The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Energy-Aware Dynamic Digital Twin Placement in Mobile Edge Computing
Abstract
5G and beyond networks promise to revolutionize device connectivity and interaction with seamless connectivity, fast response times, and intelligent features. With the development of IoT and its need for high-speed, low-latency connectivity, these networks will play a critical role in enabling the next generation of smart and connected systems. Digital twin technology will further enhance the capabilities of these systems by providing accurate and dynamic simulations of physical assets, enabling organizations to make better decisions and optimize their operations. Despite the prior research conducted in the field of optimal placement of digital twins, a few of these studies have overlooked the impact of user transmission delay, while none have addressed user energy consumption. The focus is on optimizing the positioning of digital twins at the edge servers while taking into account the energy consumption of users and ensuring reduced latency between physical devices and their corresponding digital twins. Additionally, the paper explores the potential for social-aware arrangements of digital twins to streamline the process of data exchange and service discovery across the Social Internet of Things (SIoT) network. This work utilized the CPLEX optimization tool to obtain the optimal placement of IoT digital twins at the edge servers, taking into account both user transmission delay and energy consumption. The evaluation results indicate that the proposed model enables optimal pl…
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“Scalable cloud-based platforms with dynamic resource allocation offer an effective solution to manage these larger datasets while maintaining synchronization [131].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [131]
Citations
Cites
Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models
Minerva, R., Lee, G. M., & Crespi, N. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.2998530 · Cited by 812
Digital Twins: State of the art theory and practice, challenges, and open research questions
Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100383 · Cited by 587
Optimal Placement of Social Digital Twins in Edge IoT Networks
Chukhno, O., Chukhno, N., Araniti, G., Campolo, C., Iera, A., & Molinaro, A. (2020) · Sensors · doi:10.3390/s20216181 · Cited by 42
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Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0
Mon, B. F., Hayajneh, M., Ali, N. A., Ullah, F., Ullah, H., & Alkobaisi, S. (2025) · Computers, Materials & Continua · doi:10.32604/cmc.2025.061411 · Cited by 26
Metadata
- Authors
- Hematyar, M., & Movahedi, Z.
- Affiliations
- Iran University of Science and Technology (IR)
- ORCID
- Movahedi, Zeinab
- Venue
- 2023 13th International Conference on Computer and Knowledge Engineering (ICCKE)
- Publisher
- IEEE
- Published
- 2023-11-01
- Pages
- 048-053
- OpenAlex
- W4389041069
- Cited by
- 2
- References
- 3
- Citations by year
- 2026: 1 · 2025: 1
- FWCI
- 0.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computer and Knowledge Engineering (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 › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- mobile edge computing, transmission delay, social Internet of Things, 5G, Internet of Things, edge servers, service discovery
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