A Research Study on an Entropy-Weighted Multi-View Fusion Approach for Agricultural WSN Data Based on Fuzzy Clustering

This study proposes an entropy-weighted multi-view collaborative fusion algorithm to address key challenges in agricultural Wireless Sensor Network (WSN) monitoring systems, including high redundancy in multi-modal data, low energy efficiency, and poor cross-parameter adaptability of traditional fusion methods. A fuzzy clustering framework based on principal property selection is established to enable dynamic compression of multi-source heterogeneous data at cluster head nodes. The algorithm innovatively distinguishes between principal and secondary properties based on their contributions to clustering. Clustering is performed using principal properties, allowing data from nodes with similar values to be fused into unified categories, thereby enhancing the reliability of environmental information. Experimental results show that, compared to existing agricultural WSN data fusion algorithms, the proposed method reduces fusion error by an average of 5.6%, lowers the computational complexity of the original multi-view algorithm, and is more suitable for small-sized, low-capacity sensor nodes. Moreover, it has better adaptability to multiple agricultural parameters, reduces network energy consumption, and improves computational accuracy.