The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory
Abstract
Due to the development of modern information technology, the emergence of the fog computing enhances equipment computational power and provides new solutions for traditional industrial applications. Generally, it is impossible to establish a quantitative energy-aware model with a smart meter for load balancing and scheduling optimization in smart factory. With the focus on complex energy consumption problems of manufacturing clusters, this paper proposes an energy-aware load balancing and scheduling (ELBS) method based on fog computing. First, an energy consumption model related to the workload is established on the fog node, and an optimization function aiming at the load balancing of manufacturing cluster is formulated. Then, the improved particle swarm optimization algorithm is used to obtain an optimal solution, and the priority for achieving tasks is built toward the manufacturing cluster. Finally, a multiagent system is introduced to achieve the distributed scheduling of manufacturing cluster. The proposed ELBS method is verified by experiments with candy packing line, and experimental results showed that proposed method provides optimal scheduling and load balancing for the mixing work robots.
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Cites
(2007) · Handbook of Approximation Algorithms and Metaheuristics · doi:10.1201/9781420010749-33 · Cited by 6,607
(2019) · Handbook of Machine Learning · doi:10.1142/9789811205675_0004 · Cited by 6,270
Fog computing and its role in the internet of things
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012) · Proceedings of the first edition of the MCC workshop on Mobile cloud computing · doi:10.1145/2342509.2342513 · Cited by 6,244
Dorigo, M., Birattari, M., & Stutzle, T. (2006) · IEEE Computational Intelligence Magazine · doi:10.1109/ci-m.2006.248054 · Cited by 1,880
Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016) · Computer Networks · doi:10.1016/j.comnet.2015.12.017 · Cited by 1,404
Self-citationFog Computing: A Platform for Internet of Things and Analytics
Bonomi, F., Milito, R., Natarajan, P., & Zhu, J. (2014) · Studies in Computational Intelligence · doi:10.1007/978-3-319-05029-4_7 · Cited by 1,222
Towards energy and resource efficient manufacturing: A processes and systems approach
Duflou, J. R., Sutherland, J. W., Dornfeld, D., Herrmann, C., Jeswiet, J., Kara, S., Hauschild, M., & Kellens, K. (2012) · CIRP Annals · doi:10.1016/j.cirp.2012.05.002 · Cited by 1,164
Ishibuchi, H., Yoshida, T., & Murata, T. (2003) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2003.810752 · Cited by 773
Security and privacy in fog computing: Challenges
Mukherjee, M., Matam, R., Shu, L., Maglaras, L., Ferrag, M. A., Choudhury, N., & Kumar, V. (2017) · IEEE Access · doi:10.1109/access.2017.2749422 · Cited by 533
An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling
Liu, B., Wang, L., & Jin, Y.-H. (2007) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2006.883272 · Cited by 480
Metadata
- Authors
- Wan, J., Chen, B., Wang, S., Xia, M., Li, D., & Liu, C.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2018-10
- Volume
- 14
- Issue
- 10
- Pages
- 4548-4556
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2790265691
- Cited by
- 322
- References
- 23
- FWCI
- 30.94
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wan, J., Chen, B., Wang, S., Xia, M., Li, D., & Liu, C. (2018). Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2018.2818932
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