A Research Study on an Entropy-Weighted Multi-View Fusion Approach for Agricultural WSN Data Based on Fuzzy Clustering
Abstract
1. Introduction
2. Introduction to the Entropy Weight-Collaborative Partition Multi-View Fuzzy Clustering Algorithm
3. Multi-View Data Fusion Algorithm Based on Main Attributes
3.1. Attribute Reduction
- (1)
- Normalizes all attributes and calculates their variances;
- (2)
- Defines a variance threshold m0; attributes with variance above m0 are main attributes, others are secondary.
3.2. Parameters Setting
4. Implementation Steps
Algorithm 1: Multi-View Collaborative Fusion Algorithm for Agricultural WSN Data Based on Main Attributes |
Input: At the same time, a cluster head node receives sensor data from N nodes, including node address, air temperature, air humidity, precipitation, wind force, and solar radiation. Output: Data fusion result. #Step 1: Data Registration data_structs = [] FOR EACH data IN sensor_data_list: struct = { ‘address’: data.node_address, ‘‘temperature’: data.air_temp, ‘humidity’: data.air_humidity, ‘precipitation’: data.daily_precip, ‘wind’: data.wind_speed, ‘radiation’: data.solar_rad } data_structs.append(struct) # Step 2: Identify Main Attributes (Exemplar Values) primary_attrs = [‘temperature’, ‘precipitation’, ‘radiation’] secondary_attrs = [‘humidity’, ‘wind’] # Step 3: Multi-view Collaborative Clustering k = determine_optimal_clusters(data_structs, primary_attrs) # Determine the k-value with reference to the target range centers = init_random_centers(k, primary_attrs) # Random initialization of cluster prototypes membership_matrix = init_normalized_matrix(len(data_structs), k) # Normalization of the fuzzy membership matrix view_weights = init_normalized_weights(len(primary_attrs)) # Weight normalization across views # Iterative refinement DO: prev_centers = copy(centers) # Update cluster prototypes (Mathematical implementation details are provided in Section 2 and Section 3 of the paper) centers = update_cluster_centers(data_structs, membership_matrix, view_weights, primary_attrs) # update fuzzy membership matrix membership_matrix = update_membership_matrix(data_structs, centers, view_weights, primary_attrs) # update view weights view_weights = update_view_weights(data_structs, centers, membership_matrix, primary_attrs) WHILE convergence_check(centers, prev_centers) < THRESHOLD # Step 4: Generate Consensus Values cluster_results = assign_to_clusters(membership_matrix) # Assign data points to clusters via membership fused_data = [] FOR EACH cluster IN cluster_results: fused_entry = { ‘addresses’: [struct[‘address’] FOR struct IN cluster.members], ‘fused_values’: {} } # Compute fused main attributes (intra-class mean) FOR attr IN primary_attrs + secondary_attrs: values = [struct[attr] FOR struct IN cluster.members] fused_entry[‘fused_values’][attr] = mean(values) fused_data.append(fused_entry) # Step 5: Output Fused Results RETURN fused_data # Auxiliary function example FUNCTION determine_optimal_clusters(data, attrs): # Determine optimal k within the reference range using the elbow method or the silhouette coefficient k_range = range(2, 10) # Target reference range best_k = elbow_method(data, attrs, k_range) RETURN best_k FUNCTION update_view_weights(data, centers, membership, attrs): # Update view weights using Section 3 formulae weights = [] total_weight = 0.0 FOR attr_idx, attr IN enumerate(attrs): weight = calculate_view_weight(attr, data, centers, membership) weights.append(weight) total_weight = weight # Normalization procedure RETURN [w/total_weight FOR w IN weights] |
5. Simulation Results and Analysis
5.1. Analysis of Different Cluster Center Numbers
5.2. Data Fusion Accuracy
6. Conclusions
- (1)
- Determining main parameters: Considering the computational cost of WSNs and data characteristics, identifying key parameters based on the relationships among agricultural parameters is crucial and deserves further exploration.
- (2)
- Optimizing the objective function: When fusing multiple parameters, combining fuzzy or rough set theory to address uncertainties can enhance the efficiency of the fusion process.
- (3)
- Handling secondary attributes: Simply discarding secondary attributes is a crude approach. Future work can focus on optimizing clustering results using secondary attributes to achieve better data fusion outcomes.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
WSN | Wireless sensor network |
BSBL | Block-sparse Bayesian learning |
EW-CoP-MVFCM | Entropy Weight-Collaborative Partition Multi-View Fuzzy Clustering Algorithm |
FCM | Fuzzy C-means algorithm |
MA-MVFCM-DF | Main-attribute-based multi-view data fusion algorithm |
KMEANS-DF | K-means-based data fusion algorithm |
MSE | Mean squared error |
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Single-view data fusion methods | Based on k-means | Disadvantage: The output lacks reliability and comprehensiveness |
Based on block-sparse Bayesian learning | ||
Based on important extrema points extraction | ||
Based on the distributed fuzzy logic protocol | ||
Multi-view data fusion methods | Based on the improved genetic algorithm | Disadvantage: fail to consider different clustering characteristics of various attributes |
Based on the multilevel data fusion method | ||
Based on the trust degree | ||
Based on the random forest model |
The initial number of cluster centers | k = 5 | k = 10 | k = 15 |
Data compression rate | 4.24% | 8.47% | 12.7% |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Wang, X.; You, X. A Research Study on an Entropy-Weighted Multi-View Fusion Approach for Agricultural WSN Data Based on Fuzzy Clustering. Electronics 2025, 14, 2424. https://doi.org/10.3390/electronics14122424
Wang X, You X. A Research Study on an Entropy-Weighted Multi-View Fusion Approach for Agricultural WSN Data Based on Fuzzy Clustering. Electronics. 2025; 14(12):2424. https://doi.org/10.3390/electronics14122424
Chicago/Turabian StyleWang, Xun, and Xiaohu You. 2025. "A Research Study on an Entropy-Weighted Multi-View Fusion Approach for Agricultural WSN Data Based on Fuzzy Clustering" Electronics 14, no. 12: 2424. https://doi.org/10.3390/electronics14122424
APA StyleWang, X., & You, X. (2025). A Research Study on an Entropy-Weighted Multi-View Fusion Approach for Agricultural WSN Data Based on Fuzzy Clustering. Electronics, 14(12), 2424. https://doi.org/10.3390/electronics14122424