TY  - EJOU
AU  - Zhang, Bing 
AU  - Shi, Wenqi 

TI  - A Virtual Probe Deployment Method Based on User Behavioral Feature Analysis
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 86
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Virtual probe; behavior feature analysis; anomaly detection; scheduling strategy; geosocial data collection

DO  - 10.32604/cmc.2025.067470