Abstract: To address the efficiency and privacy issues caused by the geographical insensitivity of existing mobile crowdsensing task allocation mechanisms, a task allocation mechanism based on regional heat (HTPM) is designed. This mechanism realizes personalized task publishing through the analysis of historical data, improving the success rate of worker applications and reducing the number of location privacy exposures. Firstly, an adaptive grid partitioning algorithm based on the Geohash algorithm (GAGM) is used to divide the task area based on historical data analysis. Then, HTPM assigns task matching prefixes corresponding to the task locations based on the division results, and dynamically updates the task matching prefixes based on the recruitment end time to complete task publishing. Finally, the least probable cost winner selection mechanism (LPC-WSM) is adopted to select winners. Simulation experiments based on the Kaggle taxi route dataset show that the average number of applications per person using the HTPM mechanism is reduced by 30.3%, achieving the goal of ensuring location privacy protection strength and improving task allocation efficiency.

Key words: mobile crowdsensing, task assignment, location privacy protection, differential privacy, Geohash