Multi-Sensor Complex Network Data Fusion Under the Condition of Uncertainty of Coupling Occurrence Probability

Based on the time and location information of nodes, multi-sensor complex networks can achieve high-speed and low-delay exchange of control and sensor data between sensor nodes to ensure the accuracy and real-time performance of the entire detection and control system. However, complex multi-sensor networks face limitations in terms of computing, storage, and network resources. After grouping the sensor data of nodes, this paper employs an adaptive weighted data fusion method to complete the data fusion processing in the network. This approach can significantly reduce the data redundancy in the wireless sensor network, save a large amount of storage resources, and lower the network bandwidth occupation, with high efficiency and good scalability. The least square (LS) fitting is performed by two sets of temperature and humidity data obtained by weighted fusion at the cluster head node. Experimental results indicate that the fusion of two-dimensional data transmitted to the base station or control center can further diminish the amount of data transmission. In addition, after the adaptive weighted data fusion of the two-dimensional data at the cluster head node, the energy consumption of the cluster head node without the fusion and the cluster head node after the fusion is compared. The results demonstrate that the energy consumption of the cluster head node is notably abridged, the energy of the cluster head node is saved to a greater extent, and the life cycle of the network is prolonged.