Research on optimisation processing of spatiotemporal correlation temperature and humidity data based on wireless sensor networks in cigarette factory

Abstract

The tobacco leaves have higher requirements on the environment during production and storage, especially for the temperature and humidity. In order to improve the quality of tobacco leaves, it is necessary to accurately monitor the temperature and humidity and optimise the parameters involved in the control. Based on the temperature and humidity monitoring system of cigarette factory, the authors optimised the temperature and humidity data obtained by wireless sensors. The data quality evaluation indicators were designed, and the Dixon criterion was used to eliminate gross errors in individual data instance. The abnormal data detection mechanism is designed to eliminate the fault data in multiple data instances by the similarity criteria among neighbour nodes in the area. In order to solve the problem of excessive computation caused by node explosion, extended rules of healthy node judgment were designed. Through the actual operation of the system for >6 months, the algorithm maintains good fault detection capabilities for different fault models and can provide support for temperature and humidity data processing of wireless sensor networks (WSNs).