Data Fusion Based on Temperature Monitoring of Aquaculture Ponds With Wireless Sensor Networks

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Abstract
In aquaculture ponds, wireless sensor networks with uneven temperature distribution and low collection efficiency may lead to poor monitoring effects. To improve the performance of the temperature monitoring, a high precision fusion strategy for a hierarchical wireless sensor network is proposed. In the bottom, the temperature data collected by the sensors are preprocessed by an improved unscented Kalman filter. In the middle layer, each cluster head sensor, as a local fusion center, is used to fuse the data collected from the sensors by a sequential analysis and fast inverse covariance intersection algorithm. In the top, a global fusion center is utilized to fuse the temperature data from the middle layer to reflect the global temperature by improved seagull algorithm to optimize the extreme learning machine algorithm. Through calculation and simulation, the results show that the fusion strategy not only reduces external interference but also improves the accuracy of global optimal temperature state estimation while ensuring the stability and accuracy of data fusion.
Funding Information
  • National Natural Science Foundation of China (52065064)

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