Fog computing perception mechanism based on throughput rate constraint in intelligent Internet of Things

Abstract

Due to the low power consumption, dense deployment, and unattended setup of the Internet of Things, it is more difficult to guarantee its security. In order to solve the source reliability problem, a fog computing perception mechanism based on throughput rate constraint is proposed in this paper. The core idea is the throughput rate constraint and perception strategy in fog computing, which are introduced to fog access points. The improved throughput rate constraint can achieve efficient information perception to eliminate these uncertainty and instability results and obtain more complete and reliable measurement data than a single sensor, which can improve the transmission efficiency of the network and the accuracy of the environment perception. And then, using multi-information perception to establish the prediction model can infer the degree distribution of fog node in FC network, and the transmission efficiency of the network and the accuracy of the environment perception are close to 90%. According to the results of theoretical analysis and simulation, the model has the characteristics of reliable node perception data and flexible expansion, and can effectively improve the reliability of the data source of the Internet of Things.