A Task Assignment Method Based on User-Union Clustering and Individual Preferences in Mobile Crowdsensing

Abstract

Mobile crowdsensing (MCS) offers a novel paradigm for large-scale sensing with the proliferation of smartphones. Task assignment is a critical problem in mobile crowdsensing (MCS), where service providers attempt to recruit a group of brilliant users to complete the sensing task at a limited cost. However, selecting an appropriate set of users with high quality and low cost is challenging. Existing works of task assignment ignore the data redundancy of large-scale users and the individual preference of service providers, resulting in a significant workload on the sensing platform and inaccurate assignment results. To tackle this issue, we propose a task assignment method based on user-union clustering and individual preferences, which considers the influence of clustering data quality and preference-based sensing cost. Firstly, we design a user-union clustering algorithm (UCA) by defining user similarity and setting user scale, which aims to balance user distribution, reduce data redundancy, and improve the accuracy of high-quality user aggregation. Then, we consider individual preferences of service providers and construct a preference-based task assignment algorithm (PTA) to achieve the diversified sensing cost control needs. To evaluate the performance of the proposed solutions, extensive simulations are conducted. The results demonstrate that our proposed solutions outperform the baseline algorithm, which realizes the individual preference-based task assignment under the premise of ensuring high-quality data.