Enhanced Localization in Wireless Sensor Networks Using a Bat-Optimized Malicious Anchor Node Prediction Algorithm.

Area of Science:

• Computer Science
• Network Security
• Wireless Sensor Networks

Background:

• Node localization accuracy is critical for Wireless Sensor Networks (WSNs) used in security and environmental monitoring.
• Malicious nodes compromise WSN integrity, causing inaccurate positioning and reduced functionality.

Purpose of the Study:

• To propose a novel algorithm, BO-MAP, for security-aware localization in WSNs.
• To enhance both localization precision and network security by effectively identifying and isolating malicious nodes.

Main Methods:

• Developed the bat-optimized malicious anchor prediction (BO-MAP) algorithm.
• Integrated bat optimization with density-based clustering and probabilistic analysis for malicious node detection.
• Evaluated performance against six state-of-the-art localization algorithms.

Main Results:

• BO-MAP significantly outperformed existing methods in true positive rate, false positive rate, localization accuracy, energy efficiency, and computational efficiency.
• Achieved a 95% true positive rate and a 5% false positive rate, with an AUC of 0.98.
• Demonstrated consistent reliability under varying attack severities and network conditions.

Conclusions:

• BO-MAP offers a robust solution for secure and accurate localization in WSNs.
• The algorithm's effectiveness and reliability make it suitable for practical WSN deployments.
• BO-MAP represents a significant advancement in securing WSNs against malicious node attacks.