Name
Title | ||
|---|---|---|
Process parameter estimation oriented industrial wireless sensor networks: A sequential approach |
Abstract | ||
|---|---|---|
Process parameter estimation, to a large extent, determines the quality of the industrial production. Traditionally, limited sensors are deployed in production field by elaborate wiring, which cannot provide the accurate estimate in the hostile industrial environment. Recently, industrial wireless sensor network (IWSN) has been considered as one promising technology to improve the process parameter estimation by deploying more sensors flexibly and making them work collaboratively. In this paper, a sequential IWSN (Seq-IWSN) approach is provided for the temperature estimation of the steel slab during the hot strip milling process. In Seq-IWSN, the network deployment and scheduling strategies coupling with the process parameter estimation algorithm are involved. Simulation results based on NS3 network simulator show that Seq-IWSN can help to reduce the estimation error to less than 3°C, although the covariance of the sampling noise is as large as 100. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICC.2017.7996324 | 2017 IEEE International Conference on Communications (ICC) |
Keywords | Field | DocType |
Industrial wireless sensor network,network deployment,scheduling,consensus-based sequential estimation | Industrial production,Coupling,Scheduling (computing),Computer science,Network simulation,Real-time computing,Process variable,Sampling (statistics),Wireless sensor network,Covariance | Conference |
ISSN | ISBN | Citations |
1550-3607 | 978-1-4673-9000-2 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feilong Lin | 1 | 18 | 6.36 |
| Shanying Zhu | 2 | 130 | 21.54 |
| Cai-Lian Chen | 3 | 831 | 98.98 |
| Xinping Guan | 4 | 2791 | 253.38 |