Research on the optimization of IIoT data processing latency

• Published in: Computer communications Vol. 151; pp. 290 - 298
• Main Authors: Liu, Weimin, Huang, Guan, Zheng, Aiyun, Liu, Jiaxin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2020
• Subjects: Fog computing; IIoT; ITCFN framework; SAPSO-LB algorithm; Task allocation; Fog computing; ITCFN framework; SAPSO-LB algorithm; Task allocation; IIoT
• ISSN: 0140-3664; 1873-703X
• DOI: 10.1016/j.comcom.2020.01.007

As for data processing, data should be sent to cloud, stored and computed. Usually, the amount of data is huge enough for higher latency of processing. In addition, the mobility of cloud service would be much slower. In this paper, an Industrial Internet of Things cloud–fog hybrid network (ITCFN) framework is proposed to solve high latency upon processing industrial data on cloud. In the production equipment area, edge devices such as routers and switches are utilized by framework to construct a fog computing layer between cloud server and production equipment. Since computing power of the edge devices in fog computing is much poor, a distributed computing method for multi-devices is proposed. The aim of minimum task processing delay is achieved by using the constrained particle swarm optimization load balancing algorithm based on simulated annealing method (SAPSO-LB). The experimental results show that the ITCFN based on SAPSO-LB algorithm can effectively reduce the industrial data processing delay. When ten fog computing devices are used and the collected data is between 4GB and 12GB, compared with cloud computing, the latency is improved by 84.1%-29.9%.