A study of trust mining algorithms for beacon nodes in large-scale network environments.

Area of Science:

• Network Engineering
• Wireless Communication Systems
• Cybersecurity

Background:

• Node positioning in large-scale networks suffers from significant deviations.
• Accurate node positioning relies on establishing trust in beacon nodes.
• Existing localization algorithms may not adequately address uncertainties in large network environments.

Purpose of the Study:

• To develop a trust degree mining algorithm for beacon nodes in large-scale network environments.
• To enhance the accuracy of node positioning by optimizing existing algorithms.
• To reflect the security status of nodes based on their trust in beacon nodes.

Main Methods:

• Calculating direct trust degree based on distance error and probability functions.
• Converting trust degree to influence and applying seepage theory to identify influential beacon nodes.
• Optimizing the Distance Vector Hop (DV-Hop) algorithm using node influence and Received Signal Strength Indicator (RSSI).
• Weighting average hop distance using beacon node influence for unknown nodes.

Main Results:

• The proposed algorithm accurately locates nodes in wide network environments, adapting to changes in beacon node numbers and communication distances.
• The mined trust degree effectively reflects the security status of nodes.
• The optimized DV-Hop algorithm demonstrates reduced positioning errors compared to conventional methods.

Conclusions:

• The trust degree mining algorithm is effective for precise node positioning in large-scale networks.
• Node influence derived from trust is a critical factor in improving localization accuracy.
• The algorithm provides a reliable method for assessing node security status within the network.