@article{Zhang2026, 
author = {Bing Zhang and Wenqi Shi},
title = {A Virtual Probe Deployment Method Based on User Behavioral Feature Analysis},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {86},
number = {2},
pages = {1-19},
keywords = {Virtual probe, behavior feature analysis, anomaly detection, scheduling strategy, geosocial data collection},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.067470},
doi = {10.32604/cmc.2025.067470},
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.}
}