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Prediction-Aware Adaptive Task Assignment for Spatial Crowdsourcing | IEEE Journals & Magazine | IEEE Xplore

Prediction-Aware Adaptive Task Assignment for Spatial Crowdsourcing


Abstract:

With the rapid development of wireless networks and smart devices, spatial crowdsourcing (SC) has become increasingly prevalent. The key issue in SC is efficiently assign...Show More

Abstract:

With the rapid development of wireless networks and smart devices, spatial crowdsourcing (SC) has become increasingly prevalent. The key issue in SC is efficiently assigning spatial tasks, such as parcel and food delivery, to mobile workers in order to maximize platform utility. Existing works mainly focus on task assignment based on real-time spatio-temporal constraints of workers and tasks, neglecting the influence of future spatio-temporal distributions of tasks on current assignments. In this paper, we propose a novel problem in SC called Prediction-aware Task Assignment (PTA), where the platform adaptively assigns spatial tasks to workers by considering their current and future spatio-temporal constraints to maximize overall platform revenue. To address this problem, we introduce a two-stage framework composed of task prediction and task assignment. In the task prediction stage, we develop a powerful Bilateral Spatial-Temporal Graph Convolutional Network (BSTGCNet) to predict the time and location where potential tasks may appear in the future. In the task assignment stage, we present a Deep Reinforcement Learning (DRL) approach to dynamically partition tasks into batches based on the current and future status of tasks, and conduct bipartite graph matching for spatial tasks and workers in a batch-wise manner. Finally, extensive experiments on real-world datasets validate the effectiveness and efficiency of our proposed solution.
Published in: IEEE Transactions on Mobile Computing ( Volume: 23, Issue: 12, December 2024)
Page(s): 13048 - 13061
Date of Publication: 04 July 2024

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