Information flow perception modeling and optimization of Internet of Things for cloud services

Abstract

Abstract IoT (Internet of Things) information flow perception is the foundation of the IoT architecture and plays an important role. Providing cloud services on demand is one of the key issues and core features of cloud services in the IoT information flow perception. Firstly, we study the mixed logic dynamics (MLD) modeling method of IoT information flow awareness, and use IoT sensor nodes, controlled nodes, and coordination nodes to describe system application scenarios, and use automata for internal information transmission. Secondly, an open queuing method based on large-scale information flow perception modeling and network delay analysis method is proposed. By analyzing the end-to-end delay of the node path and the average delay analysis of the whole queuing network, the open queuing is obtained. Aiming at the characteristics of distributed data of information flow perception in the IoT, a priority-based queuing network is proposed to model and analyze the aggregation nodes based on embedded multi-core SoC(System on Chip), which greatly improves the performance of embedded multi-core SoC.