Pagliari, E., Damigos, G., Small, E., & Nikolakopoulos, G. (2026) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med70602.2026.11598005
Ayoubi, M., & Li, M. (2026) · Cluster Computing · doi:10.1007/s10586-026-06358-5
Large-Scale Sensor Systems for Sentient Environments: Emerging Research Directions
Sedlak, B., Pujol, V. C., Motlagh, N. H., Morabito, R., Wilhelmi, F., Magrans, I., Murturi, I., Bellalta, B., Donta, P. K., & Dustdar, S. (2026) · IEEE Journal of Selected Areas in Sensors · doi:10.1109/jsas.2026.3716613
Real-Time Transmission Line Fault Monitoring Using SVRG-DDPG and Lightweight Edge Computing
Lin, M., Ding, Y., Lu, J., Chen, W., & Wang, T. (2026) · Informatica · doi:10.31449/inf.v50i12.13341
Industry 5.0: An Energy-Efficient Smart Task Offloading Mechanism for Multi-Access Edge Computing
Saddique, S., Baig, A., Arif, E., Zaheer, M., Hamayoun, S., & Khan, F. (2025) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/1581
Nest: Adaptive Tree-Graph Vector Database for Energy-Constrained Edge Devices
Glatter, S. L., & Chakraborty, P. (2026) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1145/3816440.3818530
A Fine-Grained Data Reconstruction Framework for Sparsely Sampled Industrial Time-Series
Zhang, Y., Yin, H., Zhou, H., Mo, R., Zhang, T., & Yang, S. X. (2026) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2026.3717545
Optimized task offloading and resource allocation framework for edge-assisted IoT applications
Jha, M. K., & Kumar, M. (2026) · Cluster Computing · doi:10.1007/s10586-026-06442-w
Cao, J., Wang, J., Huo, Z. H., Hui, J. Q., & Wang, Z. Q. (2026) · Advanced Electromagnetics · doi:10.7716/aem.v15i3.3430
Huang, W., Wang, Y., & Ren, Y. (2026) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2026.149230
Saravanan, S., Meena, V., Yasmin, S. M., Amirtharajan, R., & Praba, T. S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-28478-5_26
An Efficient Device Deployment Strategy for Obstacle-constrained Industrial Internet of Things
Yousefi, S., & Najjar-Ghabel, S. (2026) · Mobile Networks and Applications · doi:10.1007/s11036-026-02510-y
Fluid MIMO: A Timely MIMO Upgrade
Rao, C., Wong, K., Shin, H., & Murch, R. (2026) · Engineering · doi:10.1016/j.eng.2026.08.018
Ganeshanavar, P., Narayan, D. G., Savanur, S., Kachavimath, A. V., & Hiremath, P. S. (2026) · International Conference on Intelligent and Sustainable Electronics & Computing Technologies (INSECT) · doi:10.1109/insect68872.2026.11663648
Li, X., Wang, C., & Hu, B. (2026) · IEEE Access · doi:10.1109/access.2026.3732110
Resilient IIoT Edge-to-Cloud Framework for Real-Time Monitoring of Renewable Energy Plants
P.P.Manodhara, & Ediriweera, W. E. P. S. (2026) · Moratuwa Engineering Research Conference (MERCon) · doi:10.1109/mercon71835.2026.11691388
Rajappa, R. T., Periyasamy, P. S., & Kanagasabai, K. (2026) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-026-02218-6
Zhong, C., Lian, J., & Liu, C. (2026) · International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC) · doi:10.1109/aiotc71250.2026.11689147
From digital to intelligent: A framework and roadmap for industrial intelligent transformation (IIT)
Wang, C., Ming, X., & Zhang, X. (2026) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2026.101215
Rasheed, I., Alahmari, S., Altamimi, S. N., & Mostafa, H. (2026) · Internet of Things · doi:10.1016/j.iot.2026.102110
AI-Enabled V2X Framework for Improving Tourism in Hilly Regions
Chanu, A. D., Singhal, Y., Khatua, M., & Nath, S. B. (2026) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct68978.2026.11700604
LLM4ETS: Self‐Evolving Algorithm Design for Edge Task Scheduling via Large Language Models
Zhao, Y., Gao, S., Wang, Y., Ma, Y., Li, B., Wang, J., Sedlak, B., Donta, P. K., & Dustdar, S. (2026) · Software Practice and Experience · doi:10.1002/spe.70110
Shen, Q., Yang, C., Bai, W., & Wang, C. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12461-w
An uncertainty-aware compute continuum architecture for underwater species detection
Ferrari, M., Fernández, J. D. R., Aguzzi, J., D’Agostino, D., & Marini, S. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.104067
TSA-Net+: a communication-aware hybrid ANN-SNN framework for energy-efficient IoT inference
Meshram, N., Anand, V., & Tiwari, V. (2026) · Internet of Things · doi:10.1016/j.iot.2026.102117
CrashSenseAI: latency-aware edge AI system for real-time traffic accident detection and notification
Poreddy, C. R. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.4120
Sun, Y., Dong, Z., & Wang, G. (2026) · Research Square · doi:10.21203/rs.3.rs-11200633/v1
Pant, A., Joshi, B. P., & Punetha, N. (2026) · International Conference on Innovative Sustainable Computational Technologies (CISCT) · doi:10.1109/cisct69589.2026.11726914
Şara, M., Ozdemir, K., Eken, S., & Tuncer, A. (2026) · Annals of Operations Research · doi:10.1007/s10479-026-07462-w
Edge Computing Architecture for Real-Time Information Processing in Autonomous Industrial Systems
Gupta, S., Ray, K., Hossain, S., Ali, J., Yupapin, P. P., & Faubert, J. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-33805-1_49
Gomathy, A., & Durga, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-29322-0_5
Hrabovska, N., Budinská, I., Zolotová, I., Kajáti, E., & Kenyeres, M. (2026) · Sustainability · doi:10.3390/su182110928
Kho, W., Hwang, H., Kim, H., Lee, N., Min, D., Ahn, D., Kim, J., Han, C., Jeong, D., Cho, S., et al. (2026) · Small Methods · doi:10.1002/smtd.71165
Muppala, M., & Chamarthi, H. (2026) · doi:10.1109/icosics70095.2026.11746707
When digital meets physical: how does digital-real integration drive corporate open innovation?
Zhan, X., Zhan, Y., Li, G., & Cao, Q. (2026) · Technology Analysis and Strategic Management · doi:10.1080/09537325.2026.2753216
Li, Y., Zhou, J., & Gao, L. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.134970
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
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