SmartRoute Optimization Network: Anomaly‐Aware Energy‐Efficient Routing for Real‐Time Landslide Monitoring in WSNs

ABSTRACT Wireless sensor networks (WSNs) are crucial for applications such as environmental monitoring, smart cities, and disaster detection, but they face significant challenges from malicious nodes, outliers, and routing inefficiencies. These issues hinder network performance and reliability, while existing solutions often fall short due to high computational demands and limited scalability. This paper introduces the SmartRoute optimization network: anomaly‐aware energy‐efficient routing for real‐time landslide monitoring in WSNs (SRON), an innovative framework designed to enhance reliability and energy efficiency in WSNs, especially for landslide monitoring. The SRON framework integrates three core methodologies: dynamic position bias for malicious node detection, grouped query attention for precise outlier management, and SmartPPO for optimized routing. The dynamic position vias module identifies and isolates malicious nodes to prevent false data from disrupting network accuracy. The grouped query attention mechanism improves outlier detection by clustering sensor readings, thereby filtering anomalies and reducing false alarms. Furthermore, the integration of PPO with the attention sink mechanism enhances routing efficiency, enabling energy‐aware, reliable data transmission while focusing on critical data points. The developed SRON framework is rigorously tested in Python using more complex simulation parameters, achieving a malicious node detection accuracy of 96%, an outlier detection accuracy of 95%, and a routing efficiency improvement of 94%. These results underscore SRON's capability to address modern WSN challenges, offering a comprehensive, adaptive, and resource‐efficient solution for real‐time landslide detection and other critical monitoring applications in complex environments.