A New Data Fusion Algorithm for Wireless Sensor Networks Inspired by Hesitant Fuzzy Entropy.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless sensor networks (WSNs) face significant challenges due to limited node energy and resources.
• Energy consumption and bandwidth limitations are critical issues impacting WSN efficiency and lifespan.
• Data fusion offers a solution by reducing redundancy, minimizing transmission, and conserving energy.

Purpose of the Study:

• To propose a novel data fusion algorithm, Data Fusion Hesitant Fuzzy Entropy (DFHFE), for WSNs.
• To reduce data redundancy at the source and leverage redundant data for improved reliability.
• To enhance network performance in terms of energy efficiency, accuracy, and real-time operation.

Main Methods:

• Developed a new data fusion algorithm based on Hesitant Fuzzy Entropy (DFHFE).
• Implemented data fusion at the sink node using Hesitant Fuzzy Entropy to process data from sensor nodes.
• Sink nodes send aggregated local decisions to the base station for final judgment, reducing base station load.

Main Results:

• The DFHFE algorithm effectively reduces data redundancy and improves data reliability.
• Experimental results show significant improvements in network robustness, accuracy, and real-time performance.
• The proposed algorithm outperforms existing state-of-the-art methods in energy consumption and real-time efficiency.

Conclusions:

• The DFHFE algorithm offers an efficient solution for data fusion in WSNs.
• This approach enhances overall network performance and extends network lifespan.
• Hesitant Fuzzy Entropy is effectively utilized for intelligent data processing in resource-constrained environments.