Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor...

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) face significant energy consumption challenges due to limited sensor node resources.
• Existing clustering schemes struggle to efficiently manage energy and extend network operational lifespan.

Purpose of the Study:

• To propose a novel, dynamic, self-organizing Hesitant Fuzzy Entropy-based Opportunistic Clustering and data fusion Scheme (HFECS).
• To address energy consumption and network lifetime bottlenecks in WSNs through optimized clustering and data fusion.

Main Methods:

• HFECS utilizes an asynchronous working-sleeping cycle for opportunistic connections in heterogeneous clustering.
• The scheme incorporates a two-level hierarchy and characterizes energy heterogeneity across three levels.
• Multi-attribute decision modeling and entropy weight coefficient method are employed for cluster formation, head election, and data fusion.

Main Results:

• HFECS demonstrated superior performance compared to benchmark schemes in larger network sizes.
• Key performance improvements include extended half-life and stability periods, higher average residual energy, and increased network lifetime.
• Enhanced packet delivery ratio was also observed, indicating improved data transmission efficiency.

Conclusions:

• HFECS effectively reduces redundant data flow and improves energy consumption through hierarchical data fusion.
• The proposed scheme significantly extends WSN network lifetime and enhances overall performance, particularly for large-scale networks.