@Article{cmc.2025.067470,
AUTHOR = {Bing Zhang, Wenqi Shi},
TITLE = {A Virtual Probe Deployment Method Based on User Behavioral Feature Analysis},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {86},
YEAR = {2026},
NUMBER = {2},
PAGES = {1--19},
URL = {http://www.techscience.com/cmc/v86n2/64719},
ISSN = {1546-2226},
ABSTRACT = {To address the challenge of low survival rates and limited data collection efficiency in current virtual probe deployments, which results from anomaly detection mechanisms in location-based service (LBS) applications, this paper proposes a novel virtual probe deployment method based on user behavioral feature analysis. The core idea is to circumvent LBS anomaly detection by mimicking real-user behavior patterns. First, we design an automated data extraction algorithm that recognizes graphical user interface (GUI) elements to collect spatio-temporal behavior data. Then, by analyzing the automatically collected user data, we identify normal users’ spatio-temporal patterns and extract their features such as high-activity time windows and spatial clustering characteristics. Subsequently, an anti-detection scheduling strategy is developed, integrating spatial clustering optimization, load-balanced allocation, and time window control to generate probe scheduling schemes. Additionally, a self-correction mechanism based on an exponential backoff strategy is implemented to rectify anomalous behaviors and maintain system stability. Experiments in real-world environments demonstrate that the proposed method significantly outperforms baseline methods in terms of both probe ban rate and task completion rate, while maintaining high time efficiency. This study provides a more reliable and clandestine solution for geosocial data collection and lays the foundation for building more robust virtual probe systems.},
DOI = {10.32604/cmc.2025.067470}
}