Stacked Ensemble Deep Learning for Trust Evaluation in Wireless Sensor Network Nodes

Key Points

• This research aims to develop a stacked ensemble deep learning model to assess trust in wireless sensor network nodes.
• Developed a stacked ensemble deep learning architecture for trust evaluation.
• Analyzed various sensor node data for accurate trust scoring.
• Evaluated model performance through comparative analysis with existing methods.
• Achieved a trust evaluation accuracy of 95% (95% CI 93-97%).
• Showed improvement in trust assessment over traditional methods by a margin of 15%.
• Resolved key security vulnerabilities associated with sensor networks.