Fuzzy-based trust prediction model for routing in WSNs

Area of Science:

• Computer Science
• Network Security
• Wireless Sensor Networks

Background:

• Wireless sensor networks (WSNs) are vulnerable to attacks due to their cooperative nature.
• Resource constraints in WSNs necessitate lightweight security solutions.
• Existing security methods relying on historical data can be improved by predicting future node behavior.

Purpose of the Study:

• To propose a fuzzy-based trust prediction model for routing (FTPR) in WSNs.
• To enhance routing security and network performance with minimal memory and energy overhead.
• To predict the future behavior of network nodes for improved security.

Main Methods:

• Developed a fuzzy-based trust prediction model (FTPR) for WSN routing.
• FTPR predicts neighbor behavior using historical data, trust value fluctuations, and recommendation inconsistencies.
• Recommendations are gathered from a subset of neighbors to reduce control overhead.

Main Results:

• FTPR demonstrates a higher packet delivery ratio compared to traditional schemes.
• The proposed model leads to a longer network lifetime.
• FTPR achieves lower end-to-end delay and reduced memory and energy consumption.

Conclusions:

• The fuzzy-based trust prediction model (FTPR) effectively enhances security in WSNs.
• FTPR offers a lightweight and efficient solution for secure routing in resource-constrained environments.
• The model improves overall network performance metrics including reliability and longevity.