Fang Meng, Guogen Fan
The big data transmitted by the Internet of Things is affected by the inter-code interference of the transmission channel, which will cause large scheduling delays and bit errors. In this paper, a big data adaptive equilibrium scheduling model based on decision feedback equalization was proposed to improve the big data scheduling and adaptive equalization control capability of the Internet of Things transmission. In order to improve the big data scheduling and adaptive equalization control capability of the Internet of Things transmission. The transverse filtering control algorithm was used to optimize the performance of IoT transmission, and the IoT transmission channel model was constructed. The inter-code interference filtering method was adopted, and the big data scheduling anti-interference design of the Internet of Things transmission was executed. Based on bandwidth modulation and baud interval equalization control technology, large data transmission and adaptive tuning of the Internet of Things transmission communication system were performed; The channel equalization control model for the Internet of Things to transmit big data was constructed, and the maximum likelihood estimation value of the big data adaptive equalization scheduling was calculated; The IoT transmission big data fuzzy clustering process was implemented to realize the big data adaptive equalization scheduling of the Internet of Things transmission network. According to the research, based on the method in this paper, the balance of the big data scheduling of the Internet of Things was better, the anti-interference ability was stronger, and the output bit error rate was lower. The method in this paper has good application value in the design of IoT transmission and communication optimization.
Big data, adaptive equilibrium scheduling model
Fang Meng, Guogen Fan, Research on Adaptive Equilibrium Scheduling Model of Big Data Based on Internet of Things Transmission. 2019 5th International Conference on Advanced Computing, Networking and Security (ADCONS 2019). 2019: 52-59.