WSN Spatio-temporal Correlation Data Fusion Method for Dairy Cow

Abstract

The cowshed environment has significant impacts on the yield, diseases and behaviors of dairy cows. Heat stress, in particular, has a great impact on yield. The cowshed environment monitoring system based on wireless sensor network can accurately sense the temperature and other environmental parameters in real time and provide basis for manual environmental intervention and control. Energy constraint is one of the important problems that affect the long-term stable monitoring by the dairy cow wireless sensor network. So, the weighted Markov chain method is used to predict the time series of cowshed temperature. Replacing the actual values with the predicted values at the cluster head can effectively reduce data traffic in the cluster, thereby reducing network power consumption. Test data show that, the average variance of the cowshed environment temperature predicted by the method proposed in this paper is 0.185, and the average power consumption is reduced by about 40% when the compression ratio is 0.3, which effectively prolongs the network lifetime. In addition to that, the cowshed environment prediction can also help make pre-judgments for environmental control, reduce or avoid the heat stress of dairy cows after the environmental parameters exceed the thresholds and provide the basis for the multi-source data fusion for dairy cow.