Multi-Task Allocation in Mobile Crowd Sensing With Mobility Prediction

Mobile crowd sensing (MCS) is a popular sensing paradigm that leverages the power of massive mobile workers to perform various location-based sensing tasks. To assign workers with suitable tasks, recent research works investigated mobility prediction methods based on probabilistic and statistical models to estimate the worker's moving behavior, based on which the allocation algorithm is designed to match workers with tasks such that workers do not need to deviate from their daily routes and tasks can be completed as many as possible. In this paper, we propose a new multi-task allocation method based on mobility prediction, which differs from the existing works by (1) making use of workers' historical trajectories more comprehensively by using the fuzzy logic system to obtain more accurate mobility prediction and (2) designing a global heuristic searching algorithm to optimize the overall task completion rate based on the mobility prediction result, which jointly considers workers' and tasks' spatiotemporal features. We evaluate the proposed prediction method and task allocation algorithm using two real-world datasets. The experimental results validate the effectiveness of the proposed methods compared against baselines.

Automated summary

Mobile crowd sensing (MCS) is a sensing paradigm that uses massive mobile workers to perform location-based sensing tasks. This paper proposes a new multi-task allocation method based on mobility prediction, which comprehensively uses workers' historical trajectories with the fuzzy logic system and design a global heuristic searching algorithm to optimize the task completion rate. Experimental results validate the effectiveness of the proposed methods.