TY - JOUR
T1 - A Hierarchical Data Transmission Framework for Industrial Wireless Sensor and Actuator Networks
AU - Jin, Xi
AU - Kong, Fanxin
AU - Kong, Linghe
AU - Wang, Huihui
AU - Xia, Changqing
AU - Zeng, Peng
AU - Deng, Qingxu
N1 - Funding Information:
Manuscript received October 23, 2016; revised February 5, 2017; accepted March 19, 2017. Date of publication March 22, 2017; date of current version August 1, 2017. This work was supported in part by the National Natural Science Foundation of China under Grant 61502474 and Grant 61233007 and in part by the Youth Innovation Promotion Association of the Chinese Academy of Sciences. Paper no. TII-16-1201. (Corresponding author: Peng Zeng.) X. Jin and C. Xia are with the Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China (e-mail: jinxi@sia.cn; xiachangqing@sia.cn).
PY - 2017/8
Y1 - 2017/8
N2 - A smart factory generates vast amounts of data that require transmission via large-scale wireless networks. Thus, the reliability and real-time performance of large-scale wireless networks are essential for industrial production. A distributed data transmission scheme is suitable for large-scale networks, but is incapable of optimizing performance. By contrast, a centralized scheme relies on knowledge of global information and is hindered by scalability issues. To overcome these limitations, a hybrid scheme is needed. We propose a hierarchical data transmission framework that integrates the advantages of these schemes and makes a tradeoff among real-time performance, reliability, and scalability. The top level performs coarse-grained management to improve scalability and reliability by coordinating communication resources among subnetworks. The bottom level performs fine-grained management in each subnetwork, for which we propose an intrasubnetwork centralized scheduling algorithm to schedule periodic and aperiodic flows. We conduct both extensive simulations and realistic testbed experiments. The results indicate that our method has better schedulability and reduces packet loss by up to 22% relative to existing methods.
AB - A smart factory generates vast amounts of data that require transmission via large-scale wireless networks. Thus, the reliability and real-time performance of large-scale wireless networks are essential for industrial production. A distributed data transmission scheme is suitable for large-scale networks, but is incapable of optimizing performance. By contrast, a centralized scheme relies on knowledge of global information and is hindered by scalability issues. To overcome these limitations, a hybrid scheme is needed. We propose a hierarchical data transmission framework that integrates the advantages of these schemes and makes a tradeoff among real-time performance, reliability, and scalability. The top level performs coarse-grained management to improve scalability and reliability by coordinating communication resources among subnetworks. The bottom level performs fine-grained management in each subnetwork, for which we propose an intrasubnetwork centralized scheduling algorithm to schedule periodic and aperiodic flows. We conduct both extensive simulations and realistic testbed experiments. The results indicate that our method has better schedulability and reduces packet loss by up to 22% relative to existing methods.
KW - Data flow scheduling
KW - hierarchical data transmission framework
KW - resource coordination
UR - http://www.scopus.com/inward/record.url?scp=85029429650&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029429650&partnerID=8YFLogxK
U2 - 10.1109/TII.2017.2685689
DO - 10.1109/TII.2017.2685689
M3 - Article
AN - SCOPUS:85029429650
VL - 13
SP - 2019
EP - 2029
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
SN - 1551-3203
IS - 4
M1 - 7885069
ER -