Power Optimal Control Method of Network Big Data for Mechanical Vibration Wireless Sensor

Abstract

With the gradual deepening of research on wireless sensor networks at home and abroad in recent years, some wireless sensor network application systems have emerged in the fields of academic research and engineering applications. Especially in the industrial field, wireless sensor network, as a new information perception technology, has attracted more and more attention due to its advantages of self-organization, flexible deployment, strong scalability, and on-chip processing capabilities. The purpose of this paper is to study the power optimization control method of wireless sensor of mechanical vibration by network big data. In view of the current problem of power control of mechanical vibration wireless sensor network nodes, first, under the premise of ensuring the accuracy of data collection, the key factors affecting the power control of mechanical vibration wireless sensor network nodes are analyzed, the wireless sensor power control model is designed, and the network big data is proposed. Impact on the power of wireless sensors; finally, based on the above research, the fuzzy power control algorithm and its simulation are specifically studied. First, a point-to-point transmission power control system model is designed, and the fuzzy logic controller module is designed in detail, including fuzzy Part of the membership function design, fuzzy rule determination and the use of defuzzification methods. Then use simulation tools to build a point-to-point simulation model and a simulated network simulation model. Finally, these two models are used to compare the performance of fuzzy power control algorithm and fixed power method. The experimental results show that the network survival time using fuzzy power control algorithm is twice that of the fixed power method. The fuzzy power control algorithm has great advantages in both effective communication distance and network survival time.